Organizations collect and analyze vast amounts of data, but it can be difficult to monitor all of the data pipelines, workflows, and overall data quality. That’s where data observability platforms fit into the modern data stack.
The best data observability platforms on the market give you a way to monitor all the data that flows through your company—from data ingestion to analysis and data warehousing. These platforms provide data engineers, data scientists, and data engineering teams with the tools and dashboards they need to quickly monitor for bad data, data downtime events, and anomaly detection.
We’ll quickly go over what data observability is, the business benefits it provides, and how it leads to improved data quality. Then we’ll cover the pros and cons of the best data observability platforms on the market. Let’s start with a simple definition of data observability.
What is data observability?
Data observability is the ability to monitor and understand all the data that flows through an organization’s distributed systems. It provides a way to track data as it moves through various data pipelines and workflow stages, travels through data science pipelines, and moves from many sources to unified data warehouse solutions like Snowflake. This includes monitoring the data itself, as well as the processes and systems that handle data workflows.
Data observability tools give companies the ability to detect and diagnose a wide range of data issues—e.g., errors in automated data jobs, schema and format inconsistencies, missing values, or poor data reliability. Some of the key components of data observability include data monitoring, data tracing, data profiling, and data visualization.
What are the benefits of data observability?
Data observability provides a number of benefits for data science teams and businesses. One of the main benefits is the ability to improve the efficiency and effectiveness of data operations while improving overall data quality.
By monitoring and understanding the data, organizations can detect and diagnose issues quickly and easily, which leads to faster resolution and less downtime. It also leads to increased trust when business partners who rely on the data to make decisions are notified of potential delays and kept updated about what’s happening and when to expect their data.
Data observability also allows for better decision-making. By being able to track and understand the data, organizations can more easily identify trends and patterns, which informs decision-making.
Here are the main benefits of using a data observability platform:
- Improved efficiency and effectiveness of data operations by swiftly identifying and resolving issues
- Increased accuracy and reliability of data analytics by identifying and correcting errors or inconsistencies
- Better decision-making by identifying trends and patterns in the data and improving machine learning capabilities
- Regulatory compliance by understanding and tracking data flows through your data stack
- Improved data security through real-time monitoring and alerting of suspicious data access or modification
- Root cause analysis of data-related issues by tracing data through the data pipeline
- Increased transparency and control over data processing and use by understanding and visualizing data flows
Most data observability platforms on the market help you get those benefits for your company and data science teams. Here are some of our favorites, in no particular order.
What are the 5 best data observability platforms?
Datadog is a cloud-based platform that provides real-time monitoring and alerting for data, systems, and applications. It provides a comprehensive view of an organization's infrastructure, applications, and logs. Teams can then identify and resolve issues in real time.
The platform collects and aggregates data from a variety of sources—including servers, containers, APIs, SQL databases, and cloud services—before presenting results in an easy-to-use dashboard.
- Collect and visualize metrics from various sources—including applications, servers, containers, and cloud resources.
- Trace requests and transactions across distributed systems and services to identify performance bottlenecks.
- Automatically detect and alert on abnormal behavior in metrics and logs.
- Create custom dashboards to visualize metrics and logs.
- Create and manage alerts based on metric thresholds and custom conditions.
- Create and run synthetic transactions to test the availability and performance of web applications and APIs.
- User-friendly interface that makes it easy to navigate and use.
- Provides real-time monitoring of metrics and traces.
- Detects and alerts on anomalies in the data.
- Integrates with a wide range of technologies and tools—including AWS, GCP, Kubernetes, and more.
- Application Performance Management (APM) for distributed tracing.
- Limited analytics and visualization capabilities compared to other platforms.
- Datadog log management is not as advanced as other platforms, like Splunk.
Datadog has a wide variety of plans that vary from monthly fees that are priced by host, session, active function, etc. They have everything from a free infrastructure plan to cloud security management plans.
Splunk is a data observability platform that helps organizations collect, analyze, and visualize data from various sources in real time. The platform allows organizations to gain insights from their data and make data-driven decisions.
Splunk collects and processes data from a wide range of sources, including log files, network traffic, and application performance metrics. The platform also provides advanced analytics and visualization capabilities, including machine learning, natural language processing, and predictive modeling.
- Collect data from various sources—including logs, metrics, traces, and events.
- Quickly and easily find the information you need within your data.
- Wide range of visualization options—including charts, tables, and maps.
- Set up alerts and reports to notify you when certain conditions are met.
- Identify patterns and anomalies within your data.
- Process and analyze large volumes of data in real time.
- Advanced analytics and visualization capabilities are available—including machine learning, natural language processing, and predictive modeling.
- Integrates with a wide range of third-party tools and technologies.
- Security and compliance features like threat intelligence, security information and event management (SIEM), and incident response are available.
- Complex to set up and use for new users.
- Challenging data governance for organizations with strict compliance requirements.
Splunk has a wide variety of plans to fit your needs. Their Observability Cloud packages come with different levels of service, depending on the features you’re looking for.
Elastic is a data observability platform that helps organizations collect, analyze, and visualize data from various sources in real time. The platform provides a powerful and flexible solution for gaining insights from large amounts of data and making data-driven decisions.
Elasticsearch, Logstash, and Kibana (ELK stack) are the core of the Elastic platform.
- Collect and process data from a wide range of sources in real time—including log files, network traffic, and application performance metrics.
- Kibana, the visualization and dashboard tool of Elastic, allows users to create interactive visualizations, charts, and dashboards.
- Machine learning capabilities analyze and predict data patterns, identify anomalies and outliers, and create automated alerts.
- Security and compliance features are available such as role-based access control, data encryption in transit and at rest, and compliance with various regulations like SOC2, PCI, and HIPAA.
- Application Performance Management (APM) monitors the performance of your application.
- Handles large amounts of data and scales to meet the needs of your business and DataOps.
- Advanced search and analytics capabilities allow users to quickly find and analyze relevant data.
- Integrates with a wide range of technologies and platforms, making it easy to incorporate data from various sources.
- Open-source can be more cost-effective than other proprietary platforms and allows for more customization and integration with other open-source tools.
- Can be challenging to set up and use, especially for organizations that are new to the platform.
- Elastic can be complex to manage and maintain for organizations that collect and process large amounts of data.
- Kibana, the visualization tool of Elastic, provides a wide range of visualization options but not the advanced visualization features available in tools like Tableau.
Elastic Cloud data observability plans range from $95 per month to $175 per month.
4. New Relic
New Relic is a data observability platform that provides a comprehensive view of an organization's technology stack, including applications, infrastructure, and logs. It enables organizations to monitor their systems in real time and troubleshoot issues quickly.
- Application Performance Management (APM) monitors the performance of data applications to identify bottlenecks and errors.
- Monitor servers, containers, and cloud services, and identify issues with physical infrastructure.
- Simulate user interactions with applications and ensure they’re performing as expected.
- Alerts and notifications notify teams of issues or anomalies in their systems.
- Gives a comprehensive view of your technology stack—including applications, infrastructure, and logs.
- Collect, analyze, and visualize log data from different sources in real time.
- Transparent pricing ensures you only pay for what you use.
- New Relic can have a steep learning curve for new users.
- Limited integrations with other tools can make it difficult to incorporate data from some sources into the platform.
New Relic has a simple and transparent pricing structure that lets you pay based on data and user seats. You can start for free with 100GB per month of data and one user, and go all the way up to an enterprise plan with custom pricing.
Prometheus is an open-source data observability platform that helps organizations collect, analyze, and visualize metrics and time-series data. It is a monitoring and alerting system that allows teams to collect and store data, query that data, and trigger alerts based on the results of those queries.
Prometheus is designed for monitoring and alerting for cloud-native applications, it is also widely used for monitoring other systems as well.
- Collects, stores, and monitors metrics in real time.
- Detects and alerts on anomalies in the data, making it easy to identify potential problems.
- Built-in service discovery allows for automatically discovering and monitoring targets, which makes it easy to monitor distributed systems.
- Open-source platform that can be more cost-effective than other proprietary platforms and enables more customization and integration with other open-source tools.
- Prometheus provides a powerful query language, PromQL, which allows teams to perform complex queries on the stored data and create alerts based on the results.
- Prometheus integrates with Grafana to create interactive dashboards and visualizations of metrics data.
- Uses a multi-dimensional data model that allows teams to store and query metrics with different dimensions and labels.
- Prometheus's indexing is not as powerful as other solutions like Elasticsearch.
- Prometheus doesn't have any built-in data retention mechanism, which means that you will need to use third-party tools like Thanos.io to handle retention.
Prometheus is 100% open-source and free to use, you just have to invest the time in building it out.
Data pipelines and workflows are the foundation of what data observability platforms monitor. If you also need help getting your data automation in order, Shipyard integrates with data observability platforms and helps you modernize your legacy systems.
If you want to build data workflows that are easier for observability platforms to monitor, sign up to demo the Shipyard app with our free Developer plan—no credit card required.