The ever-increasing influx of data from diverse sources has become a significant challenge for organizations and their data engineers to continuously manage using incumbent, outdated tool stack that lack flexibility. Due to the lack of control over the data structures provided by external sources, organizations struggle to identify and respond to changes in data, which can be catastrophic for downstream analysis and decision-making by business users. All these issues point to a reality that without effective data observability, companies will struggle to treat data as an asset.
- 1. Data monitoring and alerting: detect patterns and anomalies, and alerts are generated when issues arise. The process also involves validating the quality, consistency, and completeness of data while ensuring that it is readily accessible to those who require it. This is done by embedding data quality checks in data pipelines. These embedded data quality checks search for patterns and anomalies, and generate alerts when problems occur. They also involve tracking and detection of schema drift, data change, pipeline run-time, frequency, and identifying bottlenecks or other issues that can impact the flow of data.
- 2. Observing data infrastructure: monitors metrics such as compute, storage, memory utilization, and network traffic. This is accomplished by monitoring databases, storage systems spread across on-prem and in private and public clouds, and identifying issues that can impact the performance and availability of data.
- 3. Data usage: observes metrics like query performance, user behaviour, and data access patterns. It also identifies any problems that can affect the efficiency and effectiveness of data-driven decision-making based on the utilization of stakeholders such as data analysts, data scientists, and business users.
- 4.Utilization and cost monitoring: tracks expenses related to the management of data pipelines, such as infrastructure and storage costs, as well as resource consumption. The approach also involves identifying opportunities to save costs and optimizing the utilization of resources to maintain high performance and reliability of data pipelines and systems.
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