Data accumulation is accelerating, with ~330 million terabytes of data created every day. To put this into perspective, a single terabyte can contain approximately 250,000 hours of music.1 Thus, it becomes challenging to observe, analyze, and get the critical insights from a high amount of data. This is where data observability tools come in.
In this article, we have examined the top 12 data observability tools, based on their capabilities and features to help businesses in their vendor selection to find the best platform that suits their needs.
Data observability vs. data monitoring
Source: Hayden James
Figure 1. Data monitoring vs. data observability
Before delving into the data observability tools capabilities, it’s critical to distinguish between data observability and data monitoring. While both aims to ensure data reliability and quality, their scope and approach differ.
Data monitoring is largely concerned with measuring certain metrics such as data pipeline performance, resource use, and processing times. It frequently takes a reactive strategy, with data teams responding to challenges as they arise.
Data observability, on the other hand, is a more comprehensive and proactive approach to analyzing and controlling data quality. It includes data monitoring but goes above and beyond by offering in-depth insights into the data itself, its lineage, and transformations. Data observability solutions allow data owners to identify and rectify issues before they have an influence on downstream processes and consumers, promoting data quality.
12 data observability tools capabilities
Data observability tools help data engineers to monitor, manage, and analyze their data pipelines, ensuring that data is accurate, timely, and consistent. Some key capabilities of data observability tools include:
1- Data lineage tracking
These tools can trace the origin and transformations of data as it moves through various stages in the data pipeline. This helps data analysts:
- Identify dependencies
- Understand the impact of changes,
- Troubleshoot data quality issues
- Save debugging time.
2- Automated monitoring
Data observability tools can continuously monitor and assess the quality of data based on predefined rules and metrics. This can include anomaly detection, data drift, and identifying data inconsistencies.
3- Real-time & customized alerts
Data observability tools can be integrated with communication platforms (e.g., Slack) and can send instant alerts and notifications to inform data scientists of potential issues.
4- Central data cataloging
These tools can automatically create and maintain a data catalog that documents all available data sources, their schemas, and metadata. This provides a central location for data teams to search and discover relevant data assets.
5- Data profiling
Data observability tools can analyze and summarize datasets, providing insights into the distribution of values, unique values, missing values, and other key statistics. This helps data teams understand the characteristics of their data and identify potential issues.
6- Data validation
These tools can run tests and validations against the data to ensure that it adheres to predefined business rules and data quality standards. This helps increase data health by catching errors and inconsistencies early in the data pipeline.
7- Data versioning
Data observability tools can track changes to data over time, allowing data teams to compare different versions of datasets and understand the impact of changes.
8- Data pipeline monitoring
These tools can monitor the performance and health of data pipelines, providing insights into processing times, resource usage, and potential bottlenecks. This helps data engineers to find and fix bad data to optimize their data pipelines for efficiency and scalability.
9- Collaboration and documentation
Data observability tools often provide collaboration features that allow data teams to share insights, leave comments, and document their findings. This helps foster a data-driven culture within the organization.
10- Integration with external data sources
Data observability tools can typically integrate with a wide range of data sources, processing platforms, and data storage systems, allowing data scientists to monitor and manage their data pipelines from a single unified interface.
11- Analytics & reporting
Data observability technologies can provide a variety of reports and visualizations to assist data teams in understanding the health of their data pipelines and the quality of their data. These findings can help guide decisions and enhance overall data management practices.
12- Instant customer support
Many data observability tools provide extensive customer service via different methods such as chat, email, and phone. Dedicated solutions engineers make sure that data teams have access to expert assistance anytime they encounter difficulties or require instruction on how to use the tool efficiently.
Vendor selection criteria
After identifying whether the vendors provide the capabilities presented above, we narrowed our vendor list based on some criteria. We used the number of B2B reviews and employees of a company to estimate its market presence because these criteria are public and verifiable.
Therefore, we set certain limits to focus our work on top companies in terms of market presence, selecting firms with
- 15+ employees
- 20+ reviews on review platforms including G2, Trustradius, Capterra
The following companies fit these criteria:
- Monte Carlo
- Mozart Data
- Unravel Data
As all vendors offer data cataloging, profiling, validation, versioning, and reporting, we did not include these capabilities in the table. Below you can see our analysis of data capability tools in terms of the capabilities and features mentioned above. You can sort Table 1, for example, by real-time alerting capabilities.
|Quality of support* (out of 10)
|20+ data sources
|Email, Slack, Pagerduty, Opsgenie
24 hour issue response and mitigation with a dedicated support channel
|Pro: $9,900/year with monthly commitment options
|20+ data sources
|Column-level lineage to BI
|Email, Slack, PagerDuty, MS Teams, API, Webhooks
|Shared Slack channel, CSM
|30+ data sources
|Starts from $12,000/year with monthly commitment options
|300+ data sources
|Starts from $15,000/year
|150+ data sources
|Email, Chat, Phone, Zoom support
|20+ data sources
|Unlimited with unsupervised learning
|Email, Slack, Microsoft Teams
|12+ data sources
|Email, Intercom, dedicated Slack channel
|18+ data sources
|Email, Slack, PagerDuty
|Starts from $499 / year
|13+ data sources
|Starts from $1 / per feature
|50+ data sources
|30+ data sources
|20+ data sources
|Email, Slack, PagerDuty, MS Teams, Webhooks
*Based on G2 reviews.
The data is gathered from the websites of vendors. If you believe we have missed any material, please contact us so that we can consider adding it to our article.
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