# Best DataOps Platforms *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)* DataOps platforms act as command centers for DataOps. These solutions orchestrate people, processes, and technology to deliver a trusted data pipeline to their users. DataOps platforms assemble several types of data management software into an individual, integrated environment. Data flows in a simple manner from various data sources. These platforms are used to leverage any analytical tool—from data collection to data reporting via a single integrated platform. The platform unifies all the development and operations in data workflows. DataOps platforms are used to provide the flexibility to support a vast number of existing and new tools, as they are added. Organizations use the platform to control the entire workflow and related processes and ensure data-driven decisions are being made. Cycle times are reduced significantly and users are empowered with a single point of access to manage the data. Companies can leverage DataOps platforms to derive on-demand insights for successful business decisions. DataOps platforms are primarily used by analytics and data teams within an organization; they are cross-functional and can be used across multiple verticals such as healthcare, finance, and others. IT operation teams can reduce storage infrastructure and increase staff productivity using a DataOps platform. Development and testing teams can decrease development cycles, app development times and reduce errors significantly by using this consolidated platform. To qualify for inclusion in the DataOps category, a product must: - Enable collaboration between data providers and data consumers to ensure data fluidity - Combine different data management practices within a single platform, acting as an end-to-end solution - Completely automate end-to-end data workflows across the data integration lifecycle - Provide a dashboard and visualization tools to support data analysis and collaboration between various stakeholders - Support deployment on any cloud environment ## Top DataOps Platforms at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,284 reviews) | Unified lakehouse DataOps with cross-team collaboration | "[Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines](https://www.g2.com/survey_responses/databricks-review-12946286)" | | 2 | [Flip](https://www.g2.com/products/kanerika-flip/reviews) | 5.0/5.0 (13 reviews) | AI-assisted pipeline validation and DataOps automation | "[FLIP Made Data Validation and Testing Far Less Painful](https://www.g2.com/survey_responses/flip-review-12253639)" | | 3 | [5X](https://www.g2.com/products/5x/reviews) | 4.9/5.0 (81 reviews) | End-to-end DataOps with unified stack consolidation | "[A reliable and scalable data partner](https://www.g2.com/survey_responses/5x-review-11889175)" | | 4 | [dbt](https://www.g2.com/products/dbt/reviews) | 4.7/5.0 (207 reviews) | SQL-based ELT transformation with version-controlled lineage | "[Simple SQL-Driven Materializations with Powerful Lineage](https://www.g2.com/survey_responses/dbt-review-12985641)" | | 5 | [ServiceNow Workflow Data Fabric](https://www.g2.com/products/servicenow-workflow-data-fabric/reviews) | 4.3/5.0 (138 reviews) | Zero-copy DataOps inside ServiceNow workflows | "[Zero-Copy, Real-Time Intelligence with ServiceNow Workflow Data Fabric](https://www.g2.com/survey_responses/servicenow-workflow-data-fabric-review-12543653)" | | 6 | [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) | 4.3/5.0 (520 reviews) | ML-driven pipeline anomaly detection and lineage | "[Smart Data Observability and Lineage That Saves Hours of Debugging](https://www.g2.com/survey_responses/monte-carlo-review-12935974)" | | 7 | [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews) | 4.5/5.0 (135 reviews) | Managed Airflow orchestration for infrastructure-free pipeline ops | "[Excellent developer and customer experience](https://www.g2.com/survey_responses/astro-by-astronomer-review-8428848)" | | 8 | [kestra](https://www.g2.com/products/kestra-technologies-kestra/reviews) | 4.6/5.0 (24 reviews) | Declarative AI pipeline orchestration with native observability | "[Exceptional Orchestration for Resilient, Self-Healing Data Pipelines](https://www.g2.com/survey_responses/kestra-review-12935633)" | | 9 | [Y42](https://www.g2.com/products/y42-y42/reviews) | 4.9/5.0 (21 reviews) | dbt-native end-to-end DataOps pipelines | "[dbt on steroids](https://www.g2.com/survey_responses/y42-review-8967021)" | | 10 | [Peliqan](https://www.g2.com/products/peliqan/reviews) | 4.8/5.0 (78 reviews) | Multi-source ELT pipelines with unified data activation | "[Custom Python Tools Made Our Agent Fit Our Workflow](https://www.g2.com/survey_responses/peliqan-review-12991439)" | --- ## What Are the Most Common Questions About DataOps Platforms? *AI-generated · Last updated: May 26, 2026* ### What DataOps Platforms that remove friction between teams and simplify collaborative workflows in enterprise environments? Based on G2 reviews, buyers in this category often look for platforms that reduce handoffs by bringing engineering, analytics, and governance work into one environment. Reviewers mention shared notebooks, unified workspaces, built-in lineage, and easier coordination across pipelines, models, and reporting as the biggest workflow improvements. According to verified users, Databricks is repeatedly described as helping teams collaborate in shared notebooks, centralize data engineering and analytics, and reduce the need to switch between disconnected tools. G2 reviewers also mention products like ILUM and ServiceNow Workflow Data Fabric for improving visibility and connecting fragmented systems, though review themes vary by use case and team maturity. ### What most trusted DataOps Platforms by Senior Data Engineers based on user reviews? Based on G2 reviews, trust in this category is usually tied to reliability, scalability, and how consistently a platform supports production workflows. According to verified users, Databricks appears most often in recent reviews and is repeatedly described as a dependable platform for large-scale processing, ETL, analytics, and machine learning in one place. G2 reviewers mention strong collaboration, broad cloud integrations, and support for unified lakehouse workflows, while also noting a learning curve and the need for cost discipline. In this review set, Databricks stands out as the most frequently mentioned option by practitioners working in enterprise-scale data environments, which makes it the clearest trusted choice from recent reviewer volume. ### Which DataOps Platforms reduce processing times from days to hours for competitive quotes? Based on G2 reviews, several products are described as speeding up data preparation, validation, and reporting by automating manual work and reducing pipeline complexity. According to verified users, Databricks is often associated with faster ETL, large-scale processing, and simpler end-to-end workflows, while QuerySurge reviewers highlight quicker regression testing and automated source-to-target validation. G2 reviewers also mention 5X for consolidating ingestion, transformation, and analytics into one platform that helps teams move faster. The best fit depends on whether your bottleneck is heavy data processing, testing and validation, or dashboard and pipeline delivery, but these products are the ones recent reviewers most often connect to faster turnaround times. **Here are some of the top-rated products on G2:** - [Databricks](https://www.g2.com/products/databricks/reviews/databricks-review-12866693) – used for large-scale ETL, analytics, and unified workflows that reviewers say speed up processing and collaboration - [QuerySurge](https://www.g2.com/products/querysurge/reviews/querysurge-review-12698761) – focused on automated ETL validation and faster regression testing across source and target systems - [5X](https://www.g2.com/products/5x/reviews/5x-review-11905989) – brings ingestion, modeling, and dashboards together to reduce manual pipeline and reporting work ### What DataOps Platforms that Data Engineers adopt for fast ETL pipelines and keep using daily? Based on G2 reviews, daily-use adoption tends to follow products that simplify recurring ETL work, reduce maintenance overhead, and keep monitoring visible in one place. According to verified users, Databricks is frequently described as a daily workspace for ETL, streaming, notebook-based collaboration, and large-scale data processing. G2 reviewers also mention dbt for version-controlled SQL transformations and repeatable modeling workflows, and Keboola for easy flow setup and broad connector coverage. Across these reviews, the common pattern is consistent use by teams that need reliable automation, reusable logic, and less manual intervention. The strongest recent signals point to Databricks, dbt, and Keboola as platforms reviewers keep embedded in day-to-day work. ### What highest rated DataOps Platforms for creating unified workspace supporting Python, SQL, and Scala? Based on G2 reviews, buyers looking for a unified workspace often prioritize notebook collaboration, support for multiple languages, and fewer tool handoffs between engineering and analytics teams. According to verified users, Databricks is the clearest match in recent reviews because users repeatedly mention working across Python, SQL, and Scala in shared notebooks and a single workspace. G2 reviewers describe it as helpful for building pipelines, running analytics, and supporting machine learning without splitting work across multiple platforms. Reviews also highlight unified catalog and governance capabilities, though some users note the interface can feel complex as usage expands. In this dataset, Databricks has the strongest and most consistent support for this exact workflow style. ### What DataOps Platforms Senior Data Engineers rely on for handling massive datasets effortlessly? Based on G2 reviews, platforms in this category are valued for distributed processing, scalable pipeline execution, and the ability to keep performance manageable as data volumes grow. According to verified users, Databricks is most often praised for handling very large datasets, batch and streaming workloads, and complex ETL within a unified environment. G2 reviewers also mention products like 5X and ILUM for integrated data operations and scalable compute patterns, but those signals are far lighter in recent review count. The strongest recurring reviewer language around massive datasets centers on Spark-based processing, autoscaling, and simplified infrastructure management, which makes Databricks the most grounded answer from the current review set. ### Which DataOps Platforms offer built-in data quality checks and lineage tracking at scale? Based on G2 reviews, buyers should look for products that combine automated monitoring with lineage views, root-cause investigation, and broad stack integrations. According to verified users, Monte Carlo is one of the clearest fits in recent reviews because users repeatedly mention automated anomaly detection, lineage visualization, freshness and schema monitoring, and centralized alerting. G2 reviewers also point to Sifflet for data observability, anomaly detection, and end-to-end lineage, while Secoda is mentioned for metadata and lineage centralization. Review patterns suggest Monte Carlo is especially strong when teams need proactive issue detection and impact tracing across large data environments rather than only basic cataloging or documentation. ### Which DataOps Platforms have transparent pricing models without credit system cost leakage? Based on G2 reviews, pricing clarity is a mixed theme in this category, and many reviewers still call out complexity or the need for closer monitoring. According to verified users, dbt receives some of the clearer positive pricing feedback in recent reviews, with users describing pricing as predictable. G2 reviewers also mention 5X as cost-efficient and a good value when consolidating multiple tools, while Seemore Data is described as helping reduce warehouse costs through optimization features. At the same time, many Databricks, Monte Carlo, and ServiceNow Workflow Data Fabric reviewers mention cost management challenges. Buyers focused on pricing transparency should validate packaging, scaling rules, and monitoring controls early in the evaluation process. ### What best DataOps Platforms for Data Engineers managing ETL pipelines and orchestration in enterprise environments? Based on G2 reviews, enterprise ETL and orchestration buyers usually want strong workflow automation, dependable monitoring, and support for complex pipelines across teams. According to verified users, Databricks is frequently chosen for unified ETL, analytics, and large-scale processing, while Astro by Astronomer is often mentioned for managed Airflow and scheduling reliability. G2 reviewers also highlight Stonebranch for centralized automation and cross-platform workflow visibility. These products address slightly different needs: Databricks for integrated data engineering work, Astro for orchestrating Airflow-based pipelines, and Stonebranch for broader enterprise job automation. The right fit depends on whether your team prioritizes notebook-centric engineering, managed orchestration, or enterprise-wide workload control. **Here are some of the top-rated products on G2:** - [Databricks](https://www.g2.com/products/databricks/reviews/databricks-review-12810747) – used to unify ETL, analytics, and pipeline development in one collaborative platform - [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews/astro-by-astronomer-review-11770945) – supports managed Airflow orchestration, scheduling, and monitoring for production workflows - [Stonebranch](https://www.g2.com/products/stonebranch/reviews/stonebranch-review-12703027) – helps automate batch jobs, client feed onboarding, and enterprise workflow operations across environments ### What should data teams evaluate when choosing DataOps Platforms for cloud cost control? Based on G2 reviews, cloud cost control comes down to how well a platform exposes usage, supports rightsizing, and reduces unnecessary compute or duplicated tooling. According to verified users, teams should evaluate whether the platform makes it easy to monitor cluster usage, tune workloads, and manage scaling policies before spend drifts upward. G2 reviewers mention Databricks cost visibility and cluster management as a frequent concern, while ILUM users highlight compute savings from flexible workload placement and configuration guidance. Seemore Data reviewers also point to optimization and auto-shutdown capabilities that help reduce warehouse waste. In practice, buyers should compare cost observability, automation for idle resource control, and how much manual oversight is still required. ## How Many DataOps Platforms Products Does G2 Track? **Total Products under this Category:** 105 ### Category Stats (Jun 2026) - **Average Rating**: 4.59/5 (↓0.01 vs May 2026) The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: DataBahn (+5.56%) - Among all products in this category, DataBahn recorded the largest rating increase compared to last month *Last updated: June 18, 2026* ## How Does G2 Rank DataOps Platforms Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 5,200+ Authentic Reviews - 105+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which DataOps Platforms Is Best for Your Use Case? - **Leader:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest Performer:** [5X](https://www.g2.com/products/5x/reviews) - **Easiest to Use:** [5X](https://www.g2.com/products/5x/reviews) - **Top Trending:** [Hightouch](https://www.g2.com/products/hightouch/reviews) - **Best Free Software:** [Databricks](https://www.g2.com/products/databricks/reviews) --- **Sponsored** ### QuerySurge QuerySurge is an enterprise-grade data quality platform that leverages AI to continuously automate data validation across your entire ecosystem ‐ from data warehouses and big data lakes to BI reports and enterprise applications. With AI-powered test creation, scalable architecture, and the leading DevOps for Data CI/CD integration, QuerySurge ensures data integrity at every stage of the pipeline. Automated Data Validation Use Cases: QuerySurge provides a smart, AI-driven, data validation & ETL testing solution for your automated testing needs. - Data Warehouse / ETL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - Business Intelligence (BI) Report Testing - Big Data Testing - Enterprise Application Data Testing What QuerySurge Provides: - Automation of your manual data validation and testing process - Ease-of-use, low-code/no-code features - Generative AI capabilities for test creation - Testing across 200+ data platforms - Integration into your CI/CD DataOps pipeline - Acceleration of your data analysis - Ensurance of regulatory compliance Key Features: - Data Connection Wizard provides an easy way to link to your data stores - Visual Query Wizard builds table-to-table and column-to-column tests without writing SQL - Generative AI module automatically creates transformation tests in bulk - DevOps for Data provides a RESTful API with 110+ calls and Swagger documentation and integrates into CI/CD pipelines - Create Custom Tests and modularize functions with snippets, set thresholds, stage data, check data types & duplicate rows, full text search, and asset tagging - Schedule tests to run immediately, at a predetermined date & time, or after any event from a build/release, CI/CD, DevOps, or test management solution - Multi-project support in a single instance, new Global Admin user, assign users and agents, import and export projects, and user activity log reports - Webhooks provide real-time integrations with DevOps, CI/CD, test management, and alerting tools - Ready-for-Analytics provides seamless integration with QuerySurge and your BI tool or open-source Metabase to create custom reports and dashboards and gain deeper, real-time insights into your data validation and ETL testing workflows - Data Analytics Dashboards and Data Intelligence Reports track, analyze, and communicate data quality [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=2686&secure%5Bdisplayable_resource_id%5D=2686&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=2686&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=54942&secure%5Bresource_id%5D=2686&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdataops-platforms%3Fpage%3D8&secure%5Btoken%5D=f97cb67e6e0312a3614ebb6a21c5cca1592c94fb64fb8b56fa449e4f038d7e25&secure%5Burl%5D=https%3A%2F%2Fwww.querysurge.com%2Fget-started%2Fprivate-demo%3Futm_source%3DG2%26utm_medium%3Dcpc%26utm_campaign%3DG2-reviews&secure%5Burl_type%5D=book_demo) --- ## What Are the Top-Rated DataOps Platforms Products in 2026? ### 1. [Databricks](https://www.g2.com/products/databricks/reviews) Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. **Average Rating:** 4.6/5.0 **Total Reviews:** 1,284 **How Do G2 Users Rate Databricks?** - **Data Observability:** 8.8/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.8/10 (Category avg: 8.6/10) - **Ease of Use:** 8.8/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Databricks?** - **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc) - **Company Website:** https://databricks.com - **Year Founded:** 2013 - **HQ Location:** San Francisco, CA - **Twitter:** @databricks (92,269 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (15,627 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 48% Enterprise, 38% Mid-Market #### What Are Databricks's Pros and Cons? **Pros:** - Features (288 reviews) - Ease of Use (278 reviews) - Integrations (189 reviews) - Collaboration (150 reviews) - Data Management (150 reviews) **Cons:** - Learning Curve (112 reviews) - Expensive (97 reviews) - Steep Learning Curve (96 reviews) - Missing Features (69 reviews) - Complexity (64 reviews) ### What Do G2 Reviewers Say About Databricks? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **seamless integration** of Databricks with AWS and its powerful features for data management and security. - Users find Databricks to offer **exceptional ease of use** , making data integration and management seamless and efficient. - Users praise the **seamless integration** of Databricks with AWS and Azure, enhancing collaboration and efficiency in data management. - Users appreciate the **excellent collaboration** in Databricks, facilitating real-time teamwork for data engineers and analysts. - Users value the **effective data management features** of Databricks, enhancing usability and decision-making with integrated tools. **Cons:** - Users face a **steep learning curve** with Databricks, complicating initial adoption and resource management. - Users find the **costs to be high** for Databricks, especially when dealing with large data sets. - Users face a **steep learning curve** with Databricks, making initial adoption challenging and requiring specialized support. - Users are disappointed by the **missing features** in Databricks, limiting customization and complicating development processes. - Users find the **complexity** of Databricks challenging due to steep learning curves and integration limitations. #### What Are Recent G2 Reviews of Databricks? **"[Great Spark Scaling, But Slow Cluster Boot Times](https://www.g2.com/survey_responses/databricks-review-12905667)"** **Rating:** 5.0/5.0 stars *— Raj P.* [Read full review](https://www.g2.com/survey_responses/databricks-review-12905667) --- **"[Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines](https://www.g2.com/survey_responses/databricks-review-12946286)"** **Rating:** 4.5/5.0 stars *— Konjengbam M.* [Read full review](https://www.g2.com/survey_responses/databricks-review-12946286) --- #### What Are G2 Users Discussing About Databricks? - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes ### 2. [Flip](https://www.g2.com/products/kanerika-flip/reviews) FLIP by Kanerika is an AI-powered, low-code/no-code platform that automates enterprise workflows, streamlines data operations, and accelerates migrations — all without the need for technical expertise. It empowers teams to modernize faster, reduce manual effort, and focus on business outcomes. Key Capabilities: Automated Data Reconciliation Low-Code/No-Code DataOps Migration Accelerators AI Workforce Accounts Payable Automation FLIP helps organizations simplify data management, enhance accuracy, and accelerate digital transformation across industries. **Average Rating:** 5.0/5.0 **Total Reviews:** 13 **How Do G2 Users Rate Flip?** - **Data Observability:** 9.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 10.0/10 (Category avg: 8.6/10) - **Ease of Use:** 10.0/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 3.3/10 (Category avg: 10/10) **Who Is the Company Behind Flip?** - **Seller:** [Kanerika](https://www.g2.com/sellers/kanerika) - **Year Founded:** 2015 - **HQ Location:** Austin, Texas, United States - **LinkedIn® Page:** http://www.linkedin.com/company/kanerika (308 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Co-Founder - **Top Industries:** Computer Software - **Company Size:** 92% Small-Business, 23% Mid-Market #### What Are Flip's Pros and Cons? **Pros:** - Features (10 reviews) - Ease of Use (9 reviews) - Fast Processing (7 reviews) - Data Syncing (6 reviews) - Customization (5 reviews) **Cons:** - Complex Setup (4 reviews) - Expensive (4 reviews) - Steep Learning Curve (4 reviews) - Missing Features (3 reviews) - Integration Issues (2 reviews) ### What Do G2 Reviewers Say About Flip? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **real-time tracking integration** that enhances visibility and customer service efficiency in their operations. - Users appreciate the **ease of use** of Flip, allowing even non-technical members to create workflows quickly. - Users value the **fast processing** of Flip, enhancing efficiency in handling financial documents and data extraction. - Users value the **data syncing capabilities** of FLIP, dramatically improving efficiency and accuracy in operations. - Users appreciate the **customization options** of Flip, enabling tailored solutions for diverse business needs and workflows. **Cons:** - Users report a **complex setup** process for Flip, often taking longer than expected due to custom requirements. - Users find FLIP to be **expensive** due to longer-than-expected implementation timelines and basic mobile app functionality. - Users find the **steep learning curve** challenging, requiring substantial time and training to fully utilize Flip's features. - Users note the **missing features** in Flip, particularly lacking advanced templates and modern reporting visuals. - Users experience **integration issues** with smaller retailers and varying data quality, requiring manual workarounds and extended implementation times. #### What Are Recent G2 Reviews of Flip? **"[FLIP Made Data Validation and Testing Far Less Painful](https://www.g2.com/survey_responses/flip-review-12253639)"** **Rating:** 5.0/5.0 stars *— Loic Le P.* [Read full review](https://www.g2.com/survey_responses/flip-review-12253639) --- **"[FLIP Delivers Fast, Automated Retail Reporting for New SKU Performance](https://www.g2.com/survey_responses/flip-review-12253012)"** **Rating:** 5.0/5.0 stars *— Milko L.* [Read full review](https://www.g2.com/survey_responses/flip-review-12253012) --- ### 3. [5X](https://www.g2.com/products/5x/reviews) 5X is an end-to-end data and AI platform. The platform organizes your data regardless of source or format. Whether you have a dedicated data team or not, our platform
transforms fragmented data into actionable insights and apps. The customer feedback we get most often is, "This is self-explanatory," and "It's super easy to use." And that is exactly what our goal was—to create a powerful, all-in-one platform that's incredibly easy to use.  The modern data stack has evolved. It's no longer about stitching together vendors. The next-generation modern data stack is an all-in-one platform that offers speed, simplicity, and decreased cost of ownership. That's exactly what we have created at 5X. Companies use 5X for multiple reasons: 1) Speed & productivity. All-in-one data platforms are incredibly efficient. We've seen companies build use cases on day 1.  Contact us to see if you qualify for a free 48 hour jumpstart! 🚀 2) Decrease your total cost of ownership by 30% compared to building your own platform. This doesn't account the people hours needed to support a platform build 🤯 3) Use our full stack data consultancy for support on data engineering & analytics 👨‍💻 5X was founded in 2020 with presence in the USA, Singapore, UK and India. Our global team is 70+ people strong and rapidly growing. We’ve recently raised our seed round from Flybridge Capital and backed by top founders from companies like Datadog, Preset, Astronomer, Mode, Rudderstack and other prominent angel investors. For more information, visit 5X.co We don't just talk about speed and simplicity; we back it up with proof. Speak to us about our 48-hour jumpstart where we can build an end-to-end use case for you in 48 hours for free. **Average Rating:** 4.9/5.0 **Total Reviews:** 81 **How Do G2 Users Rate 5X?** - **Data Observability:** 9.1/10 (Category avg: 9.0/10) - **Testing capabilities:** 9.0/10 (Category avg: 8.6/10) - **Ease of Use:** 9.5/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 6.1/10 (Category avg: 10/10) **Who Is the Company Behind 5X?** - **Seller:** [5X](https://www.g2.com/sellers/5x) - **Year Founded:** 2020 - **HQ Location:** San Francisco - **Twitter:** @DataWith5x (49 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/datawith5x/ (111 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Financial Services - **Company Size:** 56% Mid-Market, 40% Small-Business #### What Are 5X's Pros and Cons? **Pros:** - Ease of Use (28 reviews) - Customer Support (18 reviews) - Features (14 reviews) - Integrations (13 reviews) - Data Integration (10 reviews) **Cons:** - Steep Learning Curve (5 reviews) - Complex Setup (4 reviews) - Feature Limitations (4 reviews) - Learning Curve (4 reviews) - Difficult Setup (3 reviews) ### What Do G2 Reviewers Say About 5X? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of 5X, finding its integration and UI intuitive and highly effective for their needs. - Users commend the **exceptional customer support** from 5X, appreciating their responsiveness and dedication to user experience. - Users highlight the **seamless data integration** and **interactive UI** of 5X, enhancing overall functionality and usability. - Users value the **seamless integration capabilities** of 5X, enabling easy connection with existing tools and workflows. - Users highlight the **seamless data integration** provided by 5X, enhancing collaboration and operational efficiency for teams. **Cons:** - Users find the **steep learning curve** challenging initially, requiring training sessions and support for effective use. - Users find the **complex setup** of 5X delays deployment and requires significant time to learn and implement. - Users note the **feature limitations** of 5X, indicating a need for more developed and mature functionalities. - Users face a **significant learning curve** with 5X, especially for advanced features and complex workflows. - Users find the **difficult setup** challenging, as it requires significant time and learning for effective deployment. #### What Are Recent G2 Reviews of 5X? **"[Exceptional Support and User-Friendly Platform Driving Our Data Transformation](https://www.g2.com/survey_responses/5x-review-11903408)"** **Rating:** 4.0/5.0 stars *— Shuming F.* [Read full review](https://www.g2.com/survey_responses/5x-review-11903408) --- **"[A reliable and scalable data partner](https://www.g2.com/survey_responses/5x-review-11889175)"** **Rating:** 5.0/5.0 stars *— Varinderjit K.* [Read full review](https://www.g2.com/survey_responses/5x-review-11889175) --- ### 4. [dbt](https://www.g2.com/products/dbt/reviews) dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines. **Average Rating:** 4.7/5.0 **Total Reviews:** 207 **How Do G2 Users Rate dbt?** - **Data Observability:** 8.7/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.9/10 (Category avg: 8.6/10) - **Ease of Use:** 9.0/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind dbt?** - **Seller:** [dbt Labs](https://www.g2.com/sellers/dbt-labs) - **Year Founded:** 2016 - **HQ Location:** Philadelphia, US - **Twitter:** @getdbt (14,792 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dbtlabs/ (874 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Analytics Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 56% Mid-Market, 27% Small-Business #### What Are dbt's Pros and Cons? **Pros:** - Ease of Use (34 reviews) - Features (21 reviews) - Automation (17 reviews) - Transformation (16 reviews) - Data Quality (14 reviews) **Cons:** - Limited Functionality (13 reviews) - Dependency Issues (12 reviews) - Steep Learning Curve (10 reviews) - Error Handling (9 reviews) - Error Reporting (9 reviews) ### What Do G2 Reviewers Say About dbt? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of dbt, facilitating intuitive data transformations and collaboration among teams. - Users value dbt for its **robust structure and documentation** , enhancing SQL transformations and team collaboration effectively. - Users value the **automation** capabilities of dbt, enhancing collaboration and making SQL transformations efficient and maintainable. - Users find dbt to be a **powerful tool for data transformation** , simplifying processes and enhancing transparency in analytics workflows. - Users value the **high data quality** provided by dbt, ensuring integrity and transparency in analytics workflows. **Cons:** - Users find **limited functionality** in dbt, causing delays and complications, especially during debugging and project expansion. - Users face significant **dependency issues** with dbt, making troubleshooting model errors and debugging time-consuming and frustrating. - Users find the **steep learning curve** of dbt challenging, particularly with mastering Jinja, Git, and complex dependencies. - Users face difficulties with **error handling** , struggling with unclear messages and insufficient documentation for troubleshooting. - Users often find **error reporting unclear** , complicating troubleshooting and making it difficult to identify root causes. #### What Are Recent G2 Reviews of dbt? **"[Simple SQL-Driven Materializations with Powerful Lineage](https://www.g2.com/survey_responses/dbt-review-12985641)"** **Rating:** 5.0/5.0 stars *— Anish G.* [Read full review](https://www.g2.com/survey_responses/dbt-review-12985641) --- **"[dbt Streamlines Data Pipelines with Powerful Incremental and SCD2 Features](https://www.g2.com/survey_responses/dbt-review-12712114)"** **Rating:** 5.0/5.0 stars *— Hithesh P.* [Read full review](https://www.g2.com/survey_responses/dbt-review-12712114) --- #### What Are G2 Users Discussing About dbt? - [What is DBT data Modelling?](https://www.g2.com/discussions/what-is-dbt-data-modelling) - 2 comments - [What is DBT technology?](https://www.g2.com/discussions/what-is-dbt-technology) - 2 comments - [What is DBT database tool?](https://www.g2.com/discussions/what-is-dbt-database-tool) - 1 comment - [What is DBT tool used for?](https://www.g2.com/discussions/what-is-dbt-tool-used-for) - 2 comments ### 5. [ServiceNow Workflow Data Fabric](https://www.g2.com/products/servicenow-workflow-data-fabric/reviews) Workflow Data Fabric is the AI‑ready data foundation of the ServiceNow AI Platform. It connects to any data—structured, unstructured, and streaming—contextualizes it with business meaning and governance, and controls it with lineage and policies so employees and AI agents can confidently act on real‑time information to prevent disruptions, resolve requests faster, and optimize operations—all on one platform. How Workflow Data Fabric turns data into instant action Connect Unify data from systems like Salesforce, SAP, Workday, data lakes, and event streams in real time without duplication or fragile point‑to‑point integrations. With Zero Copy Connectors, Stream Connect, External Content Connectors, and Integration Hub, WDF simplifies architecture and cuts integration cost and time. Contextualize Give data business meaning and make it trustworthy with an active Data Catalog, embedded governance, and lineage. Use Knowledge Graph to map relationships (e.g., customers, assets, orders) so AI agents and workflows understand context and make accurate decisions in the flow of work. Control Apply policies, permissions, and compliance guards across connected sources so the right people and AI agents access the right data, at the right time, with full auditability and traceability—no more shadow copies or opaque pipelines. **Average Rating:** 4.3/5.0 **Total Reviews:** 138 **How Do G2 Users Rate ServiceNow Workflow Data Fabric?** - **Data Observability:** 8.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.1/10 (Category avg: 8.6/10) - **Ease of Use:** 8.0/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind ServiceNow Workflow Data Fabric?** - **Seller:** [ServiceNow](https://www.g2.com/sellers/servicenow) - **Company Website:** https://www.servicenow.com/ - **Year Founded:** 2004 - **HQ Location:** Santa Clara, CA - **Twitter:** @servicenow (55,548 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/29352/ (35,081 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 45% Enterprise, 27% Mid-Market #### What Are ServiceNow Workflow Data Fabric's Pros and Cons? **Pros:** - Ease of Use (37 reviews) - Integrations (34 reviews) - Automation (30 reviews) - Efficiency Improvement (26 reviews) - Data Management (25 reviews) **Cons:** - Complex Setup (23 reviews) - Difficult Setup (17 reviews) - Expensive (15 reviews) - Slow Performance (14 reviews) - Complexity (13 reviews) ### What Do G2 Reviewers Say About ServiceNow Workflow Data Fabric? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of ServiceNow Workflow Data Fabric, noting its simplicity and seamless integration with other services. - Users value the **seamless integration** of ServiceNow Workflow Data Fabric, enhancing coordination and efficiency across departments. - Users value the **automation capabilities** of ServiceNow Workflow Data Fabric for streamlining workflows and enhancing insights. - Users appreciate the **efficiency improvement** of ServiceNow Workflow Data Fabric, streamlining workflows and enhancing data accessibility. - Users value the **data consolidation** capabilities of ServiceNow Workflow Data Fabric, enhancing accessibility and decision-making efficiency. **Cons:** - Users note that the **complex setup** of ServiceNow Workflow Data Fabric can be challenging, requiring technical expertise for implementation. - Users find the **difficult setup** of ServiceNow Workflow Data Fabric to be complex and a barrier to adoption. - Users find the **licensing and costs can be expensive** , particularly for smaller organizations or those with limited budgets. - Users experience **slow performance** during data synchronization and setup, which complicates integration and affects collaboration speed. - Users find **learning and implementing ServiceNow Workflow Data Fabric complex** , especially for new users without strong data governance. #### What Are Recent G2 Reviews of ServiceNow Workflow Data Fabric? **"[Zero-Copy, Real-Time Intelligence with ServiceNow Workflow Data Fabric](https://www.g2.com/survey_responses/servicenow-workflow-data-fabric-review-12543653)"** **Rating:** 4.5/5.0 stars *— Younesse H.* [Read full review](https://www.g2.com/survey_responses/servicenow-workflow-data-fabric-review-12543653) --- **"[Keeps ServiceNow Workflows in Sync with Current, Unified Data](https://www.g2.com/survey_responses/servicenow-workflow-data-fabric-review-12541512)"** **Rating:** 4.5/5.0 stars *— Youssef N.* [Read full review](https://www.g2.com/survey_responses/servicenow-workflow-data-fabric-review-12541512) --- ### 6. [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) Monte Carlo is the agent trust platform, trusted by Nasdaq, Honeywell, Roche, and hundreds of enterprise organizations worldwide. Founded in 2019 and backed by leading investors, Monte Carlo pioneered data observability and has expanded into the full AI reliability stack. We're consistently ranked #1 in data observability on G2 — and we're built for what comes next. As enterprises scale from dozens to hundreds of AI agents across mission-critical use cases, Monte Carlo monitors, troubleshoots, and improves both those agents and the underlying data powering them. Our platform covers the full trust stack — from the data pipelines feeding agents, to the context they retrieve, the decisions they make, and the outputs they produce — across four trust dimensions: context quality, performance, behavior, and outputs. Critically, we meet enterprises wherever they are on the spectrum from human-guided oversight to fully autonomous operations. With 100+ integrations across Snowflake, Databricks, and the rest of your stack, you get full coverage without ripping anything out. Traditional monitoring tools stop at the pipeline or cover only one dimension of reliability — leaving teams to manually investigate, diagnose, and fix failures across disconnected tools. Monte Carlo closes that gap. Teams using Monte Carlo dramatically reduce time to detect and resolve data and AI incidents, scale monitoring coverage without scaling headcount, and build the internal trust that turns AI investments into real business outcomes. If your organization is serious enough about AI to put it in front of customers, executives, and critical decisions — Monte Carlo is the foundation it needs. **Average Rating:** 4.3/5.0 **Total Reviews:** 520 **How Do G2 Users Rate Monte Carlo?** - **Data Observability:** 9.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 7.7/10 (Category avg: 8.6/10) - **Ease of Use:** 8.3/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Monte Carlo?** - **Seller:** [Monte Carlo](https://www.g2.com/sellers/monte-carlo) - **Company Website:** https://montecarlo.ai/ - **Year Founded:** 2019 - **HQ Location:** San Francisco, US - **Twitter:** @montecarlodata (1,576 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/monte-carlo-data/ (548 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Financial Services, Computer Software - **Company Size:** 50% Enterprise, 43% Mid-Market #### What Are Monte Carlo's Pros and Cons? **Pros:** - Ease of Use (104 reviews) - Alerts (98 reviews) - Monitoring (92 reviews) - Alerting System (72 reviews) - Data Quality (49 reviews) **Cons:** - Alert Management (58 reviews) - Alert Overload (57 reviews) - Inefficient Alert System (47 reviews) - UX Improvement (46 reviews) - Limited Functionality (36 reviews) ### What Do G2 Reviewers Say About Monte Carlo? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Monte Carlo, finding its intuitive layout and guidance highly efficient. - Users value the **customizable alerts** in Monte Carlo, enhancing communication and proactive data quality management with ease. - Users highly value the **monitoring capabilities** of Monte Carlo, enabling proactive detection of data quality issues efficiently. - Users value the **powerful monitoring and alerting features** of Monte Carlo for effective data quality management. - Users value the **automated anomaly detection** in Monte Carlo, enhancing data quality and ensuring timely issue notifications. **Cons:** - Users find the **lack of manual threshold settings** for alerts limiting, affecting alert customization and flexibility. - Users experience **alert overload** due to excessive notifications, requiring adjustments for better sensitivity and relevance. - Users find the **inefficient alert system** frustrating, with issues in notifications and complex configurations hindering usability. - Users find the **UX improvement** necessary due to slow performance and disorganized features causing confusion. - Users find **limited functionality** in Monte Carlo, particularly regarding manual threshold settings and custom metrics for data comparison. #### What Are Recent G2 Reviews of Monte Carlo? **"[Smart Data Observability and Lineage That Saves Hours of Debugging](https://www.g2.com/survey_responses/monte-carlo-review-12935974)"** **Rating:** 5.0/5.0 stars *— Vandan T.* [Read full review](https://www.g2.com/survey_responses/monte-carlo-review-12935974) --- **"[Robust Data Monitoring with Seamless Alerts](https://www.g2.com/survey_responses/monte-carlo-review-12888671)"** **Rating:** 4.0/5.0 stars *— Sunny J.* [Read full review](https://www.g2.com/survey_responses/monte-carlo-review-12888671) --- #### What Are G2 Users Discussing About Monte Carlo? - [What is your primary use case for Monte Carlo, and how has it impacted your data observability?](https://www.g2.com/discussions/what-is-your-primary-use-case-for-monte-carlo-and-how-has-it-impacted-your-data-observability) - [What are the characteristics of Monte Carlo simulation?](https://www.g2.com/discussions/what-are-the-characteristics-of-monte-carlo-simulation) - [What software is used for Monte Carlo simulation?](https://www.g2.com/discussions/what-software-is-used-for-monte-carlo-simulation) - [What is Monte Carlo method used for?](https://www.g2.com/discussions/what-is-monte-carlo-method-used-for) - [What is Monte Carlo software?](https://www.g2.com/discussions/what-is-monte-carlo-software) - 1 comment ### 7. [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews) For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Astronomer is the driving force behind Apache Airflow™, the de facto standard for expressing data flows as code. Airflow is downloaded more than 31 million times each month and is used by hundreds of thousands of teams around the world. **Average Rating:** 4.5/5.0 **Total Reviews:** 135 **How Do G2 Users Rate Astro by Astronomer?** - **Data Observability:** 8.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.0/10 (Category avg: 8.6/10) - **Ease of Use:** 9.0/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Astro by Astronomer?** - **Seller:** [Astronomer](https://www.g2.com/sellers/astronomer) - **Company Website:** https://www.astronomer.io/ - **Year Founded:** 2018 - **HQ Location:** New York, US - **Twitter:** @astronomerio (19,697 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10019299 (4,595 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 47% Mid-Market, 38% Enterprise #### What Are Astro by Astronomer's Pros and Cons? **Pros:** - Ease of Use (25 reviews) - Efficiency Improvement (14 reviews) - User Interface (13 reviews) - Automation (11 reviews) - Deployment Ease (10 reviews) **Cons:** - Expensive (8 reviews) - Learning Difficulty (8 reviews) - Learning Curve (6 reviews) - Difficult Learning (5 reviews) - Feature Limitations (5 reviews) ### What Do G2 Reviewers Say About Astro by Astronomer? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Astro, finding it intuitive and efficient for complex workflows. - Users appreciate the **efficiency improvement** of Astro, saving time and simplifying data orchestration without needing a DevOps member. - Users appreciate the **intuitive user interface** of Astro, making it easy to manage complex workflows effectively. - Users value the **automation features** of Astro by Astronomer, enhancing data orchestration and simplifying workflows significantly. - Users appreciate the **deployment ease** of Astro by Astronomer, enabling reliable pipelines and efficient management of Airflow environments. **Cons:** - Users find Astro by Astronomer to be **expensive** , especially concerning for smaller teams and self-hosted setups. - Users report a **steep learning curve** , making it challenging for new team members to adapt quickly to Astro. - Users find the **steep learning curve** of Astro challenging, requiring significant time for new users to adapt. - Users report a **steep learning curve** for new users, requiring additional time for adaptation and training with Astro. - Users find the **feature limitations** of Astro by Astronomer restrictive, impacting flexibility and customization options. #### What Are Recent G2 Reviews of Astro by Astronomer? **"[Excellent developer and customer experience](https://www.g2.com/survey_responses/astro-by-astronomer-review-8428848)"** **Rating:** 5.0/5.0 stars *— Juan Roberto H.* [Read full review](https://www.g2.com/survey_responses/astro-by-astronomer-review-8428848) --- **"[Asro literally assists in data engineering work, making it easier and more productive.](https://www.g2.com/survey_responses/astro-by-astronomer-review-8519803)"** **Rating:** 5.0/5.0 stars *— Lakshminarayanan K.* [Read full review](https://www.g2.com/survey_responses/astro-by-astronomer-review-8519803) --- #### What Are G2 Users Discussing About Astro by Astronomer? - [What is your experience with Astro by Astronomer for data orchestration, and what challenges have you faced?](https://www.g2.com/discussions/what-is-your-experience-with-astro-by-astronomer-for-data-orchestration-and-what-challenges-have-you-faced) - [What is Astro by Astronomer used for?](https://www.g2.com/discussions/what-is-astro-by-astronomer-used-for) ### 8. [kestra](https://www.g2.com/products/kestra-technologies-kestra/reviews) Kestra is an open-source workflow orchestration and automation platform that enables organizations to define, schedule, and monitor both event-driven and time-based workflows. It brings Infrastructure-as-Code principles to orchestration, allowing teams to manage processes through YAML definitions or an intuitive user interface. Kestra is designed for data engineers, developers, and DevOps teams who need to automate data pipelines, integrate distributed systems, or coordinate multi-step workflows across hybrid or cloud environments. Core Capabilities Declarative orchestration: Workflows are defined in YAML and automatically synchronized with changes made through the UI or API. Event-driven and scheduled triggers: Supports automation initiated by timers or external events such as file arrivals, API calls, or message queues (Kafka, Redis, Pulsar, AMQP, MQTT, NATS, AWS SQS, Google Pub/Sub, Azure Event Hubs). Visual workflow design: Build and modify workflows directly in the browser with syntax validation, auto-completion, and real-time DAG visualization. Scalability and resilience: Distributed architecture provides high availability, fault tolerance, retries, and backfill management for millions of executions. Plugin ecosystem: Hundreds of integrations for databases, APIs, cloud platforms, and languages including Python, Node.js, Go, R, and Shell. Key Benefits Unified orchestration layer: Manage scheduled and event-based workflows in one system instead of maintaining separate schedulers or automation tools. Version control integration: Workflows can be stored in Git repositories, supporting CI/CD pipelines and Terraform for Infrastructure-as-Code management. Multi-environment flexibility: Deploy locally, in containers, or across Kubernetes clusters and major cloud providers (AWS, GCP, Azure). Observability and notifications: Track executions, manage inputs and outputs, and send alerts via Slack, PagerDuty, or email. Extensibility: Users can develop custom plugins to extend functionality and standardize internal operations. Typical Use Cases Data orchestration: Build and schedule ETL pipelines, batch or streaming data processes, and big-data workflows with tools like Spark and BigQuery. AI and ML automation: Coordinate model training, evaluation, and deployment workflows. Infrastructure automation: Replace cron jobs or legacy schedulers with declarative, event-driven execution. Microservice coordination: Connect APIs and services into reliable end-to-end processes. Getting Started Kestra can be launched locally in one command using Docker or deployed in production environments with Docker Compose, Kubernetes, or AWS CloudFormation. Users can create and run their first “Hello World” workflow in minutes via the built-in editor or API. Community and Support Kestra is distributed under the Apache 2.0 License and maintained by an active open-source community. **Average Rating:** 4.6/5.0 **Total Reviews:** 24 **How Do G2 Users Rate kestra?** - **Data Observability:** 9.8/10 (Category avg: 9.0/10) - **Testing capabilities:** 10.0/10 (Category avg: 8.6/10) - **Ease of Use:** 9.5/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 0.0/10 (Category avg: 10/10) **Who Is the Company Behind kestra?** - **Seller:** [Kestra Technologies](https://www.g2.com/sellers/kestra-technologies) - **Year Founded:** 2022 - **HQ Location:** Paris, FR - **Twitter:** @kestra_io (3,146 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kestra/ (67 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 80% Small-Business, 12% Mid-Market #### What Are kestra's Pros and Cons? **Pros:** - Centralized Management (1 reviews) - Customer Support (1 reviews) - Customization (1 reviews) - Data Security (1 reviews) - Documentation (1 reviews) **Cons:** - Alert Overload (1 reviews) - UX Improvement (1 reviews) ### What Do G2 Reviewers Say About kestra? *AI-generated summary from verified user reviews* **Pros:** - Users commend Kestra for its **centralized management** , enhancing collaboration and workflow triggers across teams effectively. - Users appreciate the **responsive customer support** and excellent documentation of Kestra, enhancing their development experience. - Users value the **customization options** of Kestra, facilitating tailored workflows that enhance collaboration across teams. - Users commend Kestra for its **exceptional data security** , ensuring reliable automation and collaboration across teams. - Users praise Kestra's **excellent documentation** and supportive community, making it easy to integrate and develop workflows. **Cons:** - Users find the **alert overload** in Kestra's UI overwhelming and difficult to filter, complicating their work processes. - Users find the **Logging/Task Runs menu items overwhelming** , making it hard to filter and navigate effectively. #### What Are Recent G2 Reviews of kestra? **"[Exceptional Orchestration for Resilient, Self-Healing Data Pipelines](https://www.g2.com/survey_responses/kestra-review-12935633)"** **Rating:** 4.0/5.0 stars *— Deemanth G.* [Read full review](https://www.g2.com/survey_responses/kestra-review-12935633) --- **"[The Adamantium to my fragile AI pipeline](https://www.g2.com/survey_responses/kestra-review-12931708)"** **Rating:** 4.5/5.0 stars *— Khushal A.* [Read full review](https://www.g2.com/survey_responses/kestra-review-12931708) --- ### 9. [Y42](https://www.g2.com/products/y42-y42/reviews) Y42’s Turnkey Data Orchestration Platform with embedded Observability gives data practitioners a unified space to reliably build, monitor, and maintain the flow of data to power their business analytics and AI applications. Y42 provides native integration of best-of-breed open-source data tools, comprehensive data governance, and better collaboration for data teams. With Y42, organizations enjoy increased accessibility to data and can make data-driven decisions reliably and efficiently. **Average Rating:** 4.9/5.0 **Total Reviews:** 21 **How Do G2 Users Rate Y42?** - **Data Observability:** 9.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 9.6/10 (Category avg: 8.6/10) - **Ease of Use:** 9.4/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Y42?** - **Seller:** [Y42](https://www.g2.com/sellers/y42-f0288f79-5826-460d-ba84-59d0f8b2f3b3) - **Year Founded:** 2020 - **HQ Location:** Berlin, DE - **Twitter:** @y42dotcom (276 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/64543299 (22 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 52% Small-Business, 38% Mid-Market #### What Are Recent G2 Reviews of Y42? **"[dbt on steroids](https://www.g2.com/survey_responses/y42-review-8967021)"** **Rating:** 5.0/5.0 stars *— Pierre Z.* [Read full review](https://www.g2.com/survey_responses/y42-review-8967021) --- **"[Great integrated cloud platform with smooth workflows to build and run data pipelines](https://www.g2.com/survey_responses/y42-review-8532682)"** **Rating:** 5.0/5.0 stars *— Max P.* [Read full review](https://www.g2.com/survey_responses/y42-review-8532682) --- #### What Are G2 Users Discussing About Y42? - [What is Y42 used for?](https://www.g2.com/discussions/what-is-y42-used-for) ### 10. [Peliqan](https://www.g2.com/products/peliqan/reviews) Peliqan.io is an all-in-one AI-first data integration and automation platform designed for business teams, scale-ups and consultants. Unlike traditional data tools that demand heavy engineering effort, Peliqan enables both business users and technical teams to connect, manage, and activate their data in one collaborative environment - without requiring a dedicated data engineer. With 250+ built-in connectors, Peliqan connects to databases, SaaS business applications (ERP, CRM, Accounting, HRM/ATS etc.), cloud storage, files and APIs as well as on-prem data sources. New connectors are available on demand within 5 business days. Peliqan offers one-click ELT pipelines to the built-in data warehouse, or you can bring your own data warehouse. Peliqan supports all major data warehouses. Thanks to Peliqan’s Excel add-in, business users and consultants can work with real-time data in Excel. Analysts and power users can use Peliqan’s advanced SQL editor with the support of an AI assistant to transform data and prepare business-ready data sets, which can be used in any BI tool such as Microsoft Power BI, Metabase, Tableau, Qlik, Looker etc. Users can also set up Reverse ETL flows. Developers can go even further with Peliqan’s low-code environment, with a built-in virtual AI Data Engineer, where they can: - Build & Publish interactive data apps - Automate writebacks into source systems - Publish API endpoints for data sharing - Implement custom pipelines - Build out internal AI Agents By empowering business users, analysts, consultants and developers, Peliqan dramatically reduces reliance on IT support and speeds up decision-making. Peliqan is not just an ELT data pipeline tool, it’s a complete solution for data orchestration, automation, and activation. Peliqan also acts as the data foundation for Agentic AI, ensuring that AI agents work with trusted, up-to-date 360° views of customers, products, orders, and more - at the speed of a cloud data warehouse. Peliqan’s data warehouse provides an AI-ready data layer out-of-the-box including: - Automatic vectorizing of structured and non-structured data for RAG (Retrieval-Augmented Generation) - Text-to-SQL - MCP Gateway In today’s landscape, a data warehouse is no longer just for BI - it’s the foundation for both BI and AI. With Peliqan.io, organizations can integrate, analyze, and activate their data seamlessly, empowering both humans and AI agents to make smarter, faster decisions. **Average Rating:** 4.8/5.0 **Total Reviews:** 78 **How Do G2 Users Rate Peliqan?** - **Data Observability:** 9.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 9.6/10 (Category avg: 8.6/10) - **Ease of Use:** 9.3/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 4.9/10 (Category avg: 10/10) **Who Is the Company Behind Peliqan?** - **Seller:** [Peliqan](https://www.g2.com/sellers/peliqan) - **Company Website:** https://peliqan.io - **Year Founded:** 2022 - **HQ Location:** Gent - **Twitter:** @Peliqan_io (9 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/peliqan-data (29 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 44% Mid-Market, 43% Small-Business #### What Are Peliqan's Pros and Cons? **Pros:** - Ease of Use (45 reviews) - Integrations (43 reviews) - Easy Integrations (37 reviews) - Connectors (36 reviews) - Data Management (36 reviews) **Cons:** - Learning Difficulty (18 reviews) - Required Technical Skills (12 reviews) - Feature Limitations (10 reviews) - Learning Curve (10 reviews) - Steep Learning Curve (9 reviews) ### What Do G2 Reviewers Say About Peliqan? *AI-generated summary from verified user reviews* **Pros:** - Users find Peliqan's **ease of use** remarkable, allowing seamless data integration and automation without coding. - Users love the **extensive integrations** offered by Peliqan, streamlining data connections and saving significant development time. - Users value the **easy integrations** of Peliqan, enabling instant connections to various apps and enhanced workflows. - Users value the **extensive connector ecosystem** of Peliqan, enabling seamless data integration and streamlined workflows. - Users value the **easy data integration** features of Peliqan, which significantly reduce development time and coding needs. **Cons:** - Users find the **learning difficulty** significant, especially for non-tech individuals needing assistance to navigate advanced features. - Users find that Peliqan requires **technical skills like Python and SQL** , making initial setup challenging for non-experts. - Users find **feature limitations** in Peliqan, such as a need for more automation templates and advanced alerting options. - Users face a **high initial learning curve** with Peliqan, requiring proper guidance and time for setup and error handling. - Users find the **steep learning curve** challenging, especially those without technical backgrounds or prior knowledge in machine learning. #### What Are Recent G2 Reviews of Peliqan? **"[Custom Python Tools Made Our Agent Fit Our Workflow](https://www.g2.com/survey_responses/peliqan-review-12991439)"** **Rating:** 5.0/5.0 stars *— PRADYUMNA K.* [Read full review](https://www.g2.com/survey_responses/peliqan-review-12991439) --- **"[Peliqan Makes Multi-Client AI Setups Simple and Consistent Across Claude and ChatGPT](https://www.g2.com/survey_responses/peliqan-review-12993223)"** **Rating:** 4.5/5.0 stars *— prashant r.* [Read full review](https://www.g2.com/survey_responses/peliqan-review-12993223) --- ### 11. [Weld](https://www.g2.com/products/weld-weld/reviews) Weld delivers an ultra-fast, secure, and reliable way to move data from all your tools, applications, and databases into cloud data warehouses, such as Snowflake, BigQuery, and Databricks. Deploy data pipelines in minutes with connectors that adapt to schema changes, detect duplicates, self-heal on failure, and run without maintenance, so your data team can focus on insights, not infrastructure. **Average Rating:** 4.8/5.0 **Total Reviews:** 104 **How Do G2 Users Rate Weld?** - **Data Observability:** 8.0/10 (Category avg: 9.0/10) - **Testing capabilities:** 7.6/10 (Category avg: 8.6/10) - **Ease of Use:** 8.8/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 5.5/10 (Category avg: 10/10) **Who Is the Company Behind Weld?** - **Seller:** [Weld](https://www.g2.com/sellers/weld-733aad41-2e36-4f42-9349-7d847f41d873) - **Company Website:** https://weld.app/ - **Year Founded:** 2021 - **HQ Location:** Copenhagen, DK - **Twitter:** @WeldHQ (98 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/weldhq/ (91 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** CEO - **Top Industries:** Computer Software, Retail - **Company Size:** 59% Small-Business, 40% Mid-Market #### What Are Weld's Pros and Cons? **Pros:** - Customer Support (10 reviews) - Ease of Use (9 reviews) - Automation (8 reviews) - Features (7 reviews) - Implementation Ease (7 reviews) **Cons:** - Limited Connectors (5 reviews) - Feature Limitations (3 reviews) - Limited Integrations (3 reviews) - Complex Setup (2 reviews) - Connection Issues (2 reviews) ### What Do G2 Reviewers Say About Weld? *AI-generated summary from verified user reviews* **Pros:** - Users highlight the **exceptional customer support** of Weld, providing quick resolutions and enhancing overall user experience. - Users commend the **ease of use** of Weld, finding it simple to connect data sources and integrate seamlessly. - Users appreciate the **automation capabilities** of Weld, significantly streamlining data integration and analysis for non-technical users. - Users highlight the **excellent customer support** and ease of setup, making the experience seamless and efficient. - Users highlight the **implementation ease** of Weld, simplifying setup and integration for smooth BI operations. **Cons:** - Users find the **limited connectors** in Weld's basic plan somewhat restrictive, impacting their overall integration capabilities. - Users find the **limited native connectors** in Weld's basic plan somewhat restrictive for their integration needs. - Users find the **limited integrations** in Weld, particularly with connectors, restricting for their needs. - Users find the **complex setup** challenging, especially when tracking property changes and debugging intricate queries. - Users occasionally face **connection issues** that may require attention, though Weld generally performs reliably once configured. #### What Are Recent G2 Reviews of Weld? **"[Easy Connector Setup and Great Value](https://www.g2.com/survey_responses/weld-review-12601466)"** **Rating:** 4.5/5.0 stars *— Lucas T.* [Read full review](https://www.g2.com/survey_responses/weld-review-12601466) --- **"[Seamless ETL with Stellar Support](https://www.g2.com/survey_responses/weld-review-12858335)"** **Rating:** 5.0/5.0 stars *— Edward S.* [Read full review](https://www.g2.com/survey_responses/weld-review-12858335) --- ### 12. [Hightouch](https://www.g2.com/products/hightouch/reviews) Hightouch is the leading data and Agentic Marketing Platform for modern marketing teams. Trusted by brands like Domino’s, Autotrader, cars.com, Superhuman (formerly Grammarly), and PetSmart, Hightouch helps marketers deliver personalized experiences, optimize performance, and move faster with data and AI. With Hightouch, business users can drive revenue, grow brand awareness, and maximize ROI without relying on engineering. Hightouch’s Composable Customer Data Platform (CDP), named a Leader in the 2026 Gartner® Magic Quadrant™ for Customer Data Platforms, collects behavioral data, resolves identities into unified Customer 360 profiles, builds audiences, syncs to 300+ destinations (including leading ad platforms), and measures campaign impact—directly from your cloud data warehouse. On top of this foundation, Hightouch’s Agentic Marketing Platform uses your complete data and all of the context from your marketing and advertising tools to power true end-to-end lifecycle and performance marketing across paid and owned channels. Purpose-built agents help you go from analyzing campaign performance, to ideating new campaigns, to generating creative, to building segments and cross-channel journeys, to activating audiences and optimization signals back into your ad platforms and downstream tools—often in minutes instead of weeks. Hightouch is built for security, compliance, and scale. Your data stays in your environment—Hightouch never becomes a system of record—and the platform meets SOC 2 Type II, HIPAA, ISO-27001, GDPR, CCPA, and Privacy Shield standards, so even the most regulated organizations can confidently use customer data to power marketing. This approach gives global teams a single, trusted foundation for activation while preserving strong governance, clear audit trails, and regional data residency requirements. **Average Rating:** 4.6/5.0 **Total Reviews:** 396 **How Do G2 Users Rate Hightouch?** - **Data Observability:** 8.4/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.1/10 (Category avg: 8.6/10) - **Ease of Use:** 9.2/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Hightouch?** - **Seller:** [Hightouch](https://www.g2.com/sellers/hightouch) - **Company Website:** https://hightouch.com/ - **Year Founded:** 2021 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/hightouchio/ (573 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 62% Mid-Market, 24% Small-Business #### What Are Hightouch's Pros and Cons? **Pros:** - Ease of Use (16 reviews) - Easy Integration (12 reviews) - Customer Support (9 reviews) - Easy Integrations (9 reviews) - Easy Setup (9 reviews) **Cons:** - Expensive (5 reviews) - Pricing Issues (5 reviews) - Integration Issues (4 reviews) - Slow Performance (4 reviews) - Syncing Issues (4 reviews) ### What Do G2 Reviewers Say About Hightouch? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** with Hightouch, finding it simple to sync data between applications seamlessly. - Users find the **easy integration** of Hightouch invaluable for seamlessly activating data across multiple platforms. - Users value the **fantastic customer support** from Hightouch, which significantly aids in setup and usage. - Users appreciate the **easy integrations** of Hightouch, which simplify onboarding and enhance the overall user experience. - Users find Hightouch's **easy setup** invaluable, simplifying the syncing of data across software applications effortlessly. **Cons:** - Users criticize Hightouch for its **expensive pricing** , urging for a more flexible and affordable plan for startups. - Users are frustrated with **pricing issues** , feeling the plan changes make Hightouch unaffordable and inaccessible. - Users face **integration issues** with Hightouch, experiencing errors and limited options that complicate usage and functionality. - Users report **slow performance** during larger data pulls, impacting their overall experience with Hightouch. - Users experience **syncing issues** that can delay processes and complicate interactions between systems like Snowflake and Salesforce. #### What Are Recent G2 Reviews of Hightouch? **"[Hightouch Speeds Insight-to-Execution for Scalable Personalization and Experiments](https://www.g2.com/survey_responses/hightouch-review-12955025)"** **Rating:** 5.0/5.0 stars *— Josh M.* [Read full review](https://www.g2.com/survey_responses/hightouch-review-12955025) --- **"[A user-friendly platform with powerful integrations](https://www.g2.com/survey_responses/hightouch-review-12595075)"** **Rating:** 5.0/5.0 stars *— Jonathan C.* [Read full review](https://www.g2.com/survey_responses/hightouch-review-12595075) --- #### What Are G2 Users Discussing About Hightouch? - [What is Hightouch used for?](https://www.g2.com/discussions/what-is-hightouch-used-for) - 1 comment ### 13. [Boost.space](https://www.g2.com/products/boost-space/reviews) Boost.space is the #1 AI-Ready Data Sync platform on G2—recognized with over 100 customer-voted badges. ⭐️ Today's enterprises are investing heavily in AI, but most initiatives fail. Why? Because AI is "stateless"—it has no memory—and it's running on chaotic data fragmented across hundreds of applications. This data chaos costs companies an average of $15 million annually and wastes nearly 12 hours per employee every week.   Boost.space solves this foundational problem by creating a new category of enterprise infrastructure: the AI Memory Layer. We are not just another application; we are the persistent, unified memory that your entire AI and automation ecosystem needs to function intelligently. Our platform connects to over 2,400 apps to dismantle data silos, creating a single source of truth (SSOT) for all your business data. But unlike passive data warehouses, Boost.space is an active, read-write memory. Our real-time, three-way synchronization engine allows AI agents to not only read unified data but also act on it by writing changes back to your operational tools. At the heart of this is our Model Context Protocol (MCP), an "AI-Ready USB-C Port" for your enterprise that allows you to prompt your business in natural language. Whether you're building an AI-Ready PIM for e-commerce or an AI-Ready CDP for marketing, Boost.space provides the essential foundation, trusted by leaders like ŠKODA to make their data AI-ready. **Average Rating:** 4.6/5.0 **Total Reviews:** 327 **How Do G2 Users Rate Boost.space?** - **Data Observability:** 8.4/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.4/10 (Category avg: 8.6/10) - **Ease of Use:** 7.3/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Boost.space?** - **Seller:** [Boost.space s.r.o.](https://www.g2.com/sellers/boost-space-s-r-o) - **Year Founded:** 2017 - **HQ Location:** Prague, CZ - **Twitter:** @boostspace (79 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/boost-space/?originalSubdomain=cz (40 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** CEO, Founder - **Top Industries:** Marketing and Advertising, Consulting - **Company Size:** 99% Small-Business, 1% Mid-Market #### What Are Boost.space's Pros and Cons? **Pros:** - Integrations (221 reviews) - Automation (211 reviews) - Easy Integrations (163 reviews) - Features (150 reviews) - Easy Integration (135 reviews) **Cons:** - Learning Curve (180 reviews) - Steep Learning Curve (153 reviews) - Learning Difficulty (65 reviews) - Complex Setup (45 reviews) - Beginner Difficulty (42 reviews) ### What Do G2 Reviewers Say About Boost.space? *AI-generated summary from verified user reviews* **Pros:** - Users love the **seamless integrations** in Boost.space, enabling efficient data management across multiple platforms. - Users appreciate the **automation capabilities** of Boost.space, enhancing workflow efficiency and streamlining business operations. - Users value the **easy integrations** of Boost.space, enjoying seamless connections and centralized data management across platforms. - Users appreciate the **enrich function** of Boost.space, enhancing data management and integration for marketing workflows. - Users value the **easy integration** of Boost.space, simplifying connections across various platforms and enhancing usability. **Cons:** - Users face a high **learning curve** , requiring time to master the interface and integrations effectively. - Users find the **steep learning curve** challenging initially, but appreciate the platform's power once mastered. - Users find the **learning curve steep** , requiring significant time investment to grasp the complex system effectively. - Users find the **complex setup** of Boost.space requires time and effort to fully understand and utilize effectively. - Users find the **beginner difficulty** challenging, often struggling with setup and requiring more guidance to navigate the platform. #### What Are Recent G2 Reviews of Boost.space? **"[a Wonderful combination of Airtable (Datasheets) & Zapir (Automation tool)](https://www.g2.com/survey_responses/boost-space-review-11005882)"** **Rating:** 4.5/5.0 stars *— Kapil A.* [Read full review](https://www.g2.com/survey_responses/boost-space-review-11005882) --- **"[An app that combines data storage like CRM/Airtable with the integration functionality of Make](https://www.g2.com/survey_responses/boost-space-review-9524372)"** **Rating:** 4.0/5.0 stars *— Michael G.* [Read full review](https://www.g2.com/survey_responses/boost-space-review-9524372) --- ### 14. [Nexla](https://www.g2.com/products/nexla/reviews) Nexla is an enterprise-grade, AI-powered data integration platform designed to help organizations unlock data from any source and transform it into production-ready data products for AI and agents. With support for 600+ pre-built connectors and multiple integration styles, including ELT, ETL, streaming, APIs, and agentic RAG, the platform enables teams to build and manage data flows without writing code. Trusted by leading enterprises, Nexla processes over one trillion records per month across industries, ​​showcasing its ability to handle large volumes of data while maintaining performance and reliability. Innovators like Autodesk, DoorDash, Instacart, Johnson & Johnson, LinkedIn, and LiveRamp rely on Nexla to keep mission-critical data flowing seamlessly across their enterprises. Key features of Nexla include flexible deployment across cloud, hybrid, and on-premises environments, ensuring compliance with enterprise-grade security standards such as SOC 2 Type II, GDPR, CCPA, and HIPAA. Nexla delivers 10x faster implementation than traditional alternatives, turning data challenges and variety into competitive advantages. Try our AI Data Engineer at https://express.dev Increase the impact of your data engineering team with next-gen data integration: ✅ Eliminate costly replications & reduce storage bills ✅ Increase engineering productivity & capacity for innovation ✅ Empower users with Pro/Low/No-code collaboration ✅ Cut out maintenance with data validation, quality monitoring, & alerts ✅ Build production-ready custom GenAI applications Go beyond one traditional integration pattern, and invest in data architecture that supports: ✅ Any integration pattern (ELT, ETL, API / API proxy, & RAG - Retrieval Augmented Generation) ✅ Bi-directional connectors out of the box & on demand ✅ Any processing speed (streaming, real-time, batch) ✅ Unstructured, structured, or semi-structured data ✅ Complete data lineage search & tagging for governance ✅ Metadata-driven architecture for agility & scale Nexla is a Gartner Cool Vendor and pairs perfectly with the technologies you rely on: ✅ Compute: AWS, Azure, Google Cloud, On-Premise ✅ Storage: S3, Redshift, BigQuery, Snowflake, Oracle, Databricks, Kafka, Redis, MongoDB, Postgres, MySQL ✅ Applications: SAP, Salesforce, Marketo, Hubspot, Amazon Seller Central, Google Ads, API, Salesforce ✅ Catalogs: Alation, Collibra, data.world ✅ Webhooks, emails, FTP & APIs ✅ Vector database & LLM: Pinecone, GPT, Falcon, LLaMDa And many more Differentiators & Awards 🏆 2025 Highest Rating Gartner Peer Insights™ Voice of the Customer for Data Integration Tools 🏆 2024 Highest Rating Gartner Peer Insights™ Voice of the Customer for Data Integration Tools 🏆 2023 Highest Rating Gartner Peer Insights™ Voice of the Customer for Data Integration Tools 🏆 2022 Highest Rating Gartner Peer Insights™ Voice of the Customer for Data Integration Tools **Average Rating:** 4.6/5.0 **Total Reviews:** 62 **How Do G2 Users Rate Nexla?** - **Data Observability:** 8.8/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.5/10 (Category avg: 8.6/10) - **Ease of Use:** 8.7/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 0/10 (Category avg: 10/10) **Who Is the Company Behind Nexla?** - **Seller:** [Nexla](https://www.g2.com/sellers/nexla) - **Company Website:** https://www.nexla.com/ - **Year Founded:** 2016 - **HQ Location:** San Mateo, California - **Twitter:** @NexlaInc (944 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/nexla/ (67 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Insurance - **Company Size:** 41% Mid-Market, 33% Small-Business #### What Are Nexla's Pros and Cons? **Pros:** - Ease of Use (21 reviews) - Automation (14 reviews) - Data Management (14 reviews) - Integrations (13 reviews) - Data Integration (10 reviews) **Cons:** - Learning Difficulty (7 reviews) - Slow Performance (7 reviews) - Difficult Learning (6 reviews) - Learning Curve (6 reviews) - Poor Documentation (6 reviews) ### What Do G2 Reviewers Say About Nexla? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Nexla, allowing non-technical data custodians to quickly integrate diverse data sources. - Users value the **automation capabilities** of Nexla, which significantly reduce manual work and enhance data consistency. - Users commend **easy data deployment and manipulation** with Nexla, appreciating its user-friendly interface and flexibility. - Users value the **seamless integrations** provided by Nexla, facilitating effortless connectivity across diverse data sources and formats. - Users admire the **ease of data integration** with Nexla, making complex processes accessible without coding skills. **Cons:** - Users find the **learning difficulty** in Nexla's setup frustrating, particularly for new users navigating its many features. - Users experience **slow performance** in UI and search, which affects the overall efficiency of Nexla. - Users find the setup process to be **difficult to learn** , particularly for new users navigating advanced features. - Users find the **learning curve steep** , particularly for new users adapting to Nexla's advanced features and documentation. - Users find the **poor documentation** of Nexla challenging, especially during setup and complex feature utilization. #### What Are Recent G2 Reviews of Nexla? **"[Robust Data Consolidation with Excellent Support](https://www.g2.com/survey_responses/nexla-review-12648347)"** **Rating:** 4.0/5.0 stars *— Jaspreet S.* [Read full review](https://www.g2.com/survey_responses/nexla-review-12648347) --- **"[Smooth Data Integration with Impressive Speed and UI](https://www.g2.com/survey_responses/nexla-review-12630612)"** **Rating:** 4.5/5.0 stars *— Srinivasulu L.* [Read full review](https://www.g2.com/survey_responses/nexla-review-12630612) --- #### What Are G2 Users Discussing About Nexla? - [How do you use Nexla?](https://www.g2.com/discussions/nexla-how-do-you-use-nexla) - [How do you use Nexla?](https://www.g2.com/discussions/how-do-you-use-nexla) - [What is a data fabric?](https://www.g2.com/discussions/nexla-what-is-a-data-fabric) - [What is a data fabric?](https://www.g2.com/discussions/what-is-a-data-fabric) - [What does DataOps do?](https://www.g2.com/discussions/nexla-what-does-dataops-do) ### 15. [ILUM](https://www.g2.com/products/ilum-ilum/reviews) Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible. **Average Rating:** 4.9/5.0 **Total Reviews:** 23 **How Do G2 Users Rate ILUM?** - **Data Observability:** 10.0/10 (Category avg: 9.0/10) - **Testing capabilities:** 9.8/10 (Category avg: 8.6/10) - **Ease of Use:** 9.3/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 7.5/10 (Category avg: 10/10) **Who Is the Company Behind ILUM?** - **Seller:** [Ilum](https://www.g2.com/sellers/ilum) - **Company Website:** https://ilum.cloud/ - **Year Founded:** 2019 - **HQ Location:** Santa Fe, US - **Twitter:** @IlumCloud (19 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/ilum-cloud/ (4 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Telecommunications - **Company Size:** 52% Enterprise, 35% Mid-Market #### What Are ILUM's Pros and Cons? **Pros:** - Ease of Use (17 reviews) - Features (17 reviews) - Integrations (17 reviews) - Setup Ease (16 reviews) - Easy Integrations (15 reviews) **Cons:** - Complex Setup (9 reviews) - Difficult Setup (9 reviews) - Learning Curve (9 reviews) - UX Improvement (8 reviews) - Complexity (7 reviews) ### What Do G2 Reviewers Say About ILUM? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of ILUM, appreciating its smooth integration and intuitive UI for seamless workflows. - Users appreciate the **seamless integration** of ILUM, simplifying Spark management and enhancing data organization and analytics. - Users value the **s seamless integration** of ILUM with existing systems, greatly enhancing data management and analytics workflows. - Users find ILUM's **seamless and fast setup** process impressive, enabling quick deployment and integration without hassle. - Users commend ILUM for its **seamless integrations** that effortlessly fit into existing tech stacks and workflows. **Cons:** - Users find the **complex setup** for advanced configurations can be challenging, though daily usage becomes intuitive thereafter. - Users find the **difficult setup** of ILUM challenging, particularly with advanced configurations and Kubernetes tuning. - Users find the **learning curve steep** initially, but support helps ease the process for effective use. - Users find the **UI sometimes unpolished and minimalistic** , making it less intuitive, especially for new users. - Users find ILUM's **complexity in advanced configurations** can be challenging, requiring additional time and resources for setup. #### What Are Recent G2 Reviews of ILUM? **"[Seamless Integration and Unified Features for Advanced Users](https://www.g2.com/survey_responses/ilum-review-11904273)"** **Rating:** 5.0/5.0 stars *— Jan L.* [Read full review](https://www.g2.com/survey_responses/ilum-review-11904273) --- **"[From Hadoop to K8s with lower TCO](https://www.g2.com/survey_responses/ilum-review-11862422)"** **Rating:** 5.0/5.0 stars *— Mark D.* [Read full review](https://www.g2.com/survey_responses/ilum-review-11862422) --- ### 16. [Mozart Data](https://www.g2.com/products/mozart-data-mozart-data/reviews) Backed by award-winning data analyst support, Mozart Data is the fastest way to set up scalable, reliable data infrastructure that doesn’t need to be maintained by you. Mozart Data’s all-in-one modern data platform empowers anyone to easily centralize, organize, and analyze their data without engineering resources. Instead of piecing together multiple tools, companies get everything they need to spin up a data stack in an hour — ETL, a data warehouse, and a data transformation tool — and gain visibility into their data pipelines. Join other data-driven companies, like Zeplin, Rippling, Modern Treasury, and Tempo, that are already getting the most out of their data. Learn more at https://www.mozartdata.com **Average Rating:** 4.6/5.0 **Total Reviews:** 68 **How Do G2 Users Rate Mozart Data?** - **Data Observability:** 8.7/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.0/10 (Category avg: 8.6/10) - **Ease of Use:** 9.3/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Mozart Data?** - **Seller:** [Mozart Data](https://www.g2.com/sellers/mozart-data) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **Twitter:** @MozartData (447 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/mozartdata/ (16 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Financial Services - **Company Size:** 53% Small-Business, 47% Mid-Market #### What Are Recent G2 Reviews of Mozart Data? **"[All in one data platform](https://www.g2.com/survey_responses/mozart-data-review-9998715)"** **Rating:** 5.0/5.0 stars *— Jim D.* [Read full review](https://www.g2.com/survey_responses/mozart-data-review-9998715) --- **"[Easy to use one and done tool to streamline the data process.](https://www.g2.com/survey_responses/mozart-data-review-9144933)"** **Rating:** 5.0/5.0 stars *— Craig C.* [Read full review](https://www.g2.com/survey_responses/mozart-data-review-9144933) --- #### What Are G2 Users Discussing About Mozart Data? - [What is Mozart Data used for?](https://www.g2.com/discussions/what-is-mozart-data-used-for) ### 17. [Stonebranch](https://www.g2.com/products/stonebranch/reviews) The Stonebranch Workload Automation solution, part of our Universal Automation Center platform, helps organizations automate, manage, and orchestrate their IT processes - across hybrid IT environments. 1. Workflow Orchestration and Automation: Holistically control scripts, jobs, tasks, and IT processes running across your on-prem, hybrid cloud, and/or multi-cloud environments. 2. Real-Time Automation: With our event-driven automation technology, it is now possible to achieve real-time automation across your entire hybrid IT environment. 3. Self-Service Automation: With a focus on ease-of-use, you can empower your workforce with self-service automation using member roles and permissions. 4. BI & Analytics: Centralize operational control and insight with proactive monitoring, reporting, and alerts. Product Features: - Drag-and-drop Workflow Creation: You don’t have to be a developer to create automation. Custom scripting is a thing of the past. Easily create workflows with an intuitive drag-and-drop user interface. - DevOps enabled: Align priorities between IT Ops and DevOps with Jobs-as-Code, Infrastructure-as-Code, and bundle-and-promote features. - Limitless 3rd Party Integrations: Integrate into any platform or application from the mainframe to the cloud. Use pre-packaged integrations, build your own, or download integration blueprints from the community-driven opensource marketplace. - Available on-premises or as a SaaS-based deployment, the UAC is a modern platform built to scale with your business. **Average Rating:** 4.5/5.0 **Total Reviews:** 125 **How Do G2 Users Rate Stonebranch?** - **Data Observability:** 9.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.9/10 (Category avg: 8.6/10) - **Ease of Use:** 8.7/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Stonebranch?** - **Seller:** [Stonebranch, Inc](https://www.g2.com/sellers/stonebranch-inc) - **Company Website:** https://www.stonebranch.com - **Year Founded:** 1999 - **HQ Location:** Alpharetta, GA - **Twitter:** @Stonebranch (1,181 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/71261/ (176 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer - **Top Industries:** Information Technology and Services, Insurance - **Company Size:** 53% Enterprise, 31% Mid-Market #### What Are Stonebranch's Pros and Cons? **Pros:** - Ease of Use (24 reviews) - Automation (15 reviews) - Customer Support (11 reviews) - Workflow Automation (11 reviews) - Workflow Management (10 reviews) **Cons:** - Complexity (15 reviews) - Difficult Learning (7 reviews) - Poor Documentation (7 reviews) - Learning Curve (6 reviews) - Complex Setup (4 reviews) ### What Do G2 Reviewers Say About Stonebranch? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of Stonebranch, finding it intuitive and user-friendly for both new and experienced users. - Users appreciate Stonebranch for its **intuitive automation** , simplifying workflows and enhancing operational efficiency effortlessly. - Users value the **responsive and helpful customer support** of Stonebranch that ensures smooth implementation and integration. - Users appreciate the **workflow automation features** of Stonebranch, enabling efficient job management and enhanced productivity. - Users highlight the **intuitive workflow management** of Stonebranch, praising its ease of use and efficient task handling. **Cons:** - Users find the **complexity of configurations and integrations** in Stonebranch to be a significant challenge in their experience. - Users face a **difficult learning curve** with Stonebranch, hindered by complex configurations and navigation challenges. - Users find the **poor documentation** challenging, making it difficult to navigate the platform and utilize its features effectively. - Users find the **steep learning curve** daunting, needing more refined documentation and user-friendly guidance. - Users find the **complex setup** challenging, especially for new users navigating the configuration and training options. #### What Are Recent G2 Reviews of Stonebranch? **"[Streamlines IT Automation with Ease](https://www.g2.com/survey_responses/stonebranch-review-12679840)"** **Rating:** 5.0/5.0 stars *— Jeff V.* [Read full review](https://www.g2.com/survey_responses/stonebranch-review-12679840) --- **"[Great Performance That Speeds Up Client Feed Onboarding](https://www.g2.com/survey_responses/stonebranch-review-12703027)"** **Rating:** 4.5/5.0 stars *— Verified User in Information Technology and Services* [Read full review](https://www.g2.com/survey_responses/stonebranch-review-12703027) --- #### What Are G2 Users Discussing About Stonebranch? - [How has Stonebranch supported your IT automation efforts, and what do you recommend for those considering it?](https://www.g2.com/discussions/how-has-stonebranch-supported-your-it-automation-efforts-and-what-do-you-recommend-for-those-considering-it) - [What is Stonebranch used for?](https://www.g2.com/discussions/what-is-stonebranch-used-for) - 1 comment - [What does Stonebranch do?](https://www.g2.com/discussions/what-does-stonebranch-do) - 1 comment, 1 upvote - [What is workload automation software?](https://www.g2.com/discussions/stonebranch-what-is-workload-automation-software) - 1 comment, 1 upvote - [What is Enterprise Scheduling?](https://www.g2.com/discussions/what-is-enterprise-scheduling) - 1 comment, 1 upvote ### 18. [Coalesce Catalog (formerly CastorDoc)](https://www.g2.com/products/castor-doc/reviews) Coalesce Catalog is a collaborative, automated data discovery & catalog tool. We believe that data people spend way too much time trying to find and understand their data. Coalesce Catalog redesigns how data people collaborate. It provides a single source of truth to reference and document all the knowledge related to data within your company. If you are looking for a table related to your customers, just look for it as you would in Google, and Coalesce Catalog provides you with all the context you will need for your analysis. Inspired by internal tools developed by Uber, Airbnb, Lyft, and Spotify, Coalesce Catalog has developed a plug-and-play solution that deploys in minutes to drive value for companies of all sizes. Discover and catalog your data today with Coalesce Catalog. **Average Rating:** 4.7/5.0 **Total Reviews:** 63 **How Do G2 Users Rate Coalesce Catalog (formerly CastorDoc)?** - **Data Observability:** 8.5/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.1/10 (Category avg: 8.6/10) - **Ease of Use:** 9.6/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Coalesce Catalog (formerly CastorDoc)?** - **Seller:** [Coalesce](https://www.g2.com/sellers/coalesce) - **Company Website:** https://coalesce.io/ - **Year Founded:** 2020 - **HQ Location:** San Francisco, CA - **LinkedIn® Page:** https://www.linkedin.com/company/coalesceio/ (118 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 59% Mid-Market, 27% Enterprise #### What Are Coalesce Catalog (formerly CastorDoc)'s Pros and Cons? **Pros:** - Ease of Use (3 reviews) - Collaboration (2 reviews) - Connectivity (2 reviews) - Data Lineage (2 reviews) - Useful (2 reviews) **Cons:** - Connector Issues (1 reviews) - Integration Issues (1 reviews) - Limitations (1 reviews) ### What Do G2 Reviewers Say About Coalesce Catalog (formerly CastorDoc)? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Coalesce Catalog, finding it simple and intuitive for data discovery and management. - Users value the **collaborative integration** with Slack, enhancing data sharing and onboarding for teams effectively. - Users appreciate the **easy connectivity** of Coalesce Catalog, allowing seamless integration and efficient data discovery. - Users value the **data lineage feature** of Coalesce Catalog for simplifying data discovery and enhancing onboarding. - Users find the **lineage feature** invaluable for simplifying data discovery and enhancing collaboration across teams. **Cons:** - Users find the **connector issues** limiting, as direct connections to other AI agents are unavailable. - Users experience **integration issues** as connecting other AI agents to the Coalesce Catalog knowledge is currently impossible. - Users face the **limitation of not connecting other AI agents** directly to the Coalesce Catalog knowledge base. #### What Are Recent G2 Reviews of Coalesce Catalog (formerly CastorDoc)? **"[User-friendly & easy adoption for all of our data users](https://www.g2.com/survey_responses/coalesce-catalog-formerly-castordoc-review-11753164)"** **Rating:** 4.5/5.0 stars *— Théo C.* [Read full review](https://www.g2.com/survey_responses/coalesce-catalog-formerly-castordoc-review-11753164) --- **"[A great tool to organize and explain your data hub](https://www.g2.com/survey_responses/coalesce-catalog-formerly-castordoc-review-10622007)"** **Rating:** 4.5/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/coalesce-catalog-formerly-castordoc-review-10622007) --- ### 19. [Atlan](https://www.g2.com/products/atlan/reviews) Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve. **Average Rating:** 4.5/5.0 **Total Reviews:** 131 **How Do G2 Users Rate Atlan?** - **Data Observability:** 8.5/10 (Category avg: 9.0/10) - **Testing capabilities:** 7.4/10 (Category avg: 8.6/10) - **Ease of Use:** 8.9/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Atlan?** - **Seller:** [Atlan](https://www.g2.com/sellers/atlan) - **Company Website:** https://www.atlan.com - **Year Founded:** 2019 - **HQ Location:** New York, US - **Twitter:** @AtlanHQ (9,804 Twitter followers) - **LinkedIn® Page:** https://in.linkedin.com/company/atlan-hq (572 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Financial Services, Information Technology and Services - **Company Size:** 52% Mid-Market, 41% Enterprise #### What Are Atlan's Pros and Cons? **Pros:** - Ease of Use (7 reviews) - Features (6 reviews) - Collaboration (5 reviews) - Data Cataloging (5 reviews) - Easy Setup (4 reviews) **Cons:** - Integration Issues (4 reviews) - Dependency Issues (3 reviews) - Limited Customization (3 reviews) - Technical Issues (3 reviews) - User Interface Issues (3 reviews) ### What Do G2 Reviewers Say About Atlan? *AI-generated summary from verified user reviews* **Pros:** - Users value Atlan's **ease of use** , making data collaboration and discovery a seamless experience for everyone. - Users love Atlan for its **great data discovery and collaboration features** , making data management effortless and efficient. - Users value Atlan for its **s seamless data collaboration** , enhancing teamwork and fostering trust in data management. - Users appreciate the **ease of data discovery and collaboration** with Atlan, enhancing their data management experience. - Users value the **easy setup** of Atlan, which streamlines implementation and enhances collaborative data experiences seamlessly. **Cons:** - Users face **integration issues** with Teams and non-native databases, complicating workflows and requiring extra setup. - Users note significant **dependency issues** with Atlan, causing barriers to effective use and integration with other tools. - Users note the **limited customization** options in Atlan, making it challenging to tailor the experience to specific workflows. - Users experience **slow technical support and occasional inaccuracies** , impacting their overall satisfaction with Atlan. - Users face challenges with the **limited customization and technical UI** , resulting in a steeper learning curve for business users. #### What Are Recent G2 Reviews of Atlan? **"[Outstanding AI Features, Easy UI, and a Truly Responsive Atlan Team](https://www.g2.com/survey_responses/atlan-review-12721177)"** **Rating:** 4.5/5.0 stars *— Verified User in Apparel & Fashion* [Read full review](https://www.g2.com/survey_responses/atlan-review-12721177) --- **"[Helpful UI, Flexible Data Model, and Great API](https://www.g2.com/survey_responses/atlan-review-12518328)"** **Rating:** 4.0/5.0 stars *— Verified User in Health, Wellness and Fitness* [Read full review](https://www.g2.com/survey_responses/atlan-review-12518328) --- ### 20. [Keboola](https://www.g2.com/products/keboola/reviews) Keboola is the unified AI & Data orchestration platform that empowers organizations to turn data into business value faster and more securely than ever. It acts as your agentic AI co-pilot for data workflows, automating everything from integration to insight. With Keboola, Engineering teams, digital natives, startup CTOs, and innovation leads alike can rapidly build and manage data products, applications, AI agents, and autonomous crews seamlessly—without sacrificing compliance or security. Built for Every Data Persona: Whether you’re a seasoned data engineer or a business analyst, Keboola is built to make you successful. Data engineers love the open extensibility – code in SQL, Python, R, or use our API/CLI to tailor any step. Analysts and non-coders love the self-service UI – point-and-click data pipeline assembly, drag-and-drop transformations with text to SQL on semantic layer, and one-click deployment of pre-built workflows. Collaboration is seamless, with shared workspaces and sandboxes that let teams build and share data products freely without affecting production. What sets us apart? With Keboola, you can build and manage data products, applications, AI agents, and autonomous crews seamlessly—without sacrificing compliance or security. 🔗 Unified Connectivity: Effortlessly connect to 700+ data sources (databases, SaaS apps, and APIs) .Real-time Streams, Change Data Capture or batch. 🤖 Agentic AI Orchestration: Keboola’s AI-driven engine orchestrates data pipelines and ML workflows automatically. It can trigger the next steps based on data events or quality checks, and dynamically allocate resources. Think of it as an autopilot for your data & AI, ensuring pipelines run optimally and recover on their own from hiccups. 🛡️ Built-in Governance & Security: Every dataset and process in Keboola is governed. Fine-grained access controls, lineage tracking, and audit logs are native to the platform. Compliance is simplified – SOC 2, GDPR, and industry standards are supported out-of-the-box. 🚀 Rapid Development & Prototyping: Innovate without constraints. Spin up isolated dev/test sandboxes in seconds to prototype new data products or AI models. 🌎 Multi-Cloud Scalability: Built on a cloud-native architecture, Keboola scales with your needs. Deploy on your preferred cloud (AWS, Azure, GCP) and let Keboola handle the heavy lifting – elastic compute, parallel processing, and workload optimization. Start small and scale to enterprise workloads globally, without re-architecting. 💡 End-to-End Insight Activation: Because Keboola unifies your data pipelines, analytics, and ML, you can go from raw data to AI-driven insights in record time. Why Keboola: Instead of cobbling together multiple tools for integration, ETL/ELT, data catalogs, automation, and AI, Keboola delivers a single platform that does it all – with unprecedented ease and intelligence. Our customers have replaced 5-10 disparate tools with Keboola’s unified solution, drastically accelerating delivery. Join 30,000+ companies and industry leaders who use Keboola to supercharge their data teams. Whether you need to deliver data to AI Agents, streamline a complex data estate, or build and share data products to business, Keboola’s AI orchestration platform adapts to your needs – freeing you to focus on innovation and business growth. **Average Rating:** 4.6/5.0 **Total Reviews:** 133 **How Do G2 Users Rate Keboola?** - **Data Observability:** 8.4/10 (Category avg: 9.0/10) - **Testing capabilities:** 7.7/10 (Category avg: 8.6/10) - **Ease of Use:** 8.6/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Keboola?** - **Seller:** [Keboola](https://www.g2.com/sellers/keboola) - **Company Website:** https://www.keboola.com - **Year Founded:** 2008 - **HQ Location:** Prague - **Twitter:** @keboola (2,004 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/keboola/ (97 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Data Engineer - **Top Industries:** Information Technology and Services, Marketing and Advertising - **Company Size:** 64% Mid-Market, 21% Small-Business #### What Are Keboola's Pros and Cons? **Pros:** - Ease of Use (35 reviews) - Features (27 reviews) - Data Management (26 reviews) - Integrations (26 reviews) - Customer Support (25 reviews) **Cons:** - Learning Curve (14 reviews) - Complexity (13 reviews) - Steep Learning Curve (11 reviews) - Data Management (9 reviews) - UX Improvement (9 reviews) ### What Do G2 Reviewers Say About Keboola? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** in Keboola, making data management accessible for less technical users. - Users value the **user-friendly data management** features of Keboola, enhancing accessibility for non-technical individuals. - Users appreciate the **intuitive setup and seamless data flow management** of Keboola, enhancing both efficiency and productivity. - Users appreciate the **numerous integrations** offered by Keboola, enhancing data management for both technical and non-technical users. - Users praise Keboola's **incredible customer support** , noting their fast and helpful assistance for any queries or issues. **Cons:** - Users find the **learning curve steep** , often needing developer assistance to navigate Keboola's complex components and settings. - Users find the **complexity** of Keboola challenging due to the steep learning curve and sometimes unclear documentation. - Users face a **steep learning curve** with Keboola, often needing developer assistance to navigate complex setups. - Users find the **interface challenging** , struggling with data management and lacking adequate support for errors and standardization. - Users find the **user interface challenging** , noting a steep learning curve and overwhelming data management experience. #### What Are Recent G2 Reviews of Keboola? **"[Streamlines Data Prep with Some Learning Curves](https://www.g2.com/survey_responses/keboola-review-9741142)"** **Rating:** 5.0/5.0 stars *— Vojta F.* [Read full review](https://www.g2.com/survey_responses/keboola-review-9741142) --- **"[Effortless Data Management with Stellar Support](https://www.g2.com/survey_responses/keboola-review-11930748)"** **Rating:** 4.5/5.0 stars *— Pavel S.* [Read full review](https://www.g2.com/survey_responses/keboola-review-11930748) --- #### What Are G2 Users Discussing About Keboola? - [What are the benefits and challenges of using Keboola for data integration, and what do you recommend for new users?](https://www.g2.com/discussions/what-are-the-benefits-and-challenges-of-using-keboola-for-data-integration-and-what-do-you-recommend-for-new-users) ### 21. [IBM StreamSets](https://www.g2.com/products/ibm-streamsets/reviews) IBM StreamSets is a robust streaming data integration tool for hybrid, multi-cloud environments that enables real-time decision making. It allows ingestion and in-flight transformation of structured, unstructured, and semi-structured data from streaming sources, and reliably delivers trusted data into diverse destinations. Flexible deployment options promote security, cost-effectiveness and performance. With several pre-built connectors, an intuitive no-code/low-code interface, and automatic adaptability to data drifts, StreamSets accelerates data pipeline operationalization. It integrates with IBM’s broader data integration capabilities, enabling reliable pipelines that unify multiple data integration patterns, underpinned by data observability capabilities for continuous data quality monitoring and remediation. That’s why the largest companies in the world trust StreamSets to power millions of data pipelines for modern analytics, data science, smart applications, and hybrid integration. **Average Rating:** 4.0/5.0 **Total Reviews:** 115 **How Do G2 Users Rate IBM StreamSets?** - **Data Observability:** 6.7/10 (Category avg: 9.0/10) - **Testing capabilities:** 7.1/10 (Category avg: 8.6/10) - **Ease of Use:** 8.4/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind IBM StreamSets?** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, New York, United States - **Twitter:** @IBMSecurity (74,660 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®) - **Ownership:** SWX:IBM **Who Uses This Product?** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 42% Enterprise, 33% Mid-Market #### What Are IBM StreamSets's Pros and Cons? **Pros:** - Ease of Use (30 reviews) - User Interface (16 reviews) - Data Management (15 reviews) - Data Pipelining (15 reviews) - Integrations (14 reviews) **Cons:** - Learning Curve (13 reviews) - Expensive (10 reviews) - Learning Difficulty (8 reviews) - Slow Performance (8 reviews) - Steep Learning Curve (8 reviews) ### What Do G2 Reviewers Say About IBM StreamSets? *AI-generated summary from verified user reviews* **Pros:** - Users value the **intuitive interface** of IBM StreamSets, making data pipeline creation and monitoring effortless and user-friendly. - Users appreciate the **user-friendly drag-and-drop interface** of IBM StreamSets, enhancing pipeline visualization and debugging efficiency. - Users appreciate the **effective data management** capabilities of IBM StreamSets for seamless pipeline creation and monitoring. - Users appreciate the **intuitive visual pipeline design** of IBM StreamSets, which simplifies data integration workflows for everyone. - Users value the **extensive integrations** of IBM StreamSets, enhancing data workflows across cloud and on-premise systems. **Cons:** - Users find the **steep learning curve** challenging, requiring deep technical knowledge to utilize advanced features effectively. - Users find the **product expensive** , especially for smaller teams, making it less competitive compared to alternatives. - Users find the **learning difficulty** to be challenging, especially with advanced features requiring significant time and effort. - Users experience **slow performance** with IBM StreamSets, especially when managing large datasets or complex pipelines. - Users find the **steep learning curve** challenging, requiring extensive technical knowledge for advanced features and configurations. #### What Are Recent G2 Reviews of IBM StreamSets? **"[Powerful Data Integration With IBM Stream sets.](https://www.g2.com/survey_responses/ibm-streamsets-review-11654909)"** **Rating:** 5.0/5.0 stars *— Abhishek D.* [Read full review](https://www.g2.com/survey_responses/ibm-streamsets-review-11654909) --- **"[Simplifies Real-Time Data Pipelines with Mixed Customization](https://www.g2.com/survey_responses/ibm-streamsets-review-12240946)"** **Rating:** 4.0/5.0 stars *— Sravya A.* [Read full review](https://www.g2.com/survey_responses/ibm-streamsets-review-12240946) --- #### What Are G2 Users Discussing About IBM StreamSets? - [What is StreamSets used for?](https://www.g2.com/discussions/what-is-streamsets-used-for) - [What is StreamSets data collector?](https://www.g2.com/discussions/what-is-streamsets-data-collector) - [What is StreamSets control hub?](https://www.g2.com/discussions/what-is-streamsets-control-hub) - [Are StreamSets free?](https://www.g2.com/discussions/are-streamsets-free) - [What is StreamSets tool?](https://www.g2.com/discussions/what-is-streamsets-tool) - 1 comment ### 22. [Acceldata](https://www.g2.com/products/acceldata/reviews) Acceldata is a pioneering provider of enterprise solutions in data observability and Agentic Data Management. Its technology enables organizations to monitor, manage, and improve the reliability, quality, and performance of data systems across cloud, hybrid, and on-prem environments. Building on its foundation in data observability, Acceldata developed an Agentic Data Management platform that applies AI agents to autonomously detect, analyze, and resolve issues across the data lifecycle. This approach brings together observability, governance, and optimization into a unified system, allowing data environments to self-monitor, self-heal, and adapt over time. By moving from manual, reactive operations to more intelligent, automated processes, Acceldata supports scalable, efficient, and context-aware data management across the enterprise. Core Features of Acceldata’s Agentic Data Management Platform 1. Autonomous AI Agents: Acceldata deploys over 10 specialized AI agents designed to manage core data functions such as data quality, lineage, profiling, governance, pipeline health, and cost optimization. These agents continuously scan systems, detect issues, reason about their cause, and either take direct action or escalate with human oversight. They collaborate to improve data reliability, reduce downtime, and drive informed decision-making. 2. xLake Reasoning Engine: At the core of the platform is the xLake Reasoning Engine—a high-scale, AI-aware engine built to handle exabytes of data. It executes across hybrid and multi-cloud environments, translating business rules into intelligent data actions. xLake enables context-aware processing and powers the agents’ ability to reason across telemetry, metadata, and historical trends. 3. Contextual Memory and Learning: Agents don’t operate in isolation. They remember past patterns, recall prior actions, and improve over time using contextual memory. This learning ability allows agents to adapt policies, refine thresholds, and prevent repeat incidents, making pipelines and systems progressively smarter and more resilient. 4. Natural Language Interface – The Business Notebook: Acceldata features a conversational interface called the Business Notebook. This AI-powered workspace allows business users and technical teams to interact with data in natural language. It explains agent actions, visualizes lineage, and empowers non-technical users to ask questions, make decisions, and access insights without needing SQL or scripting knowledge. 5. Real-Time Data Observability and Self-Healing: The platform goes beyond traditional monitoring by offering agentic observability. It autonomously scans data systems for anomalies, schema drift, freshness decay, and operational failures. Once detected, agents not only alert but also remediate issues in real time—ensuring continuous data reliability and pipeline health. 6. Policy-Driven Governance and Compliance: Acceldata embeds governance into the fabric of your data workflows. With policy agents, organizations can define and enforce access controls, data protection rules, audit logging, and compliance policies like GDPR, HIPAA, and BCBS 239—all without manual configuration. These policies evolve automatically using machine learning and agent feedback loops. 7. Unified Data Discovery and Classification: The Discovery engine continuously scans across cloud platforms, data lakes, and warehouses to classify, tag, and map data assets. It auto-generates lineage maps, enriches assets with context (e.g., usage, sensitivity), and supports plain-language search. This eliminates the need for separate data catalogs and makes every dataset AI-ready. 8. Agent Studio for Custom Agent Creation: With Agent Studio, organizations can build and deploy their own AI agents tailored to their business needs. Whether it’s a vertical-specific data rule, a proprietary policy, or a unique remediation workflow, Agent Studio offers the flexibility to extend the platform’s capabilities and orchestrate multi-agent workflows. **Average Rating:** 4.4/5.0 **Total Reviews:** 55 **How Do G2 Users Rate Acceldata?** - **Data Observability:** 9.5/10 (Category avg: 9.0/10) - **Testing capabilities:** 7.6/10 (Category avg: 8.6/10) - **Ease of Use:** 8.5/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Acceldata?** - **Seller:** [Acceldata](https://www.g2.com/sellers/acceldata) - **Company Website:** https://www.acceldata.io/ - **Year Founded:** 2018 - **HQ Location:** Campbell, CA - **Twitter:** @acceldataio (340 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/acceldata (299 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 61% Enterprise, 23% Mid-Market #### What Are Acceldata's Pros and Cons? **Pros:** - Ease of Use (18 reviews) - Customer Support (15 reviews) - Efficiency Improvement (13 reviews) - Features (13 reviews) - Monitoring (13 reviews) **Cons:** - UX Improvement (9 reviews) - Complex Setup (6 reviews) - Difficult Setup (6 reviews) - Learning Curve (6 reviews) - Learning Difficulty (6 reviews) ### What Do G2 Reviewers Say About Acceldata? *AI-generated summary from verified user reviews* **Pros:** - Users praise Acceldata for its **ease of use** , enhancing productivity with a user-friendly interface and simple integration. - Users commend the **exceptional customer support** of Acceldata, appreciating the prompt and effective assistance provided by their team. - Users commend the **efficiency improvement** from Acceldata, enhancing data visibility and enabling swift decision-making with minimal effort. - Users value the **efficient onboarding and strong technology stack** of Acceldata, enhancing data management and strategy. - Users appreciate the **seamless monitoring capabilities** of Acceldata, enhancing data management and operational efficiency significantly. **Cons:** - Users find the **UX improvement** necessary due to inconsistencies and slow response times affecting overall experience. - Users find the **complex setup** challenging, with a steep learning curve and insufficient documentation for guidance. - Users find the **difficult setup** of Acceldata challenging, with a steep learning curve and room for improvement in documentation. - Users find the **learning curve steep** , particularly during initial setup and while navigating the documentation. - Users find the **learning difficulty** of Acceldata challenging, especially when creating systems from scratch. #### What Are Recent G2 Reviews of Acceldata? **"[Versatile Data Observability with Brilliant Alerting and Monitoring](https://www.g2.com/survey_responses/acceldata-review-12949346)"** **Rating:** 4.5/5.0 stars *— Luciana S.* [Read full review](https://www.g2.com/survey_responses/acceldata-review-12949346) --- **"[Enterprise Data Reliability and Observability Platform with Strong Customization](https://www.g2.com/survey_responses/acceldata-review-12122073)"** **Rating:** 5.0/5.0 stars *— Jayakumar S.* [Read full review](https://www.g2.com/survey_responses/acceldata-review-12122073) --- ### 23. [QuerySurge](https://www.g2.com/products/querysurge/reviews) QuerySurge is an enterprise-grade data quality platform that leverages AI to continuously automate data validation across your entire ecosystem ‐ from data warehouses and big data lakes to BI reports and enterprise applications. With AI-powered test creation, scalable architecture, and the leading DevOps for Data CI/CD integration, QuerySurge ensures data integrity at every stage of the pipeline. Automated Data Validation Use Cases: QuerySurge provides a smart, AI-driven, data validation & ETL testing solution for your automated testing needs. - Data Warehouse / ETL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - Business Intelligence (BI) Report Testing - Big Data Testing - Enterprise Application Data Testing What QuerySurge Provides: - Automation of your manual data validation and testing process - Ease-of-use, low-code/no-code features - Generative AI capabilities for test creation - Testing across 200+ data platforms - Integration into your CI/CD DataOps pipeline - Acceleration of your data analysis - Ensurance of regulatory compliance Key Features: - Data Connection Wizard provides an easy way to link to your data stores - Visual Query Wizard builds table-to-table and column-to-column tests without writing SQL - Generative AI module automatically creates transformation tests in bulk - DevOps for Data provides a RESTful API with 110+ calls and Swagger documentation and integrates into CI/CD pipelines - Create Custom Tests and modularize functions with snippets, set thresholds, stage data, check data types & duplicate rows, full text search, and asset tagging - Schedule tests to run immediately, at a predetermined date & time, or after any event from a build/release, CI/CD, DevOps, or test management solution - Multi-project support in a single instance, new Global Admin user, assign users and agents, import and export projects, and user activity log reports - Webhooks provide real-time integrations with DevOps, CI/CD, test management, and alerting tools - Ready-for-Analytics provides seamless integration with QuerySurge and your BI tool or open-source Metabase to create custom reports and dashboards and gain deeper, real-time insights into your data validation and ETL testing workflows - Data Analytics Dashboards and Data Intelligence Reports track, analyze, and communicate data quality **Average Rating:** 4.6/5.0 **Total Reviews:** 35 **How Do G2 Users Rate QuerySurge?** - **Data Observability:** 9.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 8.7/10 (Category avg: 8.6/10) - **Ease of Use:** 8.7/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 5.8/10 (Category avg: 10/10) **Who Is the Company Behind QuerySurge?** - **Seller:** [QuerySurge](https://www.g2.com/sellers/querysurge) - **Company Website:** https://www.querysurge.com - **Year Founded:** 2012 - **HQ Location:** New York, US - **Twitter:** @QuerySurge (6,537 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/querysurge/ (7 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 43% Enterprise, 30% Mid-Market #### What Are QuerySurge's Pros and Cons? **Pros:** - Ease of Use (17 reviews) - Features (12 reviews) - Automation (8 reviews) - Capabilities (8 reviews) - Easy Setup (8 reviews) **Cons:** - Limited Functionality (5 reviews) - Missing Features (5 reviews) - Inaccuracy Issues (4 reviews) - Slow Performance (4 reviews) - Complex Setup (3 reviews) ### What Do G2 Reviewers Say About QuerySurge? *AI-generated summary from verified user reviews* **Pros:** - Users find QuerySurge's **ease of use** enhances productivity, making data testing and query creation efficient and intuitive. - Users highlight the **AI-driven test creation** and intuitive interface of QuerySurge, enhancing data testing efficiency significantly. - Users praise the **automation capabilities** of QuerySurge, enabling efficient testing and significant time savings for complex data validations. - Users value the **powerful automated data testing capabilities** of QuerySurge, enhancing efficiency in validation and reporting. - Users benefit from the **easy setup** of QuerySurge, streamlining the onboarding process and enhancing team productivity. **Cons:** - Users criticize the **limited functionality** of QuerySurge, particularly its lack of support for JSON and REST API data sources. - Users note the **absence of JSON and REST API support** in QuerySurge, complicating data validation processes. - Users face **inaccuracy issues** with QuerySurge, as mismatched data leads to confusion in reported failure counts. - Users often experience **slow performance** with QuerySurge, especially during operations on large projects and data staging. - Users find the **complex setup** of QuerySurge daunting, particularly for beginners and large database projects. #### What Are Recent G2 Reviews of QuerySurge? **"[Useful for ETL data validation and saves manual effort](https://www.g2.com/survey_responses/querysurge-review-12692896)"** **Rating:** 5.0/5.0 stars *— Ashitosh S.* [Read full review](https://www.g2.com/survey_responses/querysurge-review-12692896) --- **"[QuerySurge Streamlines ETL Data Validation with Efficient SQL-Based Automation](https://www.g2.com/survey_responses/querysurge-review-12931112)"** **Rating:** 5.0/5.0 stars *— Kavyasri P.* [Read full review](https://www.g2.com/survey_responses/querysurge-review-12931112) --- ### 24. [Datacoves](https://www.g2.com/products/datacoves/reviews) Datacoves is an enterprise DataOps platform with managed dbt Core and Airflow for data transformation and orchestration. We offer VS Code in the browser for dbt development with the ability to include preferred VS Code extensions and Python libraries such as the official Snowflake Extension and Snowpark. You may also optionally use our managed Airbyte and Superset for a full end-to-end solution. **Average Rating:** 4.8/5.0 **Total Reviews:** 18 **How Do G2 Users Rate Datacoves?** - **Data Observability:** 9.0/10 (Category avg: 9.0/10) - **Testing capabilities:** 9.4/10 (Category avg: 8.6/10) - **Ease of Use:** 9.0/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Datacoves?** - **Seller:** [Datacoves Inc](https://www.g2.com/sellers/datacoves-inc) - **Year Founded:** 2021 - **HQ Location:** Thousand Oaks, California - **Twitter:** @datacoves (475 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/datacoves/ (12 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 44% Enterprise, 28% Small-Business #### What Are Datacoves's Pros and Cons? **Pros:** - Ease of Use (6 reviews) - Features (5 reviews) - Integrations (5 reviews) - Customer Support (4 reviews) - Data Engineering (4 reviews) **Cons:** - Poor Customer Support (2 reviews) - Alert Overload (1 reviews) - Dashboard Issues (1 reviews) - Data Limitations (1 reviews) - Dependency Issues (1 reviews) ### What Do G2 Reviewers Say About Datacoves? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Datacoves, simplifying setup while allowing for customization and flexibility. - Users appreciate the **well-thought-out development and deployment system** of Datacoves, simplifying data engineering processes significantly. - Users value the **seamless integrations** of Datacoves, enhancing collaboration and streamlining workflows across teams. - Users commend Datacoves for its **exceptional customer support** , highlighting quick responses and valuable technical expertise throughout implementation. - Users enjoy the **top-notch data engineering tools** in Datacoves, enhancing collaboration and data quality effortlessly. **Cons:** - Users report **poor customer support** , emphasizing the need for better monitoring tools to alleviate service issues. - Users feel that **alert overload** can hinder effective monitoring and lead to unnecessary strain on customer support. - Users express the need for **improved dashboard features** to enhance monitoring and prevent unnecessary support overload. - Some users may experience **frustration with tool limitations** when using Datacoves for their ELT processes. - While some users may face **dependency issues** with ELT tools, they can work well if well-suited. #### What Are Recent G2 Reviews of Datacoves? **"[Great Developer Experience with Responsive Support](https://www.g2.com/survey_responses/datacoves-review-12882735)"** **Rating:** 5.0/5.0 stars *— Anthony L.* [Read full review](https://www.g2.com/survey_responses/datacoves-review-12882735) --- **"[Datacoves Simplifies Our Data Stack with a Smooth, Customizable Dev Experience](https://www.g2.com/survey_responses/datacoves-review-12582185)"** **Rating:** 5.0/5.0 stars *— Sung Hoon J.* [Read full review](https://www.g2.com/survey_responses/datacoves-review-12582185) --- ### 25. [Datafold](https://www.g2.com/products/datafold/reviews) Datafold is a data observability platform that helps companies prevent data catastrophes. It has a unique ability to identify, prioritize and investigate data quality issues proactively before they affect production. Datafold’s proactive approach to data quality helps data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. **Average Rating:** 4.5/5.0 **Total Reviews:** 24 **How Do G2 Users Rate Datafold?** - **Data Observability:** 8.2/10 (Category avg: 9.0/10) - **Testing capabilities:** 9.3/10 (Category avg: 8.6/10) - **Ease of Use:** 8.8/10 (Category avg: 9.0/10) - **What is your organization's estimated ROI on the product (payback period in months)?:** 10/10 (Category avg: 10/10) **Who Is the Company Behind Datafold?** - **Seller:** [Datafold](https://www.g2.com/sellers/datafold) - **Year Founded:** 2020 - **HQ Location:** New York, US - **Twitter:** @datafoldcom (1,107 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/datafold/ (32 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Information Technology and Services - **Company Size:** 54% Mid-Market, 29% Small-Business #### What Are Recent G2 Reviews of Datafold? **"[Monitor your data pipelines using Datafold](https://www.g2.com/survey_responses/datafold-review-7914705)"** **Rating:** 5.0/5.0 stars *— Kavyansh P.* [Read full review](https://www.g2.com/survey_responses/datafold-review-7914705) --- **"[Great platform for improving data quality](https://www.g2.com/survey_responses/datafold-review-7966056)"** **Rating:** 5.0/5.0 stars *— Sanjay D.* [Read full review](https://www.g2.com/survey_responses/datafold-review-7966056) --- #### What Are G2 Users Discussing About Datafold? - [What is Datafold used for?](https://www.g2.com/discussions/what-is-datafold-used-for) ## What Is DataOps Platforms? [IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure) ## What Software Categories Are Similar to DataOps Platforms? - [Data Quality Tools](https://www.g2.com/categories/data-quality) - [ETL Tools](https://www.g2.com/categories/etl-tools) - [Data Observability Software](https://www.g2.com/categories/data-observability)