In a data-driven era, companies need correct and efficient data as their decision-making tool. Nevertheless, data quality and reliability cannot be solved with occasional checks; they need strong data observability. Data observability creates a complete view of information well-being and the condition of the pipeline.
Key Pillars of Data Observability
Data observability is one of the fundamentals of data analysis. Below are the five main pillars of data observability that you should know.
Freshness
Freshness is concerned with the degree of currency of your data. It assists companies in identifying any lag in data pipes so that it can guarantee that stakeholders are operating using the latest and appropriate information.
Data freshness is important since it is used in time-based applications such as real-time data analytics, reporting, and decision-making.
Distribution
The pillar will concentrate on the statistical features of your information, including the range, averages, and frequency of your data.
Monitoring the distribution can detect unusual data, such as large spikes or completely missing values, which might reflect data corruption or higher-level problems.
Volume
Volume ensures that the amount of data entering a system corresponds with the expected amount. Fluctuations in data quantities (drops or surges) may indicate a broken pipeline, ingestion failures, or overlap, all of which may affect analysis and business results.
Schema
The Observed Schema traces the organization of your information, such as tables, common names, and data types. This pillar is necessary because any unexpected change in schema – e.g., a new column, a removed column, etc.-. can affect downstream systems and cause data mismatches etc.
Lineage
Data lineage allows for the tracing of the path of the data source, transformation, and destination. Lineage allows teams to track errors, understand the effects of changes, and increase accountability across systems.
Conclusion
All these pillars collectively make up a stable constituent of data observability, allowing companies to detect, troubleshoot, and avert data challenges well in advance before they impair business processes. Finally, visit Sifflet to learn more about data observability.
