Best Enterprise Data Observability Software

How Many Data Observability Software Products Does G2 Track?

Total Products under this Category: 74

Category Stats (Oct 2026)

  • Average Rating: 4.55/5 (↓0.01 vs Sep 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Sifflet (+0.35%) - Among all products in this category, Sifflet recorded the largest rating increase compared to last month

Last updated: October 19, 2026

How Does G2 Rank Data Observability Software Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 2,600+ Authentic Reviews
  • 74+ 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 Data Observability Software

G2 Grid® for Data Observability Software plotting products by satisfaction and market presence

Highlighted products: Monte Carlo, Acceldata, Metaplane, IBM Databand, Pantomath, Telmai, Unravel Data, and Soda.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-observability/grids.json?focus%5B%5D=monte-carlo&focus%5B%5D=acceldata&focus%5B%5D=metaplane&focus%5B%5D=ibm-databand&focus%5B%5D=pantomath&focus%5B%5D=telmai&focus%5B%5D=unravel-data&focus%5B%5D=soda&segment=enterprise)

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: 546

How Do G2 Users Rate Monte Carlo?

  • Ease of Admin: 8.5/10 (Category avg: 8.8/10)
  • Anomaly identification: 8.8/10 (Category avg: 8.8/10)
  • Automation: 8.2/10 (Category avg: 8.6/10)
  • Integrations: 8.2/10 (Category avg: 8.8/10)

Who Is the Company Behind Monte Carlo?

  • Seller: Monte Carlo
  • Company Website:
  • Year Founded: 2019
  • HQ Location: San Francisco, US
  • Twitter: @montecarlo_ai
    1,576 Twitter followers
  • 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: 50% Large, 41% Medium

What Do G2 Reviewers Say About Monte Carlo?

AI-generated summary from verified user reviews

Pros
  • Users value the intuitive interface of Monte Carlo, finding it easy to navigate and utilize effectively.
  • Users appreciate the custom alerts and integration with Teams, enhancing data monitoring and stakeholder communication efficiently.
  • Users value the effective monitoring of Monte Carlo, catching data issues early and enhancing stakeholder communication.
  • Users value the custom alerting features in Monte Carlo for efficiently monitoring and notifying stakeholders about data issues.
  • Users value the ease of setting up alerts and anomaly detection in Monte Carlo for monitoring data quality.
Cons
  • Users find the lack of manual threshold settings for alerts limiting, impacting customization for their specific needs.
  • Users experience alert overload due to noisy initial settings, prompting the need for sensitivity adjustments and muted alerts.
  • Users find the inefficient alert system problematic, with issues in notification messages and usability improvements needed.
  • Users find the UX improvement necessary due to slow performance and disorganized features leading to confusion.
  • Users find limited functionality in Monte Carlo, especially regarding custom metrics and alert threshold settings.

What Are Recent G2 Reviews of Monte Carlo?

What Are G2 Users Discussing About Monte Carlo?

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?

  • Ease of Admin: 8.6/10 (Category avg: 8.8/10)
  • Anomaly identification: 8.6/10 (Category avg: 8.8/10)
  • Automation: 8.2/10 (Category avg: 8.6/10)
  • Integrations: 8.3/10 (Category avg: 8.8/10)

Who Is the Company Behind Acceldata?

  • Seller: Acceldata
  • Company Website:
  • Year Founded: 2018
  • HQ Location: Campbell, CA
  • Twitter: @acceldataio
    340 Twitter followers
  • 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 find Acceldata to be easy to use, enhancing productivity with its user-friendly interface and seamless integration.
  • Users highly value the responsive customer support of Acceldata, enhancing their experience with prompt and effective assistance.
  • Users benefit from the efficiency improvement provided by Acceldata, ensuring reliable data and streamlined operations.
  • Users praise the efficiency and effectiveness of Acceldata, appreciating its quick onboarding and robust technology stack.
  • Users commend the effective monitoring capabilities of Acceldata, enhancing data management and proactive decision-making significantly.
Cons
  • Users feel the UX of Acceldata could be more intuitive, citing inconsistencies and slow response times as areas for improvement.
  • Users find the initial setup complex and believe improvements in documentation and visualization are needed.
  • Users find the difficult setup challenging, noting complications in initial configuration and lacking documentation.
  • Users find the steep learning curve and complex initial setup of Acceldata challenging and in need of improvement.
  • Users find the learning difficulty in setting up Acceldata challenging, noting a steep initial learning curve.

What Are Recent G2 Reviews of Acceldata?

Metaplane

Metaplane is the end-to-end data observability tool that helps data teams know when things break, what went wrong, and how to fix it. Built for the modern data stack, we empower data teams to start monitoring their data in minutes. We’re backed by world-class investors and advisors, including Y Combinator, Khosla Ventures, HubSpot, Clearbit, Okta, Flybridge, and Drift. Metaplane helps you build trust in your company's data, increase your team's awareness of the state of data quality, and ultimately save engineering time. Key features include anomaly detection, usage analytics, data lineage, and custom SQL tests. Integrations include Snowflake, Redshift, BigQuery, dbt, Looker, Mode, Metabase, Tableau, Postgres, MySQL, Slack, and PagerDuty.

Average Rating: 4.8/5.0

Total Reviews: 116

How Do G2 Users Rate Metaplane?

  • Ease of Admin: 9.3/10 (Category avg: 8.8/10)
  • Anomaly identification: 9.2/10 (Category avg: 8.8/10)
  • Automation: 9.1/10 (Category avg: 8.6/10)
  • Integrations: 9.0/10 (Category avg: 8.8/10)

Who Is the Company Behind Metaplane?

  • Seller: Datadog
  • Company Website:
  • Year Founded: 2010
  • HQ Location: New York
  • Twitter: @datadoghq
    51,207 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    10,780 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 66% Medium, 22% Small

What Do G2 Reviewers Say About Metaplane?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Metaplane, allowing for quick integration and effective anomaly detection.
  • Users value the easy integrations of Metaplane, enhancing their data monitoring and overall experience with seamless setup.
  • Users value effective monitoring with Metaplane, allowing proactive issue resolution and enhancing confidence in data reliability.
  • Users highlight the quick alerts feature of Metaplane, enhancing data issue resolution and monitoring efficiency.
  • Users value the proactive anomaly detection of Metaplane, enhancing data quality and saving significant time and effort.
Cons
  • Users find the limited customization options for complex rules somewhat restrictive, impacting their monitoring precision.
  • Users find that alert management can be overly sensitive, leading to noise and potential notifications fatigue.
  • Users experience inefficient alert systems in Metaplane, leading to noise and occasional overlooking of important notifications.
  • Users find the complex setup of Metaplane time-consuming, especially when fine-tuning monitors for specific datasets.
  • Users find the difficult setup of Metaplane time-consuming, especially when fine-tuning alerts for complex datasets.

What Are Recent G2 Reviews of Metaplane?

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IBM Databand

Detect and resolve your data issues faster than ever with Databand. The only continuous data observability platform that catches bad data before it impacts your business. Book a demo today --> https://www.ibm.com/account/reg/us-en/signup?formid=DEMO-dataaidataband CORE CAPABILITIES Incident Management Improve data reliability and quality under one roof with a single pane of glass for all your data incidents. Data Reliability Monitoring Monitor data pipeline errors such as failed runs, longer than expected durations, missing data operations, and unexpected schema changes. Data Quality Metrics Continuously validate data quality with dataset metrics for SLAs, column changes, and null records. Anomaly Detection Eliminate the unknown by seeing trends & detecting anomalies from your metadata in real time. DataOps Alerting and Routing Customize incident alerts and route notifications to impacted DataOps teams for faster resolution. End-to-end Lineage Visualize how data incidents impact upstream and downstream components of your data stack. Website www.ibm.com/products/databand

Average Rating: 4.3/5.0

Total Reviews: 65

How Do G2 Users Rate IBM Databand?

  • Ease of Admin: 8.0/10 (Category avg: 8.8/10)
  • Anomaly identification: 8.3/10 (Category avg: 8.8/10)
  • Automation: 8.2/10 (Category avg: 8.6/10)
  • Integrations: 8.6/10 (Category avg: 8.8/10)

Who Is the Company Behind IBM Databand?

  • Seller: IBM
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • Twitter: @IBMSecurity
    74,660 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Who Uses This: Software Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 44% Medium, 33% Large

What Do G2 Reviewers Say About IBM Databand?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the automated anomaly detection in IBM Databand, enhancing insights and monitoring capabilities effortlessly.
  • Users appreciate the end-to-end visibility offered by IBM Databand, simplifying ETL development and root cause analysis.
  • Users appreciate the automation features of IBM Databand, simplifying data management and enhancing workflow efficiency.
  • Users enjoy the dashboard customization in IBM Databand, facilitating ease of use and key metrics accessibility.
  • Users value the data management capabilities of IBM Databand, enabling efficient monitoring and error handling in data pipelines.
Cons
  • Users experience limited features, noting high pricing and a steep learning curve for new users.
  • Users find the pricing high for smaller teams, which can limit accessibility and overall value.
  • Users face integration issues with IBM Databand, finding limited compatibility with third-party services and complex navigation challenges.
  • Users face a steep learning curve with IBM Databand, making onboarding and navigation challenging for newcomers.
  • Users find missing features like self error resolution and limited integrations hinder their overall experience with IBM Databand.

What Are Recent G2 Reviews of IBM Databand?

Pantomath

Pantomath is pioneering the Data Operations Center (DOC), establishing a centralized, AI-driven platform necessary to manage data reliability as a strategic operational function. We are the first platform designed to continuously monitor, diagnose, and autonomously resolve data incidents across the entire cross-platform data ecosystem. Our approach transforms data reliability from a constant liability into an assured competitive advantage. Using purpose-built AI agents and a proprietary cross-platform interoperable data fabric, Pantomath automates the entire incident lifecycle: identifying the issue, pinpointing the single root cause, and executing immediate containment and mitigation. We empower organizations to move beyond costly reactive fixes, ensuring trustworthy data is delivered consistently and confidently to all stakeholders and consuming systems. Pantomath is designed for platform reliability teams, data engineers, and leaders responsible for data quality and SLAs. It supports critical use cases such as: - Detecting and resolving data incidents before stakeholders are affected - Unifying metadata, lineage, and job execution data for faster RCA - Automating resolution workflows and reducing mean time to acknowledge, detect, and resolve - Improving data trust across business teams by enabling transparency and accountability Key capabilities include: - Automated Discovery and Monitoring: Map and monitor pipelines, datasets, stored procedures, and dependencies across your stack. - AI-Powered RCA and Recommendations: Use built-in copilots to surface root cause and next steps in minutes. - Incident Correlation and Impact Analysis: Highlight downstream impact and notify the right teams in real time. - Autonomous Remediation: Self-heal pipelines through configurable automation policies. - Bring Your Own Catalog (BYOC): Integrate existing metadata tools to centralize data context. Pantomath gives enterprises a systemic, automated approach to data reliability - delivering trust, reducing noise, and empowering teams to scale data operations with confidence.

Average Rating: 4.7/5.0

Total Reviews: 15

How Do G2 Users Rate Pantomath?

  • Ease of Admin: 8.3/10 (Category avg: 8.8/10)
  • Anomaly identification: 8.5/10 (Category avg: 8.8/10)
  • Automation: 8.5/10 (Category avg: 8.6/10)
  • Integrations: 8.6/10 (Category avg: 8.8/10)

Who Is the Company Behind Pantomath?

  • Seller: Pantomath Inc.
  • Company Website:
  • Year Founded: 2022
  • HQ Location: Cincinnati
  • LinkedIn® Page: www.linkedin.com
    62 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Financial Services
  • Company Size: 73% Large, 27% Medium

What Do G2 Reviewers Say About Pantomath?

AI-generated summary from verified user reviews

Pros
  • Users value the outstanding data lineage visualization of Pantomath, enhancing efficiency in managing complex data operations.
  • Users appreciate the real-time monitoring of Pantomath, significantly enhancing efficiency in managing complex data operations.
  • Users appreciate the responsive customer support from Pantomath, ensuring a smooth setup and continuous improvements.
  • Users praise Pantomath for its exceptional efficiency improvements, enabling faster issue resolution and streamlined data operations.
  • Users commend Pantomath for its automation and real-time monitoring, significantly enhancing efficiency in managing complex data pipelines.
Cons
  • Users find the initial alert overwhelm from Pantomath challenging, though support helps refine the system over time.
  • Users find the poor documentation of Pantomath challenging for integration, despite prompt bug fixes from the team.
  • Users find the complex setup of Pantomath challenging, requiring time to adjust and manage configurations effectively.
  • Users find it difficult to learn Pantomath, especially if they lack prior experience with its features.
  • Users find the learning curve challenging, especially for those lacking prior experience with certain features.

What Are Recent G2 Reviews of Pantomath?

Telmai

Telmai is an AI-powered data observability platform that continuously monitors data across every stage of the pipeline—from ingestion to business applications. Designed for structured and semi-structured data, Telmai automatically detects anomalies, drifts, and data quality issues in real-time without sampling, ensuring reliable data for business intelligence, analytics, and AI workloads. Telmai's open architecture enables seamless data quality monitoring across the entire pipeline, integrating with over 250 systems, including data lakes, warehouses, streaming sources, and cloud storage. This provides deep insights into the health, accuracy, and consistency of data in complex environments. Telmai’s low-code interface empowers both business and technical teams to define custom metrics, automate remediation workflows, and ensure data is always actionable and reliable.

Average Rating: 4.9/5.0

Total Reviews: 22

How Do G2 Users Rate Telmai?

  • Ease of Admin: 8.8/10 (Category avg: 8.8/10)
  • Anomaly identification: 9.2/10 (Category avg: 8.8/10)
  • Automation: 9.0/10 (Category avg: 8.6/10)
  • Integrations: 9.0/10 (Category avg: 8.8/10)

Who Is the Company Behind Telmai?

  • Seller: Telmai
  • Year Founded: 2020
  • HQ Location: San Mateo , Ca
  • Twitter: @telmai1
    97 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    16 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software
  • Company Size: 55% Large, 32% Medium

What Do G2 Reviewers Say About Telmai?

AI-generated summary from verified user reviews

Pros
  • Users value the intuitive interface of Telmai, enabling both technical and business teams to manage data effortlessly.
  • Users value automation in Telmai, as it enhances data quality and minimizes manual intervention for efficiency.
  • Users value real-time anomaly detection in Telmai, enhancing data accuracy and trust across enterprise systems effortlessly.
  • Users commend Telmai for its proactive data quality monitoring, ensuring accuracy and minimizing manual interventions efficiently.
  • Users praise Telmai for its easy integrations, enhancing data observability and streamlining workflows in complex environments.
Cons
  • Users feel the basic error handling could be improved, with advanced support needed for a better experience.
  • Users note a significant learning difficulty with Telmai, requiring time to train and configure effectively.
  • Users find the limited functionality of Telmai restricts their ability to fully utilize the application.
  • Users find the UX improvement necessary, citing the challenging learning curve and need for better UI intuitiveness.
  • Users note that establishing an effective alert management system initially requires extra setup outside of Telmai.

What Are Recent G2 Reviews of Telmai?

Unravel Data

Unravel Data is an AI-powered data observability and FinOps platform that goes beyond just observing problems to empowering data teams to take immediate action for transformative results. Built to address the speed and scale of modern data platforms like Databricks, Snowflake, and BigQuery, Unravel’s AI-powered Insights Engine provides recommendations to make smarter decisions and optimize your cloud data analytics' performance, reliability, and efficiency. You get full-stack, 'workload-aware' contextual intelligence about your data applications and pipelines, with AI-driven recommendations on where and how to improve and optimize DataOps, analytics, and AI, helping you troubleshoot faster, meet SLAs, and keep budgets under control. With Unravel, data teams unlock business value from data more quickly and efficiently. Unravel leverages AI and automation to provide real-time, user-level spend reporting, code-level cost optimization tips, and automated spend controls designed to empower and unify DataOps and FinOps teams. With Unravel, data teams can monitor data flows through their pipelines, and detect code, configuration, and infrastructure issues. By correlating and analyzing the full stack of telemetry metadata, Unravel provides easy-to-understand insights, actionable recommendations, and automation to optimize performance and efficiency before you deploy into production. Unravel Data radically transforms the way businesses understand and optimize the performance and cost of their modern data applications – and the complex data pipelines that power those applications. Providing a unified view across the entire data stack, Unravel’s market-leading data observability platform leverages AI, machine learning, and advanced analytics to provide modern data teams with the actionable recommendations they need to turn data into insights. Recently recognized by CRN as a Cloud 100 Company for 2025 and named Best Data Tool & Platform of 2023 by the annual SIIA CODiE Awards, Unravel Data is trusted by some of the world’s most recognized brands, including Maersk, Mastercard, and Equifax to unlock data-driven insights and deliver new innovations to market.

Average Rating: 4.4/5.0

Total Reviews: 36

How Do G2 Users Rate Unravel Data?

  • Ease of Admin: 8.8/10 (Category avg: 8.8/10)
  • Anomaly identification: 8.3/10 (Category avg: 8.8/10)
  • Automation: 8.2/10 (Category avg: 8.6/10)
  • Integrations: 8.1/10 (Category avg: 8.8/10)

Who Is the Company Behind Unravel Data?

  • Seller: Unravel Data
  • Year Founded: 2016
  • HQ Location: Mountain View, CA
  • Twitter: @unraveldata
    1,029 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    104 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 81% Large, 17% Small

What Do G2 Reviewers Say About Unravel Data?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of access and user-friendly interface of Unravel Data, enhancing their data analysis experience.
  • Users enjoy the easy setup of Unravel Data, finding it intuitive to add AI agents effortlessly.
  • Users value the efficiency of Unravel Data, enabling quick data analysis and effective issue diagnosis.
  • Users value the clear performance insights from Unravel Data, enabling quick analysis and efficient issue diagnosis.
  • Users appreciate the installation ease of Unravel Data, enjoying its user-friendly interface and straightforward AI agent integration.
Cons
  • Users find limited features in Unravel Data, affecting its usability with large datasets and advanced performance metrics.
  • Users find the complex configuration of advanced features challenging, making it difficult to interpret some insights effectively.
  • Users find the complex setup for advanced features challenging, often needing extra context for insights interpretation.
  • Users find the configuration difficulty of advanced features in Unravel Data frustrating and challenging to manage.
  • Users dislike the configuration challenges in Unravel Data, especially when tuning detailed metrics and interpreting insights.

What Are Recent G2 Reviews of Unravel Data?

What Are G2 Users Discussing About Unravel Data?

Soda

Most companies struggle to operationalize data governance and quality. Business teams don’t want to manually enforce rules, and engineers get buried in pipeline issues — eroding trust in data and slowing innovation. Soda fixes this with the only end-to-end data quality platform that automates the entire workflow — from detection to resolution — with AI built for data quality. We meet users where they are: - Engineers manage everything as code in Git. - Business users create and review data contracts in a collaborative interface. - Together, they work in a shared, AI-powered workflow to define quality expectations, monitor metrics, and isolate and remediate bad data directly in their environment. By uniting teams, automating with AI, and securing trust at the source, Soda helps organizations like Disney, Nubank, and HelloFresh restore confidence in their data and decisions. Why Soda? - Best AI for Data Quality — purpose-built, faster, and more accurate, with 70% fewer false positives than traditional monitoring. - Unite Business and Engineering — collaborative data contracts that bridge governance and technical workflows. - Securely Isolate and Fix Bad Data — record-level anomaly detection and remediation inside your own environment. Soda brings width and depth to data quality — from every dataset across multiple warehouses to every individual record in a dataset. Join us in building a world where teams trust their data, decisions, and AI.

Average Rating: 4.4/5.0

Total Reviews: 55

How Do G2 Users Rate Soda?

  • Ease of Admin: 8.6/10 (Category avg: 8.8/10)
  • Anomaly identification: 8.6/10 (Category avg: 8.8/10)
  • Automation: 8.1/10 (Category avg: 8.6/10)
  • Integrations: 8.5/10 (Category avg: 8.8/10)

Who Is the Company Behind Soda?

  • Seller: Soda
  • Year Founded: 2018
  • HQ Location: Brussels, BE
  • Twitter: @sodadata
    897 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    129 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 44% Medium, 40% Large

What Do G2 Reviewers Say About Soda?

AI-generated summary from verified user reviews

Pros
  • Users value the high data quality of Soda, benefiting from its intuitive interface and effective project management support.
  • Users value the responsive customer support from the Soda team through Slack, leading to effective communication and assistance.
  • Users enjoy the customization options of Soda, enabling tailored data quality tests to fit specific needs.
  • Users value the clean and intuitive interface of Soda for efficient data management and real-time insights.
  • Users value the ease of use of Soda, finding its interface intuitive and helpful for efficient project management.
Cons
  • Users find that Soda has limited functionality, with advanced features feeling underutilized for their specific project management needs.
  • Users feel that limited access controls prevent them from fully utilizing Soda's advanced features tailored for project managers.
  • Users face access issues as limited permissions hinder the utilization of advanced features tailored for project management.
  • Users feel that Soda's data management issues could improve with better automated quality testing features for efficiency.
  • Users find the limited features of Soda restrict their efficiency, lacking essential tools tailored for project management tasks.

What Are Recent G2 Reviews of Soda?

Sifflet

Sifflet is the control plane for Data and AI. Data teams spend too much time firefighting — bad data reaches the business before anyone catches it, root cause takes days, and the fix is invisible to the stakeholders who were burned. The result is a slow erosion of trust in every dashboard, report, and AI output the company relies on. We give data teams one layer that catches issues across the full stack, explains exactly where they came from, and shows how to resolve them — before the CFO sees the wrong number. Teams like BBC, Saint-Gobain, Euronext, and CMA-CGM use Sifflet to run reliable data infrastructure at enterprise scale — with coverage from legacy systems to modern cloud stacks. The result: fewer incidents, faster root cause, and data that can be defended in any meeting.

Average Rating: 4.3/5.0

Total Reviews: 56

How Do G2 Users Rate Sifflet?

  • Ease of Admin: 8.5/10 (Category avg: 8.8/10)
  • Anomaly identification: 8.7/10 (Category avg: 8.8/10)
  • Automation: 8.5/10 (Category avg: 8.6/10)
  • Integrations: 7.9/10 (Category avg: 8.8/10)

Who Is the Company Behind Sifflet?

  • Seller: Sifflet
  • Year Founded: 2021
  • HQ Location: Paris, Ile-de-France
  • Twitter: @Siffletdata
    389 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    49 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 74% Medium, 23% Large

What Do G2 Reviewers Say About Sifflet?

AI-generated summary from verified user reviews

Pros
  • Users value the efficiency improvements of Sifflet for effective issue spotting and timely alerts on Slack.
  • Users find Sifflet's ease of use enhances monitoring, facilitating quick responses to issues and notifications.
  • Users value the monitoring capabilities of Sifflet, enabling effective management and identification of data anomalies.
  • Users value the full visibility of data lineage from Sifflet, enhancing understanding of data flow and dependencies.
  • Users value the real-time alerting system for proactive issue spotting and collaboration across teams using Slack.
Cons
  • Users find the limited customization options frustrating, particularly for bulk editing and advanced configurations.
  • Users report a complex setup process for Sifflet, requiring significant time and effort for onboarding and monitoring.
  • Users find the overwhelming number of alerts challenging, requiring significant time to manage incidents effectively.
  • Users note the limited integration options with Hadoop and Elasticsearch, hindering its effectiveness for large data loads.
  • Users experience occasional lineage view loading delays and confusion in complex data warehouse architectures.

What Are Recent G2 Reviews of Sifflet?

Shalaka Joshi
SJ
Researched and written by Shalaka Joshi
Updated