What is Data Observability
Data Observability is an organization’s ability to fully understand the health of the data in their system. It works by applying DevOps Observability best practices to eliminate data downtime. With automated monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues, data observability leads to healthier data pipelines, more productive data teams, and most importantly happier data consumers.
Five Pillars of Data Observability
Freshness
Freshness seeks to understand how up-to-date your data tables are, as well as the cadence at which your tables are updated. Freshness is particularly important when it comes to decision-making; after all, stale data is basically synonymous with wasted time and money.
Distribution
Distribution, in other words, a function of your data’s possible values, tells you if your data is within an accepted range. Data distribution gives you insight into whether or not your tables can be trusted based on what can be expected from your data.
Volume
Volume refers to the completeness of your data tables and offers insights into the health of your data sources. If 200 million rows suddenly turn into 5 million, you should know.
Schema
Changes in the organization of your data, in other words, schema, often indicate broken data. Monitoring who makes changes to these tables and when is foundational to understanding the health of your data ecosystem.
Lineage
When data breaks, the first question is always “where?” Data lineage provides the answer by telling you which upstream sources and downstream investors were impacted, as well as which teams are generating the data and who is accessing it. Good lineage also collects information about the data (also referred to as metadata) that speaks to governance, business, and technical guidelines associated with specific data tables, serving as a single source of truth for all consumers.
Core Elements of a Data Observability
Time-to-value
Does It connect to your existing stack quickly and seamlessly and not require modifying your pipelines, writing new code, or using a particular programming language? If it can connect quickly and seamlessly, you will be able to see the benefits much sooner and maximize your testing coverage without making major investments.
Security-first architecture
Does It monitor your data at rest and not require extracting the data from where it is currently stored? A solution that can monitor data at rest will scale across your data platform and be cost-effective for your organization. Additionally, it ensures that your organization is compliant with the highest security standards.
Minimal configuration
Does It require minimal configuration on your end to get up and running and practically no threshold-setting? A great data observability platform uses ML models to automatically learn your environment and your data. It uses anomaly detection techniques to let you know when things break. It minimizes false positives by considering not just individual metrics, but a holistic view of your data and the potential impact from any issue. As a result, you won’t have to spend valuable engineering resources configuring and maintaining noisy rules. At the same time, it gives you the flexibility to set custom rules for critical pipelines directly in your CI/CD workflow.
Core fundamental foundations
How Microsoft Fabric – Onelake supports Data Observability Framework
Core Elements of a Data Observability supported By Microsoft Fabric which is a real benefit to customers.
Time-to-value
Does It connect to your existing stack quickly and seamlessly and not require modifying your pipelines, writing new code, or using a particular programming language? If it can connect quickly and seamlessly, you will be able to see the benefits much sooner and maximize your testing coverage without making major investments. Microsoft Fabric supports time to value.
Security-first architecture
Does It monitor your data at rest and not require extracting the data from where it is currently stored? A solution that can monitor data at rest will scale across your data platform and be cost-effective for your organization. Additionally, it ensures that your organization is compliant with the highest security standards. Microsoft Fabric supports this feature Security – First.
Minimal Configuration
Does It require minimal configuration on your end to get up and running and practically no threshold-setting? A great data observability platform uses ML models to automatically learn your environment and your data. It uses anomaly detection techniques to let you know when things break. It minimizes false positives by considering not just individual metrics, but a holistic view of your data and the potential impact from any issue. As a result, you won’t have to spend valuable engineering resources configuring and maintaining noisy rules. At the same time, it gives you the flexibility to set custom rules for critical pipelines directly in your CI/CD workflow. Everything is inclusive and integrated into Microsoft fabric hence one tenant deployment and supports data observability.
