# Best Enterprise DataOps Platforms *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)* Products classified in the overall DataOps Platforms category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business DataOps Platforms to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business DataOps Platforms category. In addition to qualifying for inclusion in the DataOps Platforms category, to qualify for inclusion in the Enterprise Business DataOps Platforms category, a product must have at least 10 reviews left by a reviewer from an enterprise business. ## Category Overview **Total Products under this Category:** 99 ## Trust & Credibility Stats **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 4,400+ Authentic Reviews - 99+ 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. ## Top-Rated Products (Ranked by G2 Score) ### 1. [Databricks](https://www.g2.com/products/databricks/reviews) Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and over 60% of the Fortune 500 — rely on Databricks 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 Data Intelligence Platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase and Unity Catalog. **Average Rating:** 4.6/5.0 **Total Reviews:** 731 **User Satisfaction Scores:** - **Data Observability:** 8.6/10 (Category avg: 8.9/10) - **Testing capabilities:** 8.6/10 (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) **Seller Details:** - **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 (89,652 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (14,779 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 44% Enterprise, 40% Mid-Market #### Pros & 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) ### 2. [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:** 103 **User Satisfaction Scores:** - **Data Observability:** 8.2/10 (Category avg: 8.9/10) - **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) **Seller Details:** - **Seller:** [ServiceNow](https://www.g2.com/sellers/servicenow) - **Company Website:** https://www.servicenow.com/ - **Year Founded:** 2004 - **HQ Location:** Santa Clara, CA - **Twitter:** @servicenow (54,113 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/29352/ (32,701 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 44% Enterprise, 30% Mid-Market #### Pros & 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) ### 3. [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) Monte Carlo, the data + AI observability leader, enables enterprise organizations to drive mission-critical initiatives with trusted foundations. Nasdaq, Honeywell, Roche, and hundreds of leading organizations depend on Monte Carlo's end-to-end platform to easily detect and resolve data + AI issues at scale. Offering thoughtfully automated workflows, intuitive collaboration tools and first-of-their-kind Observability Agents for monitoring and resolution, Monte Carlo extends it's powerful platform into every layer of the data + AI estate—data, system, code, and model—to help teams detect issues immediately, resolve them quickly, and scale coverage faster. Consistently ranked #1 in its category, Monte Carlo sets the industry standard for data + AI reliability, helping enterprise teams everywhere to reduce risk, accelerate innovation, and drive more value from their data + AI products. **Average Rating:** 4.3/5.0 **Total Reviews:** 498 **User Satisfaction Scores:** - **Data Observability:** 9.2/10 (Category avg: 8.9/10) - **Testing capabilities:** 7.7/10 (Category avg: 8.6/10) - **Ease of Use:** 8.2/10 (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) **Seller Details:** - **Seller:** [Monte Carlo](https://www.g2.com/sellers/monte-carlo) - **Company Website:** https://www.montecarlodata.com/ - **HQ Location:** San Francisco, US - **Twitter:** @montecarlodata (1,576 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/monte-carlo-data/ (576 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Financial Services, Computer Software - **Company Size:** 49% Enterprise, 43% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (112 reviews) - Alerts (107 reviews) - Monitoring (97 reviews) - Alerting System (78 reviews) - Data Quality (53 reviews) **Cons:** - Alert Management (68 reviews) - Alert Overload (62 reviews) - Inefficient Alert System (53 reviews) - UX Improvement (49 reviews) - Limited Functionality (44 reviews) ### 4. [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 **User Satisfaction Scores:** - **Data Observability:** 6.7/10 (Category avg: 8.9/10) - **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) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (709,023 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 42% Enterprise, 33% Mid-Market #### Pros & 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) ### 5. [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 **User Satisfaction Scores:** - **Data Observability:** 8.2/10 (Category avg: 8.9/10) - **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) **Seller Details:** - **Seller:** [Astronomer](https://www.g2.com/sellers/astronomer) - **Company Website:** https://www.astronomer.io/ - **Year Founded:** 2018 - **HQ Location:** New York, US - **Twitter:** @astronomerio (19,776 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10019299 (4,630 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 47% Mid-Market, 38% Enterprise #### Pros & 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) ### 6. [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:** 124 **User Satisfaction Scores:** - **Data Observability:** 8.5/10 (Category avg: 8.9/10) - **Testing capabilities:** 7.4/10 (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) **Seller Details:** - **Seller:** [Atlan](https://www.g2.com/sellers/atlan) - **Company Website:** https://www.atlan.com - **Year Founded:** 2019 - **HQ Location:** New York, US - **Twitter:** @AtlanHQ (9,720 Twitter followers) - **LinkedIn® Page:** https://in.linkedin.com/company/atlan-hq (580 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Financial Services, Computer Software - **Company Size:** 53% Mid-Market, 40% Enterprise #### Pros & Cons **Pros:** - Ease of Use (18 reviews) - User Interface (12 reviews) - Features (11 reviews) - Data Lineage (10 reviews) - Easy Setup (10 reviews) **Cons:** - Learning Curve (5 reviews) - Limited Functionality (5 reviews) - User Interface Issues (5 reviews) - Difficult Learning (4 reviews) - Integration Issues (4 reviews) ### 7. [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:** 53 **User Satisfaction Scores:** - **Data Observability:** 9.5/10 (Category avg: 8.9/10) - **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) **Seller Details:** - **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 (271 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 61% Enterprise, 22% Mid-Market #### Pros & 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) ### 8. [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:** 199 **User Satisfaction Scores:** - **Data Observability:** 8.7/10 (Category avg: 8.9/10) - **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) **Seller Details:** - **Seller:** [Fivetran](https://www.g2.com/sellers/fivetran) - **Year Founded:** 2012 - **HQ Location:** Oakland, CA - **Twitter:** @fivetran (5,735 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/fivetran/ (1,738 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Analytics Engineer, Data Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 57% Mid-Market, 28% Small-Business #### Pros & Cons **Pros:** - Ease of Use (38 reviews) - Features (22 reviews) - Automation (19 reviews) - Transformation (17 reviews) - Integrations (15 reviews) **Cons:** - Limited Functionality (14 reviews) - Dependency Issues (12 reviews) - Steep Learning Curve (10 reviews) - Error Handling (9 reviews) - Error Reporting (9 reviews) ### 9. [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:** 392 **User Satisfaction Scores:** - **Data Observability:** 8.4/10 (Category avg: 8.9/10) - **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) **Seller Details:** - **Seller:** [Hightouch](https://www.g2.com/sellers/hightouch) - **Company Website:** https://hightouch.com/ - **Year Founded:** 2021 - **HQ Location:** San Francisco, US - **Twitter:** @HightouchData (2,896 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/hightouchio/ (477 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 63% Mid-Market, 25% Small-Business #### Pros & 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) ## Parent Category [IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure) ## Related Categories - [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)