Best data observability software

Here are some of the best data observability software products from G2’s data observability software category page.

Monte Carlo is known for its proactive alerting and automated root cause analysis, helping teams identify broken data pipelines before business impact occurs. It’s an excellent choice for companies managing complex, distributed data systems at scale.

2. Metaplane – Best for Lightweight Monitoring in Modern Data Stacks

Metaplane is praised for its quick setup and tight integration with tools like dbt, Snowflake, and Looker. It fits well in data teams looking for a “plug-and-observe” solution to detect schema changes and missing values instantly.

3. Acceldata – Best for Multi-Layered Pipeline and Infrastructure Visibility

Acceldata stands out for correlating infrastructure metrics with data quality signals, giving both data engineers and SREs a full operational view. It’s a go-to for organizations that want observability across ingestion, processing, and delivery layers.

4. DQLabs – Best for Self-Healing Data Workflows with AI Assistance

DQLabs automates data profiling, scoring, and enrichment using machine learning to continuously assess and improve data assets. It's especially useful for teams tasked with cleaning messy, high-volume data without manual intervention.

5. SYNQ – Best for Operationalizing Data Ownership Within Teams

SYNQ emphasizes collaboration by assigning clear data ownership and tying data incidents to individuals or teams. This is ideal for product-led data orgs who want accountability built directly into their observability platform.

6. SquaredUp – Best for Centralizing Business Metrics and Platform Signals

SquaredUp unifies key metrics across data platforms and apps into visual dashboards designed for both engineering and business users. It’s great for teams tired of switching between tools to track what’s going on in their environment.

7. Telmai – Best for Real-Time Pattern Shifts and Behavioral Anomalies

Telmai specializes in behavioral drift detection, letting users identify subtle shifts in how data behaves even when it doesn't outright fail. It’s well-suited for data science teams focused on keeping model inputs and analytical outputs trustworthy.

8. Validio – Best for Custom Thresholds and Rule-Based Quality Automation

Validio enables teams to define fine-grained validation rules and monitor multiple data layers—raw, transformed, or output. It’s a solid option for companies needing control over how quality thresholds are enforced across environments.

These tools provide a range of features to help organizations monitor and maintain data health effectively. 

I want to start a discussion with this expert software community to identify the best data observability software. Monte Carlo, Metaplane, and Acceldata are some of the top choices. Have you recently used any of these top data observability software solutions on G2? Let me know in the comments!