7 Best Data Observability Tools [2025]

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1. Sifflet

Ratings ⭐⭐⭐⭐⭐

G2 4.4/5

Overview

Sifflet is an AI-native, full-stack data observability platform designed to offer proactive insight into your data enviornment.

It has built-in monitoring and analyzes metadata signals, like lineage, schema evolution, freshness, and usage patterns.

Although Sifflet is designed for data teams and CDOs, it also has no-code options that allow business teams to use the tool and have direct access to their data. Data engineers can make use of its capabilities to monitor and optimize data workflows, while data consumers benefit from enhanced data reliability and accessibility.

Sifflet has 3 invaluable technologies: Data catalog, data monitoring, and data lineage.

By cataloging data assets according to their business criticality, Sifflet helps prioritize monitoring efforts and ensures that the most important data is always being analyzed.

Additionally, Sifflet offers a machine learning engine that helps feed context to data monitoring by registering historical events and patterns in order to prioritize anomalies and not flood inboxes with pointless notifications. This feature allows Sifflet to be the only platform that integrates context-rich metadata.

As an AI-powered data observability tool, Sifflet’s AI copilot is built by 3 AI Agents:

• Forge: Automates pipeline introspection and detects schema anomalies or changes in data flows.
• Sentinel: Provides real-time monitoring and alerts based on behavioral patterns.
• Sage: An AI assistant that contextualizes incidents using metadata and user behavior, offering root-cause suggestions and remediation tips.

Sifflet integrates seamlessly with several data environments, including data lakes, warehouses, transformation layers, orchestration tools, and business intelligence applications, enabling users to retrieve and manage metadata effectively.

✅ Pros

• Combines monitoring, lineage, catalog, and quality
• Can be deployed on the cloud or on premises
• Only tool to offer contextual alerts using metadata and AI
• Suitable for technical and non-technical users

❌ Cons

• Premium pricing may not be ideal for early-stage startups
• Advanced features like Sage require initial metadata setup

2. Monte Carlo

Ratings ⭐⭐⭐⭐⭐

G2 4.4/5

Overview

Monte Carlo is one of the pioneers in data observability. It is well know for its solid incident detection and alerting system.

Monte Carlo’s primary focus is on data reliability, looking to help organizations to reduce downtime and build trust in their analytics environments.

This tool automates monitoring and incident management, aiming to provide full insights into data pipelines with alerts based on statistical anomalies.

Monte Carlo emphasizes more on detection than on cross-functional usability or seamless integration into team workflows.

The platform integrates with major cloud data warehouses and transformation tools, offering automated lineage, field-level monitoring, and incident management. While it lacks deep AI-driven context or metadata intelligence, it’s a strong solution for enterprises looking for a battle-tested observability platform.

Some of Monte Carlo’s features include:

• Automated anomaly detection
• Data quality scoring
• Lineage and impact analysis
• Root cause analysis
• Incident triage

✅ Pros

• Mature data observability tool
• Automated lineage
• Suitable for large teams and enterprises

❌ Cons

• Lacks AI capabilities
• Limited metadata catalog
• May lack flexibility for smaller teams or modern, modular stacks

3. Metaplane

Ratings ⭐⭐⭐⭐⭐

G2 4.8/5

Overview

Metaplane is a new but fast-growing data observability tool created by Datadog, that specializes in automated monitoring with easy and straightforward setup.

Although it does not offer a data catalog or AI features, it is a simple tool, suitable for startups.

Metaplane ingests metadata such as freshness, row count, uniqueness, null rates, and query logs, and uses machine learning to predict future anomalies.

With Metaplane you can receive your alerts on your preferred channel, including Slack and email. To avoid receiving too many alerts at once, Metaplane groups your downstream alerts into a single incidents.

This data observability tool is user friendly and easy to navigate. Additionally, it creates simple views for metrics, alerts, lineage, graphs, and incident history.

Regarding monitoring, Metaplane has cross-system visibility that can compare row counts or fresh data between transactional databases and data warehouses.

Metaplane integrates with Census and Hightouch to monitor data sync pipelines. Using lineage and alerting, Metaplane ensures that issues in warehouse tables move to third-party tools.

✅ Pros

• Easy implementation
• Suitable for small teams due to its pricing
• Integrates with dbt, Snowflake, and Looker

❌ Cons

• Lacks data catalog or native lineage
• Lacks contextual insights from metadata
• Lacks root-cause analysis

4. Bigeye

Ratings ⭐⭐⭐⭐

G2 4.1/5

Overview

Bigeye is a data observability platform that centres around metadata. It is built for data reliability, data quality monitoring and custom metric tracking.

Bigeye automatically monitors, alerts, and helps you resolve data quality issues across your data stacks.

Bigeye’s metadata metrics automatically ingest operational data, such as last refresh time, row counts, and query volumes, by scanning data warehouse query logs. This will help you get a clear view of your entire dataset.

When monitoring your data, Bigeye uses a layered approach. First with wide monitoring across all tables, and then with deep monitoring, diving into dashboards with quality checks and custom logic. This hybrid ensures balanced coverage, detecting both widespread issues and protecting critical assets.

Bigeye offers an automated anomaly detection feature. By training machine learning models for each metric to identify anomalies, such as schema drift or volume shifts, the tools is able to register and improve accuracy in the future.

Bigeye generates column-level lineage and relationships across sources, dbt transformations, and BI dashboards. Therefore, helping in a quick root cause analysis and lineage.

A recent feature includes bigAI, that helps identify root cause, proposes resolutions, and suggests preventive improvements to your pipelines.

However, Sifflet offers additional features such as natural-language monitor creation, meta-driven intelligence, and a fully integrated alerting system enriched with context from lineage, usage, and schema history.

Unlike Bigeye’s code-centric approach, Sifflet emphasizes accessibility and precision, empowering both technical and business users to prevent and resolve data issues faster.

✅ Pros

• Custom metric monitoring
• Simple freshness and volume checks
• Collaborative data investigation tool

❌ Cons

• No AI-powered context
• No built-in data catalog or lineage
• Lacks some integrations

5. Acceldata

Ratings ⭐⭐⭐⭐⭐

G2 4.4/5

Overview

Acceldata is a data observabiloty platform best suited for enterprises that focus on multidimensional data monitoring.

Acceldata covers data pipelines, infrastructure, and costs, making it a good fit for complex enterprises. Its DataPulse engine monitors data reliability, pipeline performance, and use of resources across the stack.

Acceldata offers a customizable AI copilot that helps you define your own data policies and anomaly detection rules. Additionally, this AI assistant helps you set up your monitoring and configuration.

Acceldata structures its offerings into three main groups:

• Torch for data quality and reliability
• Flow for pipeline observability and lineage
• Pulse for infrastructure and performance monitoring

This tools also offers lineage across your data pipelines, transformations, BI reports, and reverse ETL.

Acceldata’s modular design and AI copilot make it a good choice for highly regulated, large-scale data environments. However, it may be too much for smaller teams.

✅ Pros

• Allows hybrid/multi-cloud setups
• Supports big data ecosystems
• Role-based dashboards

❌ Cons

• Steep learning curve
• Lacks AI agents for contextual incident analysis
• Not suitable for small data teams or startups

6. SYNQ

Ratings ⭐⭐⭐⭐⭐

G2 4.7/5

Overview

SYNQ is a fairly new data observability platform. However, in a short period it has managed to pack many features that focus on data quality and reliability monitoring.

SYNQ has automatic and custom monitors that use metadata queries for efficient monitoring. For business-specific needs, it supports custom SQL-based monitors, ideal for tracking unique metrics mid-pipeline.

Some other features include:

• Machine learning and anomaly detection

Anomaly detection uses historical patterns and sensitivity models to predict and understand issues in order to reduce notification noise, and detect any problems in freshness, volume, or schema behavior.

• Ownership activation

Alerts and incidents are sent automatically to responsible team members, and features like BI-status badges or Slack “business alerts” enable cross-functional awareness, even among non-technical users.

• Data quality dashboard

The analytics module tracks test counts, incident metrics, and trend graphs segmented by data product or owner.

This promotes accountability, helps C-level reporting, and supports constant improvement of quality processes.

• Detailed analytics (cost, quality, usage, performance)

With detailed analytics you can measure quality factors including error frequency or resolution time.

Concerning the cost of your activities, SYNQ will inform you of the cost of transforming data, running tests, or making queries.

SYNQ helps you understand which data assets generate the most value by analyzing usage patterns, such as number of queries or the variety of downstream data use cases.

SYNQ offers insights into your product’s data, focusing on intelligent alerting, collaboration, and transformation-level visibility.

However, it doesn’t come without its issues. As a younger platform, it still lacks features such as data catalog or lineage.

✅ Pros

• Real-time alerts and schema monitoring
• Native integration with Fivetran, dbt, Snowflake
• Supports ELT pipelines

❌ Cons

• Lacks lineage and catalog
• Lacks AI and metadata intelligence
• Not suited for large enterprises

7. Splunk

Ratings ⭐⭐⭐⭐

G2 4.3/5

Overview

Splunk isn’t only a data observability tool, it is a full SIEM and data platform that allows you to search, monitor, analyze, and visualize your enterprise’s data.

What does data observability look like with Splunk?

Splunk’s data observability tracks data health and behavior much like logs and metrics.

It does so through advanced data ingestion, lineage tracking, anomaly detection, alerting, and full-stack context.

Splunk’s capture technology (NoSample™ ) promises to leave no blind spots in data behavior or pipeline activity, that can help you detect issues across ingestion, transformation, and user access.

Splunk’s data tools include lineage tracking, business tag integration, and event detectors, that allow you to map out data pipelines and understand who and what relies on data sources.

This tool offers deep AI features and machine learning to allow insight and anomaly detection that enable proactive issue identification, root cause analysis, and intelligent suggestions.

Additionally, alerts are enriched with telemetry context, such as logs, traces, metrics, business impact, and event associations that build intelligent workflows.

Splunk brings a unified, telemetry-first observability experience that extends into data observability. However, organizations focused exclusively on data quality, metadata-driven monitoring, and no-code configuration may find more tailored value in specialized tools like Sifflet.

✅ Pros

• Native integration with the Splunk data platform (clouds, containers, pipelines)
• Machine learning-based anomaly detection
• Business-aware lineage and alert prioritization

❌ Cons

• Requires customization to apply to data observability use cases
• Designed only for IT teams or DevOps
• Data observability capabilities aren't as specialized as other competitors

How can I choose a data observability tool?

Using a data observability tool is crucial to understand your data quality and spot issues proactively to avoid problems flowing through your downstream data.

However, you already know that not all data observability tools are the same and not all of them will be a right fit for your business.

Save this data observability checklist to help you choose:

• Check it integrates with your data platform
• Make sure it has an easy setup and guided demos/onboarding
• Check for features including:
  • Data quality monitoring
  • Data lineage
  • Data catalog
• Make sure you can customize and prioritize alerts and notifications
• Choose a data observability tool that is scalable

Once you’re ready, why not try Sifflet and gain deep insights into your data while solving issues before they have any impact?