Data Observability Tools

Best Data Observability Tools include:

Acceldata and Monte Carlo.

All Products

(1-24 of 24)

1
Informatica Cloud Data Quality

The vendor states that Informatica Data Quality empowers companies to take a holistic approach to managing data quality across the entire organization, and that with Informatica Data Quality, users are able to ensure the success of data-driven digital transformation initiatives and…

2
Cribl Stream

Cribl Stream is a vendor-agnostic observability pipeline used to collect, reduce, enrich, normalize, and route data from any source to any destination within an existing data infrastructure. It is used to achieve full control of an organization's data stream.

3
TimeXtender

TimeXtender was designed to be a holistic solution for data integration that empowers organizations to build data solutions 10x faster using metadata and low-code automation.

4
Datafold
0 reviews

Datafold, from the company of the same name in San Francisco, is a data observability platform that helps companies prevent data catastrophes. It has the ability to identify, prioritize and investigate data quality issues proactively before they affect production.

5
Mozart Data
0 reviews

Mozart Data headquartered in San Francisco helps users get the data stack needed to consolidate, organize, and clean data so it’s ready for analysis. With it, users can go from unstructured, siloed, and messy data to analysis-ready with button clicks, SQL, and a few hours.

6
ThinkData Works

Data is the backbone of effective decision-making. However, varied sources, inconsistent formats, and evolving compliance landscape make it challenging to manage. ThinkData Works provides a catalog platform for discovering, managing, and sharing data from both internal and external…

7
Trackingplan
0 reviews

Collecting and analyzing multiple data points can be a tedious process especially prone to bugs and breakages.Trackingplan’s fully automated data QA and observability solution for digital analytics eliminates these obstacles by detecting errors in websites and apps as soon as they…

8
decube
0 reviews

decube is data observability platform which aims to reduce data quality incidents and improve data reliability. It features ML that monitors crucial datasets & establishes benchmark to evaluate volume, distribution, schema drift & freshness. Its data catalog helps to improve…

9
Metaplane
0 reviews

Metaplane is a data observability platform that helps data teams know when things break, what went wrong, and how to fix it. Built for the modern data stack, Metaplane empowers data teams to start monitoring data in minutes, with the goal of providng data teams the same standard…

10
Cribl.Cloud
0 reviews

Cribl.Cloud provides a dedicated Stream, Edge, and Search environment solution, in a solution whereCribl takes care of the infrastructure management and scaling. The solution is available on Amazon Web Services (AWS), and consumption is handled through credits that can extend across…

11
Digna
0 reviews

Digna is a data quality management solution. It is domain agnostic, meaning it adapts to various sectors, from finance to healthcare. Digna prioritizes data privacy, ensuring compliance with stringent data regulations. Moreover, it's built to scale, growing alongside an organization'…

12
Telmai
0 reviews

Telmai helps data team reduce the time spend on detecting and investigating data quality issues. It offers a low-code / no-code approach, and is a SaaS. The solution boasts high standards of encryption, identity management, role-based access control, data governance, and compliance…

13
Anomalo
0 reviews

To address data quality and other data issues, Anomalo automatically detects data issues as soon as they appear in an organization's data, and before anyone else is impacted. It is used to detect, root-cause, and resolve issues.

14
RightData DataTrust

RightData is a product based company headquartered in Atlanta with products designed to help business and IT teams to help design, config, audit, and automate data testing, reconciliation, and data validation processes.

15
IBM Databand
0 reviews

Databand, an IBM company since the 2022 acquisition, is a proactive data observability platform that helps users monitor and control data’s quality, even when its sources can't be controlled.

16
Cribl Edge
0 reviews

Cribl Edge provides the flexibility and control administrators gain from Stream, now running at the edge. Combined with automatic log discovery and metrics production, Cribl Edge empowers developers and operations teams to discover relevant telemetry hidden in unknown and legacy…

17
5x
0 reviews

5x is presented as a Modern Data Stack Platform that uses API’s to manage data infrastructure. These support automated vendor provisioning, 1-click integrations, consolidated billing, and role based access control. New vendors are added with a single click, and vendors are pre-vetted…

18
Kensu
0 reviews

Kensu is a data observability solution that allows organizations to monitor their data in real-time to cut resolution time in half, and goes further than simply scanning data sources: it monitors data at the source, so users are always in control and can always trust what they deliver.…

19
Calyptia
0 reviews

Calyptia Core helps development teams manage observability programs more cost-effectively by allowing teams to act fast as new data sources come online. Observability pipelines can be added in a matter of minutes using a drag-and-drop interface rather than coding complex configuration…

20
Soda
0 reviews

Soda Data is a data reliability and quality platform that creates observability so data teams can find, analyze, and resolve data issues. Soda aims to make it easy to detect, diagnose, and resolve data issues across the entire data product lifecycle. Usin the practice of data reliability…

21
DataOps.live
0 reviews

The DataOps.live SaaS platform is a solution for Snowflake environment management, end-to-end orchestration, CI/CD, automated testing & observability, and code management, wrapped in a developer interface. The solution aims to drive faster development, parallel collaboration,…

22
Acceldata
0 reviews

Acceldata is an enterprise data observability platform designed to offer users insights into their data stack to improve data and pipeline reliability, platform performance, and spend efficiency.

23
Bigeye Data Observability Platform

Bigeye is a data observability platform that brings data engineers, analysts, scientists, and stakeholders together to build trust in data. The vendor states users among companies like Instacart, Clubhouse, and Udacity use Bigeye to automate monitoring and anomaly detection and create…

24
Monte Carlo
0 reviews

Monte Carlo headquartered in San Francisco, offers what they present as a data observability engine that is designed to reduce data downtime, increase data reliability, and improve trust in company data and access to it.

Learn More About Data Observability Tools

What are Data Observability Tools?

Data observability tools help businesses monitor, trace, and understand both the quality of their data and the overall health of their data stack. Products in this category are installed on top of an existing data stack. These tools are primarily used by database owners and administrators, but they can also be useful for data analysts and other downstream data consumers.

Data observability tools are an evolution of Database Monitoring software, and a complement to Data Quality products. Data observability tools extract and examine metadata from tables, dashboards, and other components of a data stack. They use that metadata to map the data stack, trace data lineage, and begin monitoring for anomalies.

Data observability tools are designed to provide broad monitoring of a data stack, not granular monitoring of individual data entries. These products look at metrics over time to understand trends and baselines in data stack operations. These metrics include metadata like:

  • Number of rows in a table
  • Time since last refresh
  • Number of NULL values in a table
  • Schema changes in a database

In addition to monitoring and alerting, data observability tools help data owners respond effectively to incidents. These products include a variety of visualizations and drill-down interfaces to help diagnose the root cause of a problem. They also use their data lineage map to highlight downstream tables, queries, or dashboards that are affected by an incident. They may even automatically alert downstream consumers that their dashboards are experiencing a problem, informing them that their current data isn’t trustworthy. Used effectively, data observability products help build trust in an organization’s data and make triage and troubleshooting faster and more impactful.

Data Observability vs Data Testing

Data observability tools have some similarities to data testing tools, but their scope is very different. Data testing is focused on preventing errors before they happen. This commonly involves the use of automated test suites that validate specific behaviors in the data stack.

Data observability monitors the operations of the data stack as a whole, without relying on specifically-configured tests. Instead of validating specific individual processes, data observability monitors trends and metadata that indicate likely problems, then points users to the origin of the problem. Data observability tools may include data testing features, but they are supplemental to the overall objective of full-stack awareness.

Data Observability vs Data Quality

Data observability tools do support data quality, but they are different from data quality software. Data quality software is more granular, focusing on the quality of individual data points. These products help standardize data sources, detect and correct poorly-formed or erroneous data, and prevent bad data from being entered.

Data observability products supplement data quality software by monitoring the overall health of the data stack. They focus on trends and metadata rather than the underlying data itself.

Data Observability Tools Features

Most data observability products have the following features:

  • Current-Stack Compatibility: The product can be implemented without modifying the existing data stack.
  • At-Rest Monitoring: The product does not require data extraction, instead monitoring data and metadata in its original context.
  • Self-Configuration: The product requires minimal or no configuration to set up monitoring. It is able to understand most data stack setups, map relationships, and detect anomalies with little manual configuration.
  • Automated Anomaly Detection: The product learns from historical data to flag outliers and anomalies. Users can give feedback on anomalies to help the product learn, but the product does not require users to manually set up detection thresholds.
  • Incident Alerts: The product can send alerts when an incident or anomaly occurs. These alerts may be integrated with third-party applications such as Slack or e-mail.
  • Data Lineage: The product traces and visualizes data lineage, from original tables to downstream consumers, to help users understand the impact of incidents.
  • Downstream Alerting: In addition to alerting data owners, the product can send alerts to downstream consumers if there is an issue with their data.
  • CI/CD Integration: The product integrates with CI/CD or source control tools to detect breaking issues before they impact production. It may also link incidents to relevant code updates to help with root-cause analysis.
  • Usage Analytics: The product includes analytics tools to help understand how and where data is being consumed.

Data Observability Tools Comparison

When selecting a data observability tool, keep the following factors in mind:

Ease of Setup: Most data observability tools are designed to integrate seamlessly with popular data software. However, no product has perfect coverage. Make sure you pick a tool that covers as many components of your data stack as possible without extra configuration.

Business Size: Data observability tools are marketed primarily towards mid-sized companies and large enterprises. However, some vendors may offer free versions for trial or individual usage. If you’re on your own or part of a small team, shop around for data observability products with non-enterprise editions.

Platform Integration: Some broad-scope observability tools include data observability functionality. If you’re in search of enterprise-wide tools that solve more than just data observability, you might get more value from a broad observability platform. Conversely, if you’re only interested in observability for your data stack, don’t pay for a suite of tools you’ll never use.

Start a Data Observability Tools comparison here

Pricing Information

Pricing for most data observability tools is only available via a quote from the vendor. Vendors that do list pricing tend to use a tiered per-month pricing model.

Listed prices range from $300 monthly to over $1,000 monthly depending on feature set and the number of users supported. Free tiers and free trials are available from some vendors.

Related Categories

Frequently Asked Questions

What do Data Observability Tools do?

Data observability tools help organizations monitor and troubleshoot their data stacks. These products alert data owners to issues such as delayed refresh times, schema changes, large changes to data tables, and other non-failstate incidents that can affect data quality.

What are the benefits of using Data Observability Tools?

By using data observability tools, businesses can become aware of data incidents as soon as they happen. Products in this category also include features that help teams respond to incidents quickly and intelligently. These features include third-party alerting, impact analysis, and lineage tracing tools.

What are the best Data Observability Tools?

The following are popular data observability tools:

How much do Data Observability Tools cost?

Pricing for data observability tools can range from free to over $1,000 per month. Most vendors only offer pricing via a quote.