Data observability products monitor data quality and workload performance to ensure the delivery of accurate, timely data for analytics. Data engineers should evaluate data observability products by their breadth of capabilities, governance, heterogeneous integration, ease of use, and scale and performance.

Key Takeaways

• Real-time business intelligence (BI) projects, self-service, data science projects, and the rise of data-driven applications make enterprise data architectures more complex by the day. Data engineers struggle to deliver high volumes of accurate, timely data across heterogeneous hybrid and multi-cloud environments.
• Data observability products can help overcome these challenges by monitoring and optimizing analytics workloads across the applications, data, and infrastructure layers in enterprise environments.
• Selected and implemented well, a data observability product can support use cases for data governance, data engineering, and business planning.
• Data engineering teams and their managers should evaluate data observability products according to five criteria: breadth of capabilities, governance, heterogeneous integration, ease of use, and scale and performance.
• Consider these guiding principles as you review your short list of products:
  • Define your baseline requirements. Assemble those “must-have” capabilities in each of the five evaluation criteria. Filter out the data observability products that fail to address these requirements.
  • Stress-test candidate products. During your proof of concept, test the ability of short-listed products to address your stretch goals, especially as they relate to heterogeneous integration, scale, and performance.
  • Listen to your full team. Both entry-level users and power users need to become more productive with your data observability product.

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