Data Observability: An Introductory Guide
This post was written by Talha Khalid, a full-stack developer and data scientist who loves to make the cold and hard topics exciting and easy to understand.
As more companies rely on data insights to drive critical business decisions, data must be accurate, reliable, and of high quality. Gaining insights from data is essential, but so is the data’s integrity so that you can be sure that data isn’t missing, incorrectly added, or misused. This is where data observability comes in.
What is data observability?
You’ll need to access and analyze data from all system parts. This includes application, infrastructure, system, and user data. Collecting data from all these sources makes it possible to gain a complete view of the system and identify areas for improvement. It involves the comprehensive access and analysis of data, including logs, metrics, and traces. Those are the three pillars of data observability.
Logs provide a chronological account of events (time series data) and are crucial for debugging and understanding system behavior.
Metrics offer quantitative data about processes and provide insights into the overall health of a system.
Traces provide detailed information about the execution path within a system and help identify bottlenecks, latency issues, and errors.
By integrating these three types of telemetry data, data observability allows for a complete view of the system. For example, you can use metrics to set up alarms for malfunction notifications, and associated exemplars can help you pinpoint relevant traces. Analyzing logs associated with these traces provides the necessary context to efficiently identify and resolve root causes.
What makes data observability different from monitoring?
Data observability involves continuously adding new metrics to improve the monitoring and optimization of a system’s performance. By implementing data observability practices, organizations can ensure that their systems are reliable, efficient, and effective. Unlike monitoring, data observability provides comprehensive coverage, scalability, and traceability of data, which allows for better analysis of the impact of any changes. Data observability is not solely focused on monitoring data quality. It also provides an overview of all data assets and attributes.
For instance, data monitoring can identify issues such as values falling outside the expected range, improper data updates, or sudden changes in the volume of data being processed. Monitoring generates alerts based on predefined patterns and presents data as aggregates and averages. However, without data observability, it would be impossible to establish these patterns based solely on data testing results.
Data observability facilitates the transition from understanding what is happening to understanding why it’s happening. It not only tracks data, but it also ensures its quality in terms of accuracy, completeness, consistency, timeliness, validity, and uniqueness.
Why data observability is important
Digitization implies management based on quality data that can be trusted, i.e., accurate, relevant, and up to date. Moreover, accuracy is not the only characteristic of data quality. Like food, data has a limited shelf life, the period during which it correctly reflects reality. The world is changing so fast that twenty-four-hour-old data can be hopelessly outdated. And as the number and variety of data sources grow, their expiration date in some domains is a couple of hours. When data is partial, erroneous, missing, or inaccurate, data downtime increases as the ecosystem of sources and consumers becomes more complex.
Data downtime means wasted time and resources for data engineers and developers. For business users, it undermines confidence when making data-driven decisions. However, instead of a comprehensive approach to solving the problem of data downtime, teams often prefer to work in firefighting mode, correcting local data quality flaws on a one-time basis.
This is not in line with DataOps, which is increasingly popular today. Like DevOps, it aims to integrate data development and maintenance processes to improve the efficiency of corporate governance and industry interaction. It does this through distributed collection, centralized analytics, and a flexible policy for accessing information, taking into account its confidentiality, restrictions on use, and integrity.
Just as DevOps uses a CI/CD approach to software development and operations, DataOps aims to enable seamless collaboration between data engineers and data scientists to increase their business value. This aligns with business digitization and eliminates data downtime by applying DevOps best practices to overseeing the data pipeline.
Data observability tools use automated monitoring of data processing pipelines and triage of detected problems with the generation of appropriate alerts to take action to resolve identified incidents quickly. It’s based on the following key components:
Data freshness, i.e., the relevance to the relevant expiration date and frequency of renewal.
Data distribution, i.e., whether it’s within the acceptable range.
Volume as a measure of the completeness of the data. This can give you an idea of the state of the sources. For example, if you normally receive 200 million rows per day and suddenly get only 5 million, you may have problems with the data source or a pipeline bottleneck.
Data schema. This provides the organization with processed and stored structures. Changing the schema is often associated with data corruption. Keeping track of who changes these structures provides a basis for understanding the state of the entire data ecosystem.
Data origin, or lineage. This allows you to understand which upstream data sources and downstream data sinks were affected by a failure such as a schema or scope change. It can also tell you which commands generate the data and who has access to it. In addition, lineage includes information (metadata) related to management, business, and technical advice associated with specific datasheets and serves as a single source of trusted information.
The above components detect data outage incidents as they occur, providing a coherent observability framework for true end-to-end reliability. Data observation solutions not only monitor these components, but they also prevent bad data from entering production pipelines with the following capabilities:
Connecting to an existing stack without changing the data pipelines, developing new code, or using a particular programming language. This lets you quickly recoup costs and ensure maximum testing coverage without significant investments.
Tracking data at rest without actually retrieving it from storage. This allows the data monitoring solution to be performant, scalable, and cost-effective while also ensuring security.
Minimum setting without manually setting thresholds. The best data observation tools should use machine learning models that learn the environment and data automatically. Anomaly detection tools generate alerts in atypical situations, minimizing false positives and relieving the data engineer from having to set up and maintain observation rules.
A broad context that allows you to define key resources, dependencies, and various scenarios on the fly to get deep data observability with little effort, quickly sort and troubleshoot, and communicate effectively with all stakeholders affected by data reliability issues. This generates rich information about data assets, allowing you to make changes responsibly.
Data observability tools
It’s challenging to implement the entire range of requirements for a data observation tool within the framework of one technology. For example, batch and stream processing frameworks such as Apache Spark, Flink, NiFi, AirFlow, Kafka, etc., are often used as the basis for building a big data processing pipeline. However, they don’t provide a complete cycle of maintaining the metadata and generating alerts. Therefore, for data-driven organizations with many sources, receivers, and data processing pipelines, it would be better to choose a ready-made data observability tool like InfluxDB, which offers a single datastore for all time series data with high cardinality and the ability to integrate with different data warehouses.