# Best Data Observability Software

  *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*

   Data observability involves the complete monitoring, managing, and understanding of the modern data tech stack. These tools allow companies to better manage their data by helping them discover and solve real-time data issues and gain complete insight into the system’s data health. Data observability tools help companies accelerate the adoption of data across departments. This helps in making strategic and data-driven decisions that benefit the entire organization.

The concept of data observability stems from best practices learned from DevOps software to manage impartial, inaccurate, or erroneous data. These best practices, which include optimizing logs, real-time insights, and so on, enable the creation of error-free and trusted data across the entire data stack, which includes data sources, data warehouses, ETL tools, ML/BI tools, etc.

Data observability tools are a part of [DataOps platforms](https://www.g2.com/categories/dataops-platforms). DataOps platforms assemble several types of data management software into an individual, integrated environment. The platform unifies all the development and operations in data workflows. Data observability software focuses on monitoring the health of the data pipelines and the overall system.

Data observability tools differ from [monitoring software](https://www.g2.com/categories/monitoring) since the latter focuses on pre-determined metrics to identify bugs, whereas data observability focuses on real-time detection and resolution. Data observability also differs from [data quality software](https://www.g2.com/categories/data-quality), wherein the former focuses on reducing the number of data incidents while accelerating resolution time. Data quality is the result of powerful data observability across the modern data stack.

To qualify for inclusion in the Data Observability category, a product must:

- Proactively monitor, alert, track, log, compare, and analyze data for any errors or issues across the entire data stack
- Monitor data at rest and data in motion, and does not require data extraction from current storage location
- Connect to an existing stack without any need to write code or modify data pipelines





## Best Data Observability Software At A Glance

- **Leader:** [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews)
- **Highest Performer:** [SquaredUp](https://www.g2.com/products/squaredup-squaredup/reviews)
- **Easiest to Use:** [Dash0](https://www.g2.com/products/dash0/reviews)
- **Top Trending:** [Sifflet](https://www.g2.com/products/sifflet/reviews)
- **Best Free Software:** [Metaplane](https://www.g2.com/products/metaplane/reviews)


## Top-Rated Products (Ranked by G2 Score)
  ### 1. [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:** 485

**User Satisfaction Scores:**

- **Ease of Admin:** 8.5/10 (Category avg: 8.8/10)
- **Anomaly identification:** 8.7/10 (Category avg: 8.8/10)
- **Automation:** 8.1/10 (Category avg: 8.6/10)
- **Integrations:** 8.1/10 (Category avg: 8.8/10)


**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, 44% 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)

  ### 2. [Metaplane](https://www.g2.com/products/metaplane/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Datadog](https://www.g2.com/sellers/datadog)
- **Year Founded:** 2010
- **HQ Location:** New York
- **Twitter:** @datadoghq (50,828 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1066442/ (10,625 employees on LinkedIn®)
- **Ownership:** NASDAQ: DDOG

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 66% Mid-Market, 22% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (6 reviews)
- Easy Integrations (6 reviews)
- Monitoring (4 reviews)
- Alerting System (3 reviews)
- Anomaly Detection (3 reviews)

**Cons:**

- Limited Customization (4 reviews)
- Alert Management (3 reviews)
- Inefficient Alert System (3 reviews)
- Complex Setup (2 reviews)
- Difficult Setup (2 reviews)

  ### 3. [DQLabs](https://www.g2.com/products/dqlabs/reviews)
  DQLabs redefines data management with Semantics and GenAI powered Modern Data Quality Platform, empowering organisations to transform raw data into reliable, actionable insights. Our automation-first, self-learning platform seamlessly integrates Data Observability, Augmented Data Quality, Data Discovery and Semantics, fostering collaborative decision-making across your data ecosystem. Turn data into action faster, easier, cost-efficiently and more reliably with DQLabs - your gateway to transformative business outcomes.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 43

**User Satisfaction Scores:**

- **Ease of Admin:** 9.5/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.1/10 (Category avg: 8.8/10)
- **Automation:** 9.3/10 (Category avg: 8.6/10)
- **Integrations:** 9.0/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [DQLabs](https://www.g2.com/sellers/dqlabs)
- **Year Founded:** 2020
- **HQ Location:** Pasadena, California
- **Twitter:** @DQLABSAI (245 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dqlabsai/ (95 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 44% Mid-Market, 20% Enterprise


#### Pros & Cons

**Pros:**

- Data Quality (28 reviews)
- Ease of Use (24 reviews)
- Efficiency Improvement (24 reviews)
- Automation (20 reviews)
- Features (20 reviews)

**Cons:**

- Poor Documentation (7 reviews)
- Product Immaturity (2 reviews)
- Complexity (1 reviews)
- Data Management Issues (1 reviews)
- Data Quality (1 reviews)

  ### 4. [Dash0](https://www.g2.com/products/dash0/reviews)
  Dash0 is the only OpenTelemetry Native observability platform built with developers in mind. With granular, resource-centric monitoring, Dash0 provides real-time visibility across your applications and infrastructure. Its simple, transparent pricing and seamless integration with open standards like Perses, PromQL, and Kubernetes make it a breeze to use. Dash0 helps you quickly spot issues and optimize performance with actionable insights from logs, traces, and metrics, all in one place.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 41

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [Dash0](https://www.g2.com/sellers/dash0-d8c455c3-cd78-4a96-904e-add5fd91946b)
- **Company Website:** https://www.dash0.com
- **Year Founded:** 2023
- **HQ Location:** New York
- **Twitter:** @dash0hq (2,285 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dash0hq (109 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 50% Small-Business, 31% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (31 reviews)
- Customer Support (30 reviews)
- User Interface (24 reviews)
- Easy Setup (20 reviews)
- Easy Integrations (18 reviews)

**Cons:**

- Limited Features (10 reviews)
- Feature Deficiency (9 reviews)
- Missing Features (9 reviews)
- Limited Customization (6 reviews)
- Feature Limitations (5 reviews)

  ### 5. [Rakuten SixthSense Data Observability](https://www.g2.com/products/rakuten-sixthsense-data-observability/reviews)
  SixthSense Data Observability is a sophisticated data observability solution designed to help users effectively manage, monitor, and optimize their data pipelines. This product addresses the critical need for organizations to gain comprehensive visibility into their data flows, ensuring that data remains accurate, reliable, and actionable. By providing real-time insights into data quality and performance, SixthSense empowers businesses to make informed decisions based on trustworthy data. Targeted primarily at data engineers, data analysts, and business intelligence professionals, SixthSense Data Observability serves organizations that rely heavily on data for operational efficiency and strategic planning. With the increasing complexity of data ecosystems, these professionals require tools that can seamlessly integrate with their existing workflows while providing robust monitoring capabilities. The solution is particularly beneficial for companies that handle large volumes of data and need to ensure that their data pipelines are functioning optimally without interruptions or inaccuracies. Key features of SixthSense Data Observability include real-time monitoring, anomaly detection, and automated alerting. These functionalities allow users to identify and address potential issues before they escalate, minimizing downtime and enhancing overall data reliability. The platform also offers comprehensive dashboards that visualize data flows and performance metrics, making it easier for users to track the health of their data pipelines at a glance. Additionally, the solution supports integration with various data sources and tools, ensuring that it fits seamlessly into existing data architectures. The benefits of using SixthSense Data Observability extend beyond mere monitoring. By providing actionable insights and facilitating proactive management of data pipelines, the solution helps organizations enhance their data accuracy and integrity. This leads to improved decision-making processes and fosters a data-driven culture within the organization. Furthermore, the ability to quickly identify and resolve data issues reduces operational risks and enhances overall productivity, allowing teams to focus on strategic initiatives rather than troubleshooting data problems. In a landscape where data is a critical asset, SixthSense Data Observability stands out by offering a comprehensive, user-friendly approach to data management. Its focus on real-time insights and proactive monitoring equips organizations with the tools they need to navigate the complexities of modern data environments effectively. As businesses continue to rely on data for competitive advantage, implementing a robust observability solution like SixthSense becomes essential for maintaining data quality and operational excellence.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

- **Anomaly identification:** 9.0/10 (Category avg: 8.8/10)
- **Automation:** 9.0/10 (Category avg: 8.6/10)
- **Integrations:** 9.0/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Rakuten SixthSense](https://www.g2.com/sellers/rakuten-sixthsense-f1af4c23-8be7-4bf4-a775-a4d50eebce5d)
- **Year Founded:** 2016
- **HQ Location:** Bengaluru, IN
- **LinkedIn® Page:** https://www.linkedin.com/company/rakuten-sixthsense/ (12 employees on LinkedIn®)
- **Ownership:** TYO: 4755

**Reviewer Demographics:**
  - **Company Size:** 45% Enterprise, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Customer Support (7 reviews)
- Ease of Use (7 reviews)
- Data Lineage (4 reviews)
- Monitoring (4 reviews)
- Real-time Monitoring (4 reviews)

**Cons:**

- Expensive (3 reviews)
- Complex Setup (2 reviews)
- Alert Management (1 reviews)
- Limited Integration (1 reviews)

  ### 6. [SquaredUp](https://www.g2.com/products/squaredup-squaredup/reviews)
  SquaredUp is a unified observability portal. Say goodbye to blind spots and data silos. Using data mesh and cutting-edge data visualization, SquaredUp gives IT and engineering teams one place to see everything that matters. Bring together data from across your tech stack without the headache of moving the data. Unlike other monitoring and observability tools that rely on a data warehouse, SquaredUp leaves your data where it is, plugging directly into each data source to index and stitch the data together using a data mesh. Teams have one place to go where they can search, visualize, and analyze data across all their tools. Take control of infrastructure, application, and product performance with unified visibility. Learn more at squaredup.com What you get: \> Cutting-edge data visualization \> Access to 100+ data sources \> Any custom data source via Web API \> Multi-cloud observability \> Cost monitoring \> Unlimited dashboards \> Unlimited monitors Key features: \> Out-of-box dashboards \> Simple, flexible dashboard designer \> Real-time monitoring \> High-level roll-up views \> Object drill downs \> Notifications (Slack, Teams, email, etc.) \> SQL analytics Free for up to 3 users. Head over to SquaredUp - The Unified Observability Portal to learn more \>\>\> https://squaredup.com/ We also have a dedicated SCOM product, Dashboard Server for SCOM. See our product page to learn how we help to transform SCOM with end-to-end visibility \>\>\> https://ds.squaredup.com/ To find out what plan is best for you, go to \>\>\> https://squaredup.com/pricing/


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 15

**User Satisfaction Scores:**

- **Ease of Admin:** 9.6/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.2/10 (Category avg: 8.8/10)
- **Automation:** 9.2/10 (Category avg: 8.6/10)
- **Integrations:** 9.2/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [SquaredUp](https://www.g2.com/sellers/squaredup-a3718528-f219-4047-b8f5-3a20303868d1)
- **Year Founded:** 2011
- **HQ Location:** Maidenhead, GB
- **Twitter:** @squared_up (1,174 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/squared-up-limited/ (94 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 60% Enterprise, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Dashboard Customization (1 reviews)
- Dashboard Design (1 reviews)
- Dashboard Quality (1 reviews)
- Dashboards (1 reviews)
- Dashboard Usability (1 reviews)

**Cons:**

- Complexity (1 reviews)
- Complex Setup (1 reviews)
- Configuration Difficulty (1 reviews)
- Difficult Configuration (1 reviews)
- Difficult Learning (1 reviews)

  ### 7. [decube](https://www.g2.com/products/decube/reviews)
  Decube is a Context Layer platform specifically designed for the AI era, providing organizations with the ability to give their data meaning, memory, and trust. This innovative system integrates various components such as metadata management, automated lineage tracking, data quality assurance, and observability to create a comprehensive real-time map of data dynamics. By understanding how data operates, flows, and its reliability, Decube empowers enterprises to make informed decisions and effectively manage AI workloads. Targeted primarily at enterprises that rely heavily on data-driven decision-making, Decube addresses a critical challenge faced by many organizations: the lack of contextual understanding of their data. In an age where data is abundant, the real issue lies in the ability to interpret and utilize that data effectively. Decube provides a connected understanding of the entire data ecosystem, which helps eliminate blind spots and enhances governance. This contextual awareness is essential for organizations looking to leverage AI technologies and ensure that their models, dashboards, and agents operate with greater intelligence and safety. Key features of Decube include its robust metadata management capabilities, which allow users to track and manage data lineage effortlessly. This feature ensures that organizations can trace the origins and transformations of their data, thereby enhancing transparency and accountability. Additionally, Decube’s focus on data quality means that users can trust the information they are working with, reducing the risk of errors in critical decision-making processes. The observability aspect of the platform further enables organizations to monitor data flows in real-time, ensuring that any issues can be identified and addressed promptly. The benefits of using Decube extend beyond mere data management. By providing a living, interconnected understanding of data, Decube enhances the overall operational confidence of organizations. This platform not only strengthens governance but also facilitates smarter decision-making by ensuring that all data-driven models are built on a foundation of reliable and contextualized information. As businesses increasingly depend on trustworthy data and AI-ready infrastructure, Decube stands out as a vital tool that equips them with the necessary context to navigate the complexities of the modern data landscape.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 24

**User Satisfaction Scores:**

- **Ease of Admin:** 8.8/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.1/10 (Category avg: 8.8/10)
- **Automation:** 8.8/10 (Category avg: 8.6/10)
- **Integrations:** 8.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Decube Data](https://www.g2.com/sellers/decube-data)
- **Company Website:** https://decube.io
- **Year Founded:** 2022
- **HQ Location:** Kuala Lumpur
- **Twitter:** @decube_data (113 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/decube-data/ (44 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 38% Mid-Market, 33% Enterprise


#### Pros & Cons

**Pros:**

- User Interface (8 reviews)
- Ease of Use (7 reviews)
- Features (7 reviews)
- Data Quality (6 reviews)
- Insights (6 reviews)

**Cons:**

- Limited Functionality (3 reviews)
- Complex Setup (2 reviews)
- Limited Features (2 reviews)
- Missing Features (2 reviews)
- Poor Customer Support (2 reviews)

  ### 8. [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:**

- **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:** 8.3/10 (Category avg: 8.8/10)


**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 (339 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)

  ### 9. [SYNQ](https://www.g2.com/products/synq-synq/reviews)
  SYNQ is a data observability platform that helps modern data teams define, monitor, and manage their data products. It brings together ownership, testing, and incident workflows so teams can stay ahead of issues, reduce data downtime, and deliver trusted data faster. With SYNQ, every critical data product has clear ownership and real-time visibility into its health. When something breaks, the right people are alerted—with the context they need to understand and resolve the issue quickly. At the center of SYNQ is Scout, your autonomous, always-on data quality agent. Scout proactively monitors data products, recommends what and where to test, does root-cause analysis and fixes issues. It connects lineage, issue history, and contextual data to help teams fix problems faster. SYNQ integrates with the tools you already use and is trusted by leading scale-ups and enterpises such as VOI, Avios, Aiven and Ebury.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 34

**User Satisfaction Scores:**

- **Ease of Admin:** 9.7/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.0/10 (Category avg: 8.8/10)
- **Automation:** 9.0/10 (Category avg: 8.6/10)
- **Integrations:** 9.2/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [SYNQ](https://www.g2.com/sellers/synq)
- **Year Founded:** 2022
- **HQ Location:** London, GB
- **LinkedIn® Page:** https://www.linkedin.com/company/getsynq/ (17 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Analytics Engineer
  - **Top Industries:** Leisure, Travel & Tourism, Computer Software
  - **Company Size:** 91% Mid-Market, 6% Enterprise


#### Pros & Cons

**Pros:**

- Customer Support (9 reviews)
- Data Lineage (4 reviews)
- Monitoring (4 reviews)
- Alerting System (3 reviews)
- Data Quality (3 reviews)

**Cons:**

- Not User-Friendly (2 reviews)
- UX Improvement (2 reviews)
- Alert Management (1 reviews)
- Complex Setup (1 reviews)
- Data Management (1 reviews)

  ### 10. [Avo](https://www.g2.com/products/avo/reviews)
  We help customers plan, implement, and govern their event-driven infrastructure.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 23

**User Satisfaction Scores:**

- **Ease of Admin:** 9.5/10 (Category avg: 8.8/10)
- **Anomaly identification:** 2.5/10 (Category avg: 8.8/10)
- **Automation:** 5.0/10 (Category avg: 8.6/10)
- **Integrations:** 8.3/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Avo](https://www.g2.com/sellers/avo)
- **Year Founded:** 2018
- **HQ Location:** Walnut, US
- **Twitter:** @avohq (726 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/avohq/ (34 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services
  - **Company Size:** 57% Mid-Market, 30% Small-Business


#### Pros & Cons

**Pros:**

- Data Lineage (2 reviews)
- Ease of Use (2 reviews)
- Comprehensive Overview (1 reviews)
- Customer Support (1 reviews)
- Data Management (1 reviews)

**Cons:**

- Complex Setup (1 reviews)
- Difficult Setup (1 reviews)
- Learning Difficulty (1 reviews)
- Steep Learning Curve (1 reviews)

  ### 11. [Matia](https://www.g2.com/products/matia/reviews)
  Matia is a data operations platform that enables modern data teams to build, manage, and monitor end-to-end data pipelines in one place. Matia allows data teams to spend time managing their data, instead of their tools. Matia combines ingestion, reverse ETL, data cataloging, and observability into a single, unified interface. Rather than stitching together multiple tools for data movement, observability, and metadata tracking, teams use Matia to streamline their workflow, reduce vendor bloat, and improve data trust across the organization. Common use cases include syncing operational data into warehouse destinations, monitoring pipeline health with built-in alerts, documenting data assets automatically, and aligning data delivery with business-critical SLAs. Teams adopt Matia to simplify their stack, reduce engineering overhead, and create more transparent, reliable data infrastructure.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 32

**User Satisfaction Scores:**

- **Ease of Admin:** 10.0/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.2/10 (Category avg: 8.8/10)
- **Automation:** 9.7/10 (Category avg: 8.6/10)
- **Integrations:** 10.0/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Matia](https://www.g2.com/sellers/matia)
- **Company Website:** https://www.matia.io
- **Year Founded:** 2023
- **HQ Location:** Miami, US
- **LinkedIn® Page:** http://linkedin.com/company/matia-data (39 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Computer Software
  - **Company Size:** 59% Mid-Market, 25% Small-Business


#### Pros & Cons

**Pros:**

- Customer Support (25 reviews)
- Ease of Use (18 reviews)
- Features (18 reviews)
- Integrations (13 reviews)
- Reliability (12 reviews)

**Cons:**

- Limited Connectors (4 reviews)
- Missing Features (4 reviews)
- Limited Features (3 reviews)
- Limited Integrations (3 reviews)
- Not User-Friendly (3 reviews)

  ### 12. [Telmai](https://www.g2.com/products/telmai/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Telmai](https://www.g2.com/sellers/telmai)
- **Year Founded:** 2020
- **HQ Location:** San Mateo , Ca
- **Twitter:** @telmai1 (98 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/telmai/ (15 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 55% Enterprise, 32% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Automation (5 reviews)
- Anomaly Detection (4 reviews)
- Data Quality (4 reviews)
- Easy Integrations (4 reviews)

**Cons:**

- Error Handling (2 reviews)
- Learning Difficulty (2 reviews)
- Limited Functionality (2 reviews)
- UX Improvement (2 reviews)
- Alert Management (1 reviews)

  ### 13. [Elementary Data](https://www.g2.com/products/elementary-data/reviews)
  Elementary is a data observability solution designed for dbt-centric data stacks. It seamlessly integrates into your dbt development workflow and pipelines and ensures you are the first to know when something breaks. Trusted by 5000+ analytics and data engineers, Elementary helps data-driven companies deliver production-grade data.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 18

**User Satisfaction Scores:**

- **Ease of Admin:** 8.9/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.0/10 (Category avg: 8.8/10)
- **Automation:** 8.6/10 (Category avg: 8.6/10)
- **Integrations:** 7.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Elementary Data](https://www.g2.com/sellers/elementary-data)
- **Year Founded:** 2022
- **HQ Location:** N/A
- **Twitter:** @ElementaryData (406 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/elementary-data/ (41 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 56% Mid-Market, 22% Enterprise


#### Pros & Cons

**Pros:**

- Features (8 reviews)
- Insights (8 reviews)
- Ease of Use (7 reviews)
- Slack Integration (7 reviews)
- Alerting System (6 reviews)

**Cons:**

- Integration Issues (6 reviews)
- Database Integration Issues (4 reviews)
- Limited Integration (3 reviews)
- API Limitations (2 reviews)
- Limited Features (2 reviews)

  ### 14. [Validio](https://www.g2.com/products/validio/reviews)
  Validio helps Fortune 2000 enterprises and leading tech companies like Nordea, AllianceBernstein, Walden, Point Predictive, and Truecaller improve the reliability of their analytical and operational data. Validio's AI-powered platform automatically monitors and validates both data and business KPIs, surfacing issues in real-time. This enables confident data-driven decision making across business domains such as user experiences, personalization, growth, and product development.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 17

**User Satisfaction Scores:**

- **Ease of Admin:** 9.7/10 (Category avg: 8.8/10)
- **Anomaly identification:** 10.0/10 (Category avg: 8.8/10)
- **Automation:** 9.5/10 (Category avg: 8.6/10)
- **Integrations:** 9.4/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Validio](https://www.g2.com/sellers/validio)
- **Year Founded:** 2019
- **HQ Location:** Stockholm, SE
- **Twitter:** @Validio_Data (65 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/validio-ab/ (38 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 59% Mid-Market, 29% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (5 reviews)
- Easy Setup (4 reviews)
- Setup Ease (3 reviews)
- Alerts (2 reviews)
- Customer Support (2 reviews)

**Cons:**

- Limited Customization (4 reviews)
- Inadequate Reporting (2 reviews)
- Filtering Issues (1 reviews)
- Poor Documentation (1 reviews)

  ### 15. [Great Expectations](https://www.g2.com/products/great-expectations/reviews)
  We're helping data teams have confidence in their data, no matter what. GX Cloud is our end-to-end platform for managing your data quality process. It delivers the intuitive experience of a fully managed SaaS solution while harnessing the power of the world's most popular data quality framework. With GX Cloud, data teams can work quickly, collaborate effectively, and always know what to expect from their data. GX Core is our open source Python offering, and the world's most popular data quality framework. It's a powerful, flexible data quality solution that empowers data teams to communicate better and take action effectively. At its heart are Expectations: verifiable assertions about your data that create clear and expressive data quality tests.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 11

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [Great Expectations](https://www.g2.com/sellers/great-expectations)
- **Year Founded:** 2017
- **HQ Location:** Remote, US
- **Twitter:** @expectgreatdata (3,558 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/greatexpectations-data/ (44 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 45% Mid-Market, 36% Small-Business


  ### 16. [Sifflet](https://www.g2.com/products/sifflet/reviews)
  About Sifflet Sifflet is a business-aware data observability platform that moves data teams from reactive firefighting to proactive decision intelligence. Powered by an intelligent system of AI agents—Sentinel, Sage, and Forge—Sifflet autonomously detects anomalies, diagnoses root causes, and suggests code resolutions. By enriching technical alerts with full-stack lineage and downstream business usage, Sifflet allows data engineers and leaders to prioritize incidents based on business risk rather than technical severity. Trusted by industry leaders like Carrefour or Penguin Random House, Sifflet bridges the gap between data quality and business impact, ensuring your data is always safe for executive decisions and AI consumption. Learn more at siffletdata.com.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 45

**User Satisfaction Scores:**

- **Ease of Admin:** 8.5/10 (Category avg: 8.8/10)
- **Anomaly identification:** 8.5/10 (Category avg: 8.8/10)
- **Automation:** 8.3/10 (Category avg: 8.6/10)
- **Integrations:** 7.6/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Sifflet](https://www.g2.com/sellers/sifflet)
- **Company Website:** https://www.siffletdata.com/
- **Year Founded:** 2021
- **HQ Location:** Paris, Ile-de-France
- **Twitter:** @Siffletdata (392 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sifflet/ (48 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 78% Mid-Market, 24% Enterprise


#### Pros & Cons

**Pros:**

- Efficiency Improvement (37 reviews)
- Ease of Use (36 reviews)
- Monitoring (36 reviews)
- Data Lineage (32 reviews)
- Alerting System (31 reviews)

**Cons:**

- Limited Customization (17 reviews)
- Complex Setup (11 reviews)
- Alert Management (10 reviews)
- Limited Integration (10 reviews)
- Lineage Issues (10 reviews)

  ### 17. [Unravel Data](https://www.g2.com/products/unravel-data/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Unravel Data](https://www.g2.com/sellers/unravel-data)
- **Year Founded:** 2016
- **HQ Location:** Mountain View, CA
- **Twitter:** @unraveldata (1,028 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/unravel-data (103 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Financial Services
  - **Company Size:** 81% Enterprise, 17% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (2 reviews)
- Easy Setup (1 reviews)
- Efficiency (1 reviews)
- Insights (1 reviews)
- Installation Ease (1 reviews)

**Cons:**

- Limited Features (2 reviews)
- Complex Configuration (1 reviews)
- Complex Setup (1 reviews)
- Configuration Difficulty (1 reviews)
- Configuration Issues (1 reviews)

  ### 18. [IBM Databand](https://www.g2.com/products/ibm-databand/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (708,000 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Data Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Mid-Market, 33% Enterprise


#### Pros & Cons

**Pros:**

- Anomaly Detection (2 reviews)
- Monitoring (2 reviews)
- Automation (1 reviews)
- Dashboard Customization (1 reviews)
- Data Management (1 reviews)

**Cons:**

- Limited Features (3 reviews)
- Expensive (2 reviews)
- Integration Issues (2 reviews)
- Learning Difficulty (2 reviews)
- Missing Features (2 reviews)

  ### 19. [groundcover](https://www.g2.com/products/groundcover/reviews)
  Observability, for the Cloud. Monitor everything you run in your cloud without compromising on cost, granularity, or scale.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 26

**User Satisfaction Scores:**

- **Ease of Admin:** 8.8/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.2/10 (Category avg: 8.8/10)
- **Automation:** 8.0/10 (Category avg: 8.6/10)
- **Integrations:** 9.7/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Groundcover](https://www.g2.com/sellers/groundcover)
- **Year Founded:** 2021
- **HQ Location:** Tel Aviv, IL
- **LinkedIn® Page:** https://www.linkedin.com/company/groundcover-com/ (92 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 50% Mid-Market, 38% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (17 reviews)
- Monitoring (9 reviews)
- User Interface (9 reviews)
- Easy Setup (8 reviews)
- Observability (8 reviews)

**Cons:**

- UX Improvement (7 reviews)
- Limited Features (4 reviews)
- Missing Features (4 reviews)
- Poor Customer Support (4 reviews)
- Dashboard Issues (3 reviews)

  ### 20. [Soda](https://www.g2.com/products/soda/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Soda](https://www.g2.com/sellers/soda)
- **Year Founded:** 2018
- **HQ Location:** Brussels, BE
- **Twitter:** @sodadata (898 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sodadata/ (125 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Mid-Market, 40% Enterprise


#### Pros & Cons

**Pros:**

- Data Quality (2 reviews)
- Customer Support (1 reviews)
- Customization (1 reviews)
- Data Management (1 reviews)
- Ease of Use (1 reviews)

**Cons:**

- Limited Functionality (2 reviews)
- Access Control (1 reviews)
- Access Issues (1 reviews)
- Data Management Issues (1 reviews)
- Limited Features (1 reviews)

  ### 21. [Bigeye](https://www.g2.com/products/bigeye/reviews)
  AI is only as good as the data it runs on. Bigeye is the enterprise AI Trust platform built for data-driven organizations that need confidence in how AI uses their data. A longtime leader in data observability and lineage, Bigeye brings data quality, sensitivity scanning, governance, and runtime policy enforcement together in a single, end-to-end platform. This unified approach gives enterprises full visibility and control over how data is accessed, governed, and acted on by AI. By comprehensively managing data and AI, Bigeye helps teams accelerate AI deployments, improve stakeholder trust, and ensure accuracy, safety, and reliability at scale. Leading organizations including USAA, Zoom, Hertz, Cisco, and Freedom Mortgage rely on Bigeye to keep their data, and the AI built on top of it, reliable by default.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 22

**User Satisfaction Scores:**

- **Ease of Admin:** 7.2/10 (Category avg: 8.8/10)
- **Anomaly identification:** 8.2/10 (Category avg: 8.8/10)
- **Automation:** 8.1/10 (Category avg: 8.6/10)
- **Integrations:** 8.4/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Bigeye](https://www.g2.com/sellers/bigeye)
- **Year Founded:** 2019
- **HQ Location:** San Francisco, US
- **Twitter:** @bigeyedata (643 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/bigeye-data/ (68 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 55% Small-Business, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (1 reviews)
- Features (1 reviews)

**Cons:**

- Limited Features (1 reviews)
- Limited Integrations (1 reviews)

  ### 22. [Mozart Data](https://www.g2.com/products/mozart-data-mozart-data/reviews)
  Backed by award-winning data analyst support, Mozart Data is the fastest way to set up scalable, reliable data infrastructure that doesn’t need to be maintained by you. Mozart Data’s all-in-one modern data platform empowers anyone to easily centralize, organize, and analyze their data without engineering resources. Instead of piecing together multiple tools, companies get everything they need to spin up a data stack in an hour — ETL, a data warehouse, and a data transformation tool — and gain visibility into their data pipelines. Join other data-driven companies, like Zeplin, Rippling, Modern Treasury, and Tempo, that are already getting the most out of their data. Learn more at https://www.mozartdata.com


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 68

**User Satisfaction Scores:**

- **Ease of Admin:** 9.3/10 (Category avg: 8.8/10)
- **Anomaly identification:** 7.9/10 (Category avg: 8.8/10)
- **Automation:** 9.3/10 (Category avg: 8.6/10)
- **Integrations:** 9.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Mozart Data](https://www.g2.com/sellers/mozart-data)
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **Twitter:** @MozartData (449 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/mozartdata/ (16 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Financial Services
  - **Company Size:** 53% Small-Business, 47% Mid-Market


  ### 23. [ThinkData Works](https://www.g2.com/products/thinkdata-works/reviews)
  ThinkData Works offers a data catalog platform designed to improve the process of connecting, managing, and sharing data. Cut costs and improve productivity with unique features for virtualizing entire data warehouses, managing custom metadata and templates, and monitoring data health in real time. The result is a centralized view of refined data that can be better leveraged to drive business outcomes — with access and governance controls that support observability and compliance. - Build automated connections to data from any source - Manage, observe, and govern data on a platform as user-friendly as it is powerful - Easily and securely share data to any users, organizations, or applications https://www.thinkdataworks.com


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 14

**User Satisfaction Scores:**

- **Ease of Admin:** 8.7/10 (Category avg: 8.8/10)
- **Anomaly identification:** 9.4/10 (Category avg: 8.8/10)
- **Automation:** 9.2/10 (Category avg: 8.6/10)
- **Integrations:** 9.2/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [ThinkData Works](https://www.g2.com/sellers/thinkdata-works)
- **Year Founded:** 2014
- **HQ Location:** Toronto, CA
- **Twitter:** @thinkdataworks (1,214 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/9217518 (17 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 64% Mid-Market, 36% Small-Business


  ### 24. [Pantomath](https://www.g2.com/products/pantomath/reviews)
  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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Pantomath Inc.](https://www.g2.com/sellers/pantomath-inc)
- **Company Website:** https://www.pantomath.com/
- **Year Founded:** 2022
- **HQ Location:** Cincinnati
- **LinkedIn® Page:** https://www.linkedin.com/company/pantomathdata (47 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services
  - **Company Size:** 73% Enterprise, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Data Lineage (8 reviews)
- Monitoring (7 reviews)
- Customer Support (6 reviews)
- Efficiency Improvement (5 reviews)
- Automation (3 reviews)

**Cons:**

- Alert Management (6 reviews)
- Poor Documentation (2 reviews)
- Complex Setup (1 reviews)
- Difficult Learning Curve (1 reviews)
- Learning Curve (1 reviews)

  ### 25. [Datafold](https://www.g2.com/products/datafold/reviews)
  Datafold is a data observability platform that helps companies prevent data catastrophes. It has a unique ability to identify, prioritize and investigate data quality issues proactively before they affect production. Datafold’s proactive approach to data quality helps data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 24

**User Satisfaction Scores:**

- **Ease of Admin:** 7.9/10 (Category avg: 8.8/10)
- **Anomaly identification:** 8.1/10 (Category avg: 8.8/10)
- **Automation:** 8.3/10 (Category avg: 8.6/10)
- **Integrations:** 8.3/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Datafold](https://www.g2.com/sellers/datafold)
- **Year Founded:** 2020
- **HQ Location:** New York, US
- **Twitter:** @datafoldcom (1,106 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/datafold/ (33 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 54% Mid-Market, 29% Small-Business




## Parent Category

[IT Management Software](https://www.g2.com/categories/it-management)



## Related Categories

- [Data Quality Tools](https://www.g2.com/categories/data-quality)
- [Database Monitoring Tools](https://www.g2.com/categories/database-monitoring)
- [DataOps Platforms](https://www.g2.com/categories/dataops-platforms)



---

## Buyer Guide

### What You Should Know About Data Observability Software

### Data Observability Software FAQs

### What’s the most recommended customer success platform for SaaS companies?

For software teams prioritizing data accuracy, operational visibility, and rapid issue resolution, top data observability platforms on G2 include:

- [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) focuses exclusively on data observability, providing automated monitoring, anomaly detection, and data lineage to help teams catch and resolve data issues before they impact users.
- [Metaplane](https://www.g2.com/products/metaplane/reviews) delivers end-to-end data observability with features like schema change detection, freshness monitoring, and anomaly detection, enabling teams to maintain data quality effectively.
- [Acceldata](https://www.g2.com/products/acceldata/reviews) combines data quality monitoring, pipeline performance, and infrastructure health insights in one platform, helping software companies ensure their data operations run smoothly at scale.

### What’s the best data observability software for small businesses?

For small businesses aiming to maintain data quality, monitor pipelines, and catch issues early without complex setup, [leading small business data observability solutions](https://www.g2.com/categories/data-observability/small-business) include:

- [Bigeye](https://www.g2.com/products/bigeye/reviews) combines automated anomaly detection and root cause analysis in a platform that makes data reliability accessible without requiring deep technical resources, which is great for scaling teams.
- [IBM Databand](https://www.g2.com/products/ibm-databand/reviews) delivers proactive monitoring and automated alerts while integrating easily with existing data pipelines, making it approachable for growing businesses with limited engineering support.
- [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) offers a powerful but flexible solution with automated monitoring, data lineage, and incident resolution features. Although enterprise-ready, it also provides packages suitable for fast-growing small companies.