Best Enterprise DataOps Platforms

How Many DataOps Platforms Products Does G2 Track?

Total Products under this Category: 126

Category Stats (Nov 2026)

  • Average Rating: 4.58/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: DataBahn (+1.05%) - Among all products in this category, DataBahn recorded the largest rating increase compared to last month

Last updated: November 01, 2026

How Does G2 Rank DataOps Platforms Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 7,000+ Authentic Reviews
  • 126+ 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.

G2 Grid® for DataOps Platforms

G2 Grid® for DataOps Platforms plotting products by satisfaction and market presence

Highlighted products: Databricks, ServiceNow Workflow Data Fabric, Astro by Astronomer, Informatica Data Integration and Engineering, Fivetran, Monte Carlo, Atlan, and IBM StreamSets.

Underlying data: [Grid® JSON](https://www.g2.com/categories/dataops-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=servicenow-workflow-data-fabric&focus%5B%5D=astro-by-astronomer&focus%5B%5D=informatica-data-integration-and-engineering&focus%5B%5D=fivetran&focus%5B%5D=monte-carlo&focus%5B%5D=atlan&focus%5B%5D=ibm-streamsets&segment=enterprise)

Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, 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: 1,384

How Do G2 Users Rate Databricks?

  • Data Observability: 8.7/10 (Category avg: 8.9/10)
  • Testing capabilities: 8.7/10 (Category avg: 8.7/10)
  • 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.
  • Company Website:
  • Year Founded: 2013
  • HQ Location: San Francisco, CA
  • LinkedIn® Page: www.linkedin.com
    14,336 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 47% Large, 39% Medium

What Do G2 Reviewers Say About Databricks?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the seamless integration of big data and AI in Databricks, enhancing collaboration and performance.
  • Users appreciate the ease of use of Databricks, valuing its seamless integration and fast performance.
  • Users appreciate the seamless integration of Databricks with big data processing and AI, enhancing collaboration and performance.
  • Users value the seamless collaboration features of Databricks, enhancing teamwork and data analysis across departments.
  • Users appreciate the seamless integration of big data processing and AI in Databricks, enhancing collaboration and efficiency.
Cons
  • Users find the learning curve steep with Databricks, requiring time and skills to adapt effectively.
  • Users note that the cost can scale quickly, making cost monitoring and control a significant challenge.
  • Users find the steep learning curve of Databricks challenging, requiring considerable effort to adapt for business use cases.
  • Users find the inadequate ingestion options for non-standard data sources a limitation in Databricks' functionality.
  • Users find Databricks' complexity challenging, especially for beginners and smaller teams trying to fine-tune the system.

What Are Recent G2 Reviews of Databricks?

What Are G2 Users Discussing About Databricks?

ServiceNow Workflow Data Fabric

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, Workflow Data Fabric 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: 153

How Do G2 Users Rate ServiceNow Workflow Data Fabric?

  • Data Observability: 8.1/10 (Category avg: 8.9/10)
  • Testing capabilities: 8.0/10 (Category avg: 8.7/10)
  • Ease of Use: 8.1/10 (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
  • Company Website:
  • Year Founded: 2004
  • HQ Location: Santa Clara, CA
  • LinkedIn® Page: www.linkedin.com
    35,078 employees on LinkedIn®
  • Ownership: NYSE:NOW

Who Uses This Product?

  • Who Uses This: Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 46% Large, 28% Medium

What Do G2 Reviewers Say About ServiceNow Workflow Data Fabric?

AI-generated summary from verified user reviews

Pros
  • Users find ServiceNow's ease of use enhances workflow management, allowing seamless integration and automation with minimal effort.
  • Users value the seamless integrations of ServiceNow Workflow Data Fabric, enhancing collaboration and streamlining processes across departments.
  • Users appreciate the low-code automation of ServiceNow Workflow Data Fabric, simplifying integrations and enhancing workflow management.
  • Users value the efficiency improvement from ServiceNow Workflow Data Fabric, enhancing operations and reducing manual effort significantly.
  • Users value the unified data access of ServiceNow Workflow Data Fabric, enhancing efficiency and simplifying decision-making across workflows.
Cons
  • Users find the complex setup of ServiceNow Workflow Data Fabric challenging, requiring significant time and expertise for proper configuration.
  • Users find the difficult setup of ServiceNow Workflow Data Fabric time-consuming and challenging, impacting accessibility and integration.
  • Users find the pricing quite expensive, making it challenging for smaller teams to justify the costs.
  • Users experience slow performance when handling large datasets, impacting efficiency and responsiveness during data operations.
  • Users find the complexity of learning and managing features in ServiceNow Workflow Data Fabric challenging for new users.

What Are Recent G2 Reviews of ServiceNow Workflow Data Fabric?

Informatica Data Integration and Engineering

Cloud Data Integration (CDI) is a comprehensive, AI-powered ETL and ELT solution designed to easily move, transform, and synchronize data between various sources and target systems across any cloud, anywhere, at scale. Built on a modern technology stack, CDI offers easy, efficient and cost-effective data ingestion, transformation, synchronization, and replication for a multi-cloud and serverless world for everyone and everywhere. It covers diverse integration patterns, ensuring you have well-architected and seamlessly automated data pipelines for all your advanced analytics and AI needs.

Average Rating: 4.3/5.0

Total Reviews: 320

How Do G2 Users Rate Informatica Data Integration and Engineering?

  • Data Observability: 5.0/10 (Category avg: 8.9/10)
  • 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 Informatica Data Integration and Engineering?

  • Seller: Informatica
  • Company Website:
  • Year Founded: 1993
  • HQ Location: Redwood City, CA
  • LinkedIn® Page: www.linkedin.com
    2,473 employees on LinkedIn®
  • Ownership: NYSE: INFA

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 57% Large, 28% Medium

What Do G2 Reviewers Say About Informatica Data Integration and Engineering?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of Informatica Data Integration, making ETL tasks manageable for both beginners and experts.
  • Users appreciate the efficiency of Informatica Data Integration, benefiting from seamless connectivity and scalable data transfers.
  • Users appreciate the robust connectivity and ease of use of Informatica Data Integration for ETL tasks.
  • Users value the time-saving access of Informatica Data Integration, enabling efficient workflows without software installation.
  • Users appreciate the automation features of Informatica CDI, enhancing their data integration workflows efficiently.
Cons
  • Users find the complexity of setup and configuration challenging, especially for newcomers to the platform.
  • Users find the complex usability of Informatica Data Integration and Engineering challenging, especially for beginners.
  • Users find debugging difficult, as it often requires significant effort and complex configurations to resolve issues effectively.
  • Users find the difficult setup process of Informatica Data Integration and Engineering challenging, especially for newcomers to the platform.
  • Users struggle with unusual error messages in Informatica, often requiring tedious troubleshooting to resolve mapping issues.

What Are Recent G2 Reviews of Informatica Data Integration and Engineering?

What Are G2 Users Discussing About Informatica Data Integration and Engineering?

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Astro by Astronomer

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: 8.9/10)
  • Testing capabilities: 8.0/10 (Category avg: 8.7/10)
  • 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
  • Company Website:
  • Year Founded: 2018
  • HQ Location: New York, US
  • LinkedIn® Page: www.linkedin.com
    4,483 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% Medium, 38% Large

What Do G2 Reviewers Say About Astro by Astronomer?

AI-generated summary from verified user reviews

Pros
  • Users find Astro by Astronomer to be easy to use, enhancing productivity with seamless integrations and efficient task management.
  • Users value the efficiency improvement of Astro by Astronomer, enhancing productivity and simplifying Airflow management.
  • Users love the intuitive interface of Astro, making Airflow orchestration and management effortless and efficient.
  • Users value the automation capabilities of Astro by Astronomer, simplifying task management and enhancing workflow efficiency.
  • Users praise the deployment ease of Astro by Astronomer, simplifying Airflow management and enhancing productivity effortlessly.
Cons
  • Users find Astro by Astronomer to be expensive, which poses challenges for small teams and flexibility in setups.
  • Users face a steep learning curve with Astro, making initial adaptation and training time-consuming.
  • Users find the learning curve challenging, requiring substantial time for new users to adapt and train effectively.
  • Users find the steep learning curve of Astro challenging, requiring additional time and effort for new users.
  • Users find Astro by Astronomer has limited customization options, impacting flexibility and adaptability for diverse needs.

What Are Recent G2 Reviews of Astro by Astronomer?

What Are G2 Users Discussing About Astro by Astronomer?

Fivetran

Fivetran is the data foundation for AI. One platform moves, manages, and transforms data from every application, database, event stream, and file your business runs on into a governed foundation that analytics, operations, and AI can act on. Connectors deploy in minutes, run themselves, and adjust automatically when a source changes, so your data team spends its time building, not maintaining pipelines.

Average Rating: 4.3/5.0

Total Reviews: 839

How Do G2 Users Rate Fivetran?

  • Data Observability: 8.4/10 (Category avg: 8.9/10)
  • Testing capabilities: 8.7/10 (Category avg: 8.7/10)
  • 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 Fivetran?

  • Seller: Fivetran
  • Company Website:
  • Year Founded: 2012
  • HQ Location: Oakland, CA
  • LinkedIn® Page: www.linkedin.com
    1,902 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 59% Medium, 27% Small

What Do G2 Reviewers Say About Fivetran?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of Fivetran, praising its intuitive interface and simple setup processes.
  • Users value Fivetran's easy setup, allowing seamless integration without needing deep technical expertise.
  • Users highlight the easy integration of Fivetran, enabling quick setup and reliable connections across platforms.
  • Users highlight the responsive and helpful customer support of Fivetran, ensuring a smooth data integration experience.
  • Users appreciate the user-friendly interface of Fivetran, making data integration and connector setup seamless and intuitive.
Cons
  • Users experience sync issues with Fivetran, leading to delays and difficulties in data management for projects.
  • Users find Fivetran to be expensive, especially for startups and small businesses with high data volumes.
  • Users face integration issues with Fivetran, particularly with costly dependencies and missing connectors for certain data sources.
  • Users express concerns about high pricing issues with Fivetran, especially as data needs grow and scale.
  • Users note the steep learning curve with Fivetran, requiring technical skills for complex queries and overcoming initial confusion.

What Are Recent G2 Reviews of Fivetran?

What Are G2 Users Discussing About Fivetran?

Monte Carlo

Monte Carlo is the agent trust platform, trusted by Nasdaq, Cisco, PepsiCo, 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 thousands 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. Only Monte Carlo closes the full trust loop across both data and AI, and 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: 579

How Do G2 Users Rate Monte Carlo?

  • Data Observability: 9.2/10 (Category avg: 8.9/10)
  • Testing capabilities: 7.7/10 (Category avg: 8.7/10)
  • 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
  • Company Website:
  • Year Founded: 2019
  • HQ Location: San Francisco, US
  • LinkedIn® Page: www.linkedin.com
    550 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Senior Data Engineer
  • Top Industries: Financial Services, Computer Software
  • Company Size: 51% Large, 41% Medium

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, benefiting from intuitive tools and quick navigation features.
  • Users appreciate the reliable alerts from Monte Carlo, enhancing confidence in code quality and bug detection.
  • Users appreciate the reliable monitoring of Monte Carlo, which enhances data quality and swiftly identifies issues in pipelines.
  • Users value the automated alerting system in Monte Carlo for its quick detection of data issues, enhancing reliability.
  • Users appreciate the reliable data quality of Monte Carlo, enhancing confidence and simplifying bug detection in their code.
Cons
  • Users express frustration with ineffective alert management, citing poor communication and unresponsive ML threshold adjustments.
  • Users often feel overwhelmed by alert overload due to ineffective ML threshold adjustments, leading to numerous false errors.
  • Users experience an inefficient alert system with Monte Carlo, leading to missed updates and numerous false errors.
  • Users find the UX improvements lacking, particularly in feature accessibility and efficiency in database selections.
  • Users express frustration with Monte Carlo's limited functionality, citing lack of coding options and delayed alerts for metric monitors.

What Are Recent G2 Reviews of Monte Carlo?

What Are G2 Users Discussing About Monte Carlo?

Atlan

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: 135

How Do G2 Users Rate Atlan?

  • Data Observability: 8.5/10 (Category avg: 8.9/10)
  • Testing capabilities: 7.4/10 (Category avg: 8.7/10)
  • 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 Atlan?

  • Seller: Atlan
  • Company Website:
  • Year Founded: 2019
  • HQ Location: New York, US
  • LinkedIn® Page: in.linkedin.com
    508 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Financial Services, Information Technology and Services
  • Company Size: 51% Medium, 42% Large

What Do G2 Reviewers Say About Atlan?

AI-generated summary from verified user reviews

Pros
  • Users commend Atlan for its ease of use, facilitating seamless data discovery and collaboration across teams.
  • Users appreciate the intuitive user interface of Atlan, which enhances collaboration and makes data management effortless and enjoyable.
  • Users value Atlan's great data discovery and collaboration features, enhancing their ability to work seamlessly with data.
  • Users value the detailed lineage feature of Atlan for its effectiveness in tracking data origins and impact.
  • Users commend Atlan for its easy setup, simplifying data implementation and enhancing overall productivity effortlessly.
Cons
  • Users find the steep learning curve of Atlan challenging, making it tough for newcomers to maximize its value.
  • Users note the limited functionality of Atlan, specifically in automation and team task management features.
  • Users face user interface issues that hinder navigation, customization, and overall user-friendliness for business tasks.
  • Users experience a difficult learning curve with Atlan, making it challenging for newcomers to utilize its features effectively.
  • Users face integration issues with Teams, requiring more refined workflows and additional infrastructure for non-native databases.

What Are Recent G2 Reviews of Atlan?

IBM StreamSets

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: 113

How Do G2 Users Rate IBM StreamSets?

  • Data Observability: 6.7/10 (Category avg: 8.9/10)
  • Testing capabilities: 7.1/10 (Category avg: 8.7/10)
  • 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
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM
  • Phone: 1-866-277-7488

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 43% Large, 34% Medium

What Do G2 Reviewers Say About IBM StreamSets?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of IBM StreamSets, thanks to its intuitive design and low-code implementation.
  • Users appreciate the intuitive visual interface of IBM StreamSets, making data pipeline creation simple and efficient.
  • Users appreciate the simplified data integration workflows of IBM StreamSets, enhancing efficiency and productivity across teams.
  • Users find IBM StreamSets to excel in data pipelining, simplifying integration workflows with its intuitive design and reliable support.
  • Users appreciate the wide range of connectors IBM StreamSets offers, enhancing ease in data integration workflows.
Cons
  • Users note a steep learning curve for IBM StreamSets, requiring extensive training to master advanced features.
  • Users express concern over the high costs and licensing complexity associated with IBM StreamSets, affecting accessibility.
  • Users note the learning difficulty with advanced features on IBM StreamSets, requiring significant time and training.
  • Users report slow performance with UI, especially when handling complex pipelines, impacting overall usability.
  • Users find the platform has a steep learning curve, requiring significant time and training to utilize advanced features.

What Are Recent G2 Reviews of IBM StreamSets?

What Are G2 Users Discussing About IBM StreamSets?

dbt

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: 209

How Do G2 Users Rate dbt?

  • Data Observability: 8.7/10 (Category avg: 8.9/10)
  • Testing capabilities: 8.9/10 (Category avg: 8.7/10)
  • 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: Fivetran
  • Year Founded: 2012
  • HQ Location: Oakland, CA
  • LinkedIn® Page: www.linkedin.com
    1,902 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% Medium, 27% Small

What Do G2 Reviewers Say About dbt?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of dbt, benefiting from its simple setup and efficient data transformation processes.
  • Users value the ease of use and structured approach of dbt, enhancing collaboration and reliability in data transformation.
  • Users value the automation of data modeling in dbt, enhancing trust and accuracy in their analytics processes.
  • Users value the organized and predictable structure of dbt, enhancing their analytics workflow and fostering collaboration.
  • Users value the easy integrations of dbt, streamlining data workflows and enhancing collaboration without added delays.
Cons
  • Users often experience limited functionality due to issues like debugging complexities and rigid project structures, hindering progress.
  • Users face dependency issues with dbt, leading to inflexibility, time-consuming debugging, and increased project complexity.
  • Users find the steep learning curve of dbt challenging, complicating onboarding and slowing down project progression.
  • Users experience vague error messages that complicate debugging and troubleshooting in dbt, hindering their workflow efficiency.
  • Users find error reporting and troubleshooting difficult, often facing vague messages that hinder debugging and workflow efficiency.

What Are Recent G2 Reviews of dbt?

What Are G2 Users Discussing About dbt?

Acceldata

Acceldata is the Data and AI platform for Autonomous Enterprise. Large enterprises need uniform data access, compute capabilities and governance to run analytics and deploy agents across their hybrid infrastructure. But most enterprise data platforms were built on the assumption that data would eventually centralize into a single warehouse or lakehouse. In practice, large enterprises operate four or more data platforms simultaneously, with data distributed across cloud providers, on-premises systems, and regulated environments that cannot move data across borders. Acceldata addresses this by bringing compute to wherever data already resides, rather than forcing data to move to the compute engine. Acceldata supports every stage of the data lifecycle, on any platform, bringing compute to where the data lives and ensuring data trust by unifying data quality, governance and observability into an intelligent, AI-driven fabric. Trusted by Fortune 500 companies worldwide, Acceldata empowers businesses to unlock the full potential of their data in the AI era. Key capabilities include: 1. Data and AI Observability: End-to-end monitoring of data pipelines, data quality, AI agent behavior, and LLM outputs. Includes anomaly detection, data lineage, reconciliation, and alert management across environments. 2. Agentic Data Management (ADM): Deploys autonomous agents to automate data quality monitoring, pipeline operations, catalog management, and incident response across distributed data estates. 3. Agentic Data Engineering (ADE): Builds, orchestrates, and runs data pipelines using intelligent agents that automate pipeline creation, federated querying, and job management. 4. Data Warehousing: Executes queries in-place across lakehouses and warehouses using a Velox-accelerated engine, supporting open formats including Apache Iceberg, Delta Lake, Hudi, and Parquet with no vendor lock-in. 5. Data Platform Modernization: Provides phased migration paths for enterprises running Hadoop or Cloudera infrastructure, with in-place, sidecar, and forklift migration options built on an open-source foundation. Acceldata is designed for large enterprises in financial services, life sciences, telecommunications, manufacturing, retail, and insurance, particularly organizations operating regulated data environments where governance and data residency requirements prevent full cloud migration. The platform integrates with Snowflake, Databricks, AWS, Azure, GCP, and major open-source data frameworks.

Average Rating: 4.4/5.0

Total Reviews: 54

How Do G2 Users Rate Acceldata?

  • Data Observability: 9.5/10 (Category avg: 8.9/10)
  • Testing capabilities: 7.6/10 (Category avg: 8.7/10)
  • 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
  • Company Website:
  • Year Founded: 2018
  • HQ Location: Campbell, CA
  • LinkedIn® Page: www.linkedin.com
    330 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 62% Large, 22% Medium

What Do G2 Reviewers Say About Acceldata?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Acceldata, enabling efficient onboarding and seamless data management without complexity.
  • Users value the round-the-clock customer support from Acceldata, ensuring timely resolutions and continuous assistance.
  • Users value the efficiency improvement with Acceldata, enjoying seamless operations and effective real-time monitoring tools.
  • Users appreciate the unified platform for data reliability, enhancing operational efficiency and data health management at scale.
  • Users value the real-time monitoring capabilities of Acceldata, which enhance data observability and operational efficiency.
Cons
  • Users find the steep learning curve challenging for non-technical stakeholders, although UI improvements are on the way.
  • Users find the complex setup challenging, with difficult documentation and confusing data visualizations during initial use.
  • Users find the difficult setup of Acceldata challenging, especially when creating a system from scratch.
  • Users find a steep learning curve with Acceldata, particularly for non-technical stakeholders navigating its extensive features.
  • Users find a steep learning curve for Acceldata due to its extensive features, impacting usability for non-technical users.

What Are Recent G2 Reviews of Acceldata?

Coalesce

Coalesce is the data operating layer for analytics and AI. It unifies data transformation, cataloging, and quality in one governed platform. Coalesce Transform, Coalesce Catalog, and Coalesce Quality share one metadata layer, one set of definitions, and one governance model. • One system, not a stitched toolchain: Transform, Catalog, and Quality share one metadata layer, one set of definitions, and one governance model. • Govern every actor, human or AI: an agent follows the same standards, approvals, and lineage as an engineer in the IDE. • A purpose-built place to review AI's work: the visual graph, node inspection, and column-level lineage let humans verify agent-built pipelines before they ship, with an audit trail that separates agent from human work. • Request to production in one flow: no handoffs across tools and teams; governed by default. Coalesce Transform: Build, automate, and govern data transformations—with agents, within the UI, or directly through code. Engineers work in the visual builder, the IDE, or locally through Coalesce Desktop and the coa CLI. Every pipeline compiles to optimized SQL with column-level lineage, reusable Node Types and Packages, and validation on every change. Coalesce Catalog: Discover, document, and govern every data asset with AI. Catalog connects across your stack, documents assets with AI, and keeps lineage, ownership, and definitions current automatically—so people and AI agents work from the same understanding of the data. Coalesce Quality: Monitor, test, and validate data with observability built into the pipeline. Tests, rules, and anomaly monitors run as part of the pipeline. When something breaks, lineage-aware alerting shows what's affected, who owns it, and where the failure started. Built for humans and AI agents: Coalesce Copilot turns natural-language intent into governed transformations. Scout, an always-on data SRE, triages issues and proposes fixes for human review. Transform MCP, Catalog MCP, and Quality MCP let AI agents like Claude Code read metadata, traverse lineage, check quality, and make governed changes under the same standards as engineers. Coalesce never stores data at rest, encrypts data in motion, and runs production refreshes inside your data platform. Leading data teams use Coalesce to resolve incidents 5× faster, deliver migrations and projects 4× faster, and reclaim 50%+ of warehouse compute.

Average Rating: 4.7/5.0

Total Reviews: 118

How Do G2 Users Rate Coalesce?

  • Data Observability: 8.5/10 (Category avg: 8.9/10)
  • Testing capabilities: 8.2/10 (Category avg: 8.7/10)
  • 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 Coalesce?

  • Seller: Coalesce
  • Company Website:
  • Year Founded: 2020
  • HQ Location: San Francisco, CA
  • LinkedIn® Page: www.linkedin.com
    104 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Analytics Engineer
  • Top Industries: Computer Software, Financial Services
  • Company Size: 63% Medium, 23% Large

What Do G2 Reviewers Say About Coalesce?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Coalesce, finding it intuitive and accessible for all experience levels.
  • Users value the automation capabilities of Coalesce, which significantly streamline data transformation and save time.
  • Users commend the excellent customer support of Coalesce, highlighting its responsiveness and dedication to user needs.
  • Users value the intuitive UI of Coalesce, making it easy for everyone to use and implement quickly.
  • Users love Coalesce for its user-friendly interface and seamless integration with Snowflake, enhancing data transformation efficiency.
Cons
  • Users often face UI design issues that hinder navigation and usability, affecting their overall experience with Coalesce.
  • Users face integration issues with Coalesce, particularly with limited options and challenges in managing git functionalities.
  • Users find the learning curve challenging due to the lack of comprehensive online courses and deep dives on features.
  • Users find limitations in credential management and customization options, hindering their overall experience with Coalesce.
  • Users find the poor documentation lacks depth, making it challenging to understand features and best practices effectively.

What Are Recent G2 Reviews of Coalesce?

Hightouch

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: 424

How Do G2 Users Rate Hightouch?

  • Data Observability: 8.4/10 (Category avg: 8.9/10)
  • Testing capabilities: 8.1/10 (Category avg: 8.7/10)
  • 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
  • Company Website:
  • Year Founded: 2021
  • HQ Location: San Francisco, US
  • LinkedIn® Page: www.linkedin.com
    636 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 60% Medium, 27% Small

What Do G2 Reviewers Say About Hightouch?

AI-generated summary from verified user reviews

Pros
  • Users commend Hightouch for its ease of use, ensuring a smooth and intuitive setup experience for new users.
  • Users highlight the easy integration with Hightouch, appreciating its smooth setup and clear guidance for onboarding.
  • Users commend the responsive customer support of Hightouch, enhancing integration processes and addressing issues promptly.
  • Users appreciate the easy integrations of Hightouch, highlighting its smooth setup and intuitive user experience for developers.
  • Users find the easy setup of Hightouch intuitive, allowing rapid integration and smooth onboarding for effective data activation.
Cons
  • Users find Hightouch expensive, especially for larger organizations, leading to concerns about affordability and value.
  • Users express concerns about high pricing issues with Hightouch, particularly regarding affordability and unexpected changes to plans.
  • Users report integration issues with Hightouch, especially regarding sync times and advanced transformations requiring workarounds.
  • Users experience slow performance with Hightouch, particularly during larger data pulls, affecting usability significantly.
  • Users face syncing issues with prolonged job times and insufficient scheduling options affecting efficiency and effectiveness.

What Are Recent G2 Reviews of Hightouch?

What Are G2 Users Discussing About Hightouch?

Timextender

Timextender is a metadata-driven data platform that helps organizations build AI-ready data infrastructure up to 10x faster. The Timextender Data Platform unifies four modules, Data Integration, Data Enrichment, Data Quality, and Orchestration, that together automate the ingestion, governance, and delivery of data across the storage platform of your choice. Timextender is designed to help businesses manage their data infrastructure without extensive manual coding, giving data teams direct control over accuracy, governance, and the pace at which they can put trusted data to work.

Average Rating: 4.3/5.0

Total Reviews: 146

How Do G2 Users Rate Timextender?

  • 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 Timextender?

Who Uses This Product?

  • Who Uses This: Business Intelligence Consultant, Data Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 45% Medium, 33% Small

What Do G2 Reviewers Say About Timextender?

AI-generated summary from verified user reviews

Pros
  • Users find TimeXtender highly user-friendly, appreciating its intuitive design and supportive training resources for seamless integration.
  • Users value the exceptional customer support from TimeXtender, especially the friendly service desk and comprehensive training.
  • Users appreciate the automation features of TimeXtender, significantly saving time and ensuring consistent, high-quality data.
  • Users value the ease of use and fast implementation of TimeXtender, coupled with excellent customer support.
  • Users value the time-saving automation of TimeXtender, which simplifies data management and speeds up processes significantly.
Cons
  • Users face challenges with the learning curve and documentation, complicating onboarding and efficient use of the tool.
  • Users often struggle with unstructured documentation and a steep learning curve, complicating the onboarding of new team members.
  • Users find the poor documentation of timeXtender challenging, especially for new users seeking updated resources.
  • Users find the steep learning curve challenging, noting complexity without sufficient training and support for integration.
  • Users often face unclear error reporting, making it difficult to understand issues and leading to frustration.

What Are Recent G2 Reviews of Timextender?

What Are G2 Users Discussing About Timextender?

Nexla

Nexla is the data connectivity layer for enterprise AI, connecting agents and applications to data systems, business applications, APIs, documents, and other enterprise sources through 1,000+ connectors. Nexla powers real-time data and actions for agents over MCP, alongside Agentic Data Integration supporting high-volume ETL, CDC, streaming, API integration, and document ingestion. With Express.dev, teams build production data pipelines conversationally, and with MCP Studio, they create task-specific, governed MCP servers. A full enterprise interface lets teams of all skill levels configure, monitor, govern, and operate integrations at scale. Try Express at https://express.dev At the core of the platform is Helix, a context engine that continuously learns from metadata, schemas, telemetry, and documentation to give agents accurate context and keep pipelines reliable. Nexla processes more than 12 trillion records annually and meets SOC 2 Type II, ISO 27001, HIPAA, and GDPR standards. Nexla is trusted by Fortune 100 enterprises including DoorDash, LiveRamp, LinkedIn, and J&J. Why teams choose Nexla ✅ Connect agents to 1,000+ systems with real-time data and actions over MCP ✅ Use bidirectional connectors out of the box, or generate new ones on demand ✅ Build production pipelines conversationally with Express.dev ✅ Create task-specific, governed MCP servers with MCP Studio ✅ Run ETL, ELT, CDC, streaming, API integration, and document ingestion on one platform, in real time or batch ✅ Work with any data type: structured, semi-structured, and unstructured ✅ Ground agents in accurate context with Helix, on a metadata-driven architecture built to scale with AI workloads ✅ Automate data quality validation, monitoring, and alerts ✅ Govern access with role-based controls, full lineage, search, and tagging for auditability Nexla is a Gartner Cool Vendor and pairs perfectly with the technologies you rely on ✅ Cloud: AWS, Azure, Google Cloud, on-premises ✅ Data platforms: Snowflake, Databricks, BigQuery, Redshift, S3, Oracle, Kafka, MongoDB, Postgres, MySQL ✅ Applications: SAP, Salesforce, NetSuite, Workday, HubSpot, Marketo, Google Ads ✅ Catalogs: Alation, Collibra, data.world ✅ Vector databases and LLMs: Pinecone and major model providers ✅ Webhooks, email, FTP, and APIs And many more Differentiators & Awards 🏆 2022-2025 Highest Rating Gartner Peer Insights™ Voice of the Customer for Data Integration Tools 🏆 G2 Fall 2026 High Performer and Momentum Leader

Average Rating: 4.6/5.0

Total Reviews: 63

How Do G2 Users Rate Nexla?

  • Data Observability: 8.8/10 (Category avg: 8.9/10)
  • Testing capabilities: 8.5/10 (Category avg: 8.7/10)
  • 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
  • Company Website:
  • Year Founded: 2016
  • HQ Location: San Mateo, California
  • LinkedIn® Page: www.linkedin.com
    71 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Insurance
  • Company Size: 42% Medium, 33% Small

What Do G2 Reviewers Say About Nexla?

AI-generated summary from verified user reviews

Pros
  • Users love the ease of use of Nexla, praising its user-friendly interface and seamless integration capabilities.
  • Users value the effortless automation in Nexla, enabling streamlined workflows and efficient data management across teams.
  • Users value the user-friendly interface of Nexla, enabling effortless management of complex data pipelines and automated flows.
  • Users praise the easy integration of Nexla, enhancing accessibility and streamlining connections across various data sources.
  • Users appreciate the ease of deployment and integration with Nexla, enabling seamless data manipulation without coding.
Cons
  • Users find the learning difficulty of Nexla challenging, particularly due to its complex features and insufficient documentation.
  • Users report slow performance with UI and searches, especially during large data processing and multiple flows.
  • Users find the difficult learning curve of Nexla challenging, especially when navigating complex features and setup.
  • Users find the learning curve steep, especially with complex features requiring prior knowledge and better documentation.
  • Users find the poor documentation of Nexla limits their understanding of advanced features and complex configurations.

What Are Recent G2 Reviews of Nexla?

What Are G2 Users Discussing About Nexla?

Shalaka Joshi
SJ
Researched and written by Shalaka Joshi
Updated