Best Enterprise Data Observability Software

How Many Data Observability Software Products Does G2 Track?

Total Products under this Category: 74

Category Stats (Nov 2026)

  • Average Rating: 4.56/5 (↑0.01 vs Oct 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: DataBuck (+10.3%) - Among all products in this category, DataBuck recorded the largest rating increase compared to last month

Last updated: November 10, 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: 579

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: 8.7/10)
  • Integrations: 8.2/10 (Category avg: 8.9/10)

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?

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: 8.7/10)
  • Integrations: 8.3/10 (Category avg: 8.9/10)

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?

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: 8.7/10)
  • Integrations: 9.0/10 (Category avg: 8.9/10)

Who Is the Company Behind Metaplane?

  • Seller: Datadog
  • Company Website:
  • Year Founded: 2010
  • HQ Location: New York
  • LinkedIn® Page: www.linkedin.com
    10,780 employees on LinkedIn®
  • Ownership: NASDAQ: DDOG

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 value the ease of use of Metaplane, enjoying its intuitive design and seamless integration with workflows.
  • Users value the easy integrations of Metaplane, enhancing workflow efficiency and boosting confidence in data reliability.
  • Users value Metaplane for its proactive monitoring, enabling quick detection of data quality issues and seamless integration.
  • Users value the quick alerts feature of Metaplane, enhancing smooth monitoring and proactive problem resolution.
  • Users value the proactive automated anomaly detection of Metaplane, significantly enhancing data trust and saving time.
Cons
  • Users face limited customization options, making it challenging to tailor monitoring and alerts to specific needs.
  • Users find that alert sensitivity can create noise, making it necessary to carefully adjust notification settings.
  • Users find the inefficient alert system in Metaplane can create unnecessary noise and require time-consuming adjustments.
  • Users find the complex setup of Metaplane time-consuming, especially when fine-tuning monitors and alerts.
  • Users find the difficult setup of Metaplane time-consuming, especially when fine-tuning monitors 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: 8.7/10)
  • Integrations: 8.6/10 (Category avg: 8.9/10)

Who Is the Company Behind IBM Databand?

  • 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: 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 feature of IBM Databand, enhancing monitoring and insights in data pipelines.
  • Users value the monitoring capabilities of IBM Databand, appreciating its insights and end-to-end visibility in data pipelines.
  • Users appreciate the automation capabilities of IBM Databand, enhancing efficiency in data pipeline management and monitoring.
  • Users value the customizable dashboard of IBM Databand for its simplicity and accessibility of key metrics.
  • Users value the data management capabilities of IBM Databand for efficiently handling ETL processes and pipeline monitoring.
Cons
  • Users note the limited features in IBM Databand, finding it challenging for smaller teams and new users.
  • Users find IBM Databand to be expensive for smaller teams, limiting accessibility and complicating onboarding for new users.
  • Users face integration issues with IBM Databand, particularly with limited compatibility and customizability for dashboards.
  • Users find IBM Databand has a steep learning curve, making onboarding challenging and navigation complex for new users.
  • Users find the missing features in IBM Databand, such as self error resolution and limited integrations, frustrating.

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: 8.7/10)
  • Integrations: 8.6/10 (Category avg: 8.9/10)

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 praise Pantomath for its outstanding data lineage visualization, streamlining operations and enhancing overall efficiency.
  • Users value the real-time monitoring features of Pantomath, enhancing management of complex data and improving operational efficiency.
  • Users commend Pantomath's excellent customer support, highlighting their responsiveness and effective issue resolution.
  • Users benefit from enhanced efficiency in data operations, gaining quick insights and improved productivity with Pantomath.
  • Users value the automation of Pantomath, which simplifies management of complex data pipelines and enhances operational efficiency.
Cons
  • Users struggle with initial alert overload and the need for configuration, complicating the onboarding process.
  • Users find the poor documentation on IaC integration frustrating, despite timely bug fixes from the Pantomath team.
  • Users find the complex setup challenging, needing time to adjust and streamline configurations effectively.
  • Users find the difficult learning curve challenging, especially those lacking prior experience with similar features.
  • Users find the learning curve steep, especially for those lacking prior experience with some 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: 8.7/10)
  • Integrations: 9.0/10 (Category avg: 8.9/10)

Who Is the Company Behind Telmai?

  • Seller: Telmai
  • Year Founded: 2020
  • HQ Location: San Mateo , Ca
  • 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 appreciate the ease of use of Telmai, enabling quick data quality tracking without complex setups.
  • Users value the automation of data quality checks in Telmai, enabling proactive issue resolution and minimizing manual work.
  • Users praise the ML-driven anomaly detection in Telmai for proactively maintaining data reliability and preventing issues.
  • Users value Telmai for its proactive data quality assurance, enabling efficient monitoring and issue resolution.
  • Users value the easy integrations of Telmai, enabling seamless connection with existing cloud data environments and tools.
Cons
  • Users note that while error handling is basic, advanced error handling support is missing, requiring additional setup for alerts.
  • Users note a significant learning difficulty with Telmai, requiring time and training to harness its full potential.
  • Users find the limited functionality of Telmai restricts deeper insights and complicates the management of data.
  • Users note a steep learning curve with Telmai, hindering efficient training and configuration for optimal usage.
  • Users found the initial alert management setup cumbersome, requiring external workflows for effective operational efficiency.

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: 8.7/10)
  • Integrations: 8.1/10 (Category avg: 8.9/10)

Who Is the Company Behind Unravel Data?

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 use in Unravel Data, finding it intuitive and efficient for data analysis.
  • Users value the easy setup of Unravel Data, appreciating its straightforward interface for adding AI agents.
  • Users commend the efficiency of Unravel Data, enabling swift analysis and effective diagnosis of data issues.
  • Users value the clear performance insights from Unravel Data, enabling quick data analysis and efficient issue diagnosis.
  • Users appreciate the installation ease of Unravel Data, finding the process quick and user-friendly.
Cons
  • Users find Unravel Data has limited features, struggling with large datasets and lacking intuitive insight interpretation.
  • Users dislike the complex configuration of advanced features, making it difficult to interpret insights effectively.
  • Users find the complex setup of Unravel Data challenging, particularly when configuring advanced features and interpreting insights.
  • Users find configuration difficulty with advanced features in Unravel Data, complicating the tuning of detailed performance metrics.
  • Users dislike the configuration challenges in Unravel Data, finding it difficult to interpret insights and tune performance metrics.

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: 8.7/10)
  • Integrations: 8.5/10 (Category avg: 8.9/10)

Who Is the Company Behind Soda?

  • Seller: Soda
  • Year Founded: 2018
  • HQ Location: Brussels, BE
  • 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 streamlined data quality tasks in Soda, enhancing team alignment on data integrity and insights.
  • Users value the responsive support from Soda, enjoying direct interactions with the team through Slack for efficient assistance.
  • Users love the customization options of Soda, enabling tailored data quality tests and flexible usage without upfront licensing.
  • Users value the streamlined data management of Soda, enhancing team alignment and ensuring data quality effortlessly.
  • Users appreciate the ease of use of Soda, finding its interface intuitive and efficient for project management.
Cons
  • Users find the limited functionality of Soda restricts access to advanced features, hindering project management efficiency.
  • Users feel that limited access control restricts advanced feature utilization, making it less relevant for project managers' tasks.
  • Users find access issues limiting, as many advanced features remain underutilized or irrelevant for their specific roles.
  • Users feel there are data management issues with Soda, particularly related to the need for automated data quality testing.
  • Users find the limited features of Soda restrict growth and often feel underutilized for their specific roles.

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: 8.7/10)
  • Integrations: 7.9/10 (Category avg: 8.9/10)

Who Is the Company Behind Sifflet?

  • Seller: Sifflet
  • Year Founded: 2021
  • HQ Location: Paris, Ile-de-France
  • 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 with Sifflet, allowing proactive issue tracking and reducing unnecessary alerts.
  • Users value the ease of use of Sifflet, especially in creating monitors effortlessly for all team members.
  • Users value the ease of creating monitors with Sifflet, significantly improving data pipeline management and issue prevention.
  • Users value the clear visibility of data lineage in Sifflet, aiding in quick issue resolution and understanding impacts.
  • Users value the real-time alerts from Sifflet, enhancing issue identification and awareness across teams.
Cons
  • Users find the limited customization options for tags and dashboard layouts restrictive, impacting usability and efficiency.
  • Users find the setup process complex and time-consuming, requiring significant effort and customization to optimize functionality.
  • Users find the alert management configuration complex, which can overwhelm new users but is manageable with experience.
  • Users note the limited integration capabilities of Sifflet, affecting functionality and requiring manual UI operations.
  • Users face challenges with lineage issues, affecting root cause analysis and loading times for large datasets.

What Are Recent G2 Reviews of Sifflet?

Shalaka Joshi
SJ
Researched and written by Shalaka Joshi
Updated