The Definitive Guide to Data Observability for Analytics and AI

About the Paper

Acceldata_Q12021WP_Cover.PNGExploding data supply and demand are pushing enterprise data pipelines to their limits. Data consumers want to use more data for more use cases from a wider variety of data sources. Enterprise data teams can't keep up. Traditional monitoring tools, built for web applications and micro services, don’t provide insights into distributed data pipelines and processes.

Although increasing investments in engineering, operations, and new tools may help temporarily, it doesn't solve the fundamental problem. The majority of enterprise data teams struggle mightily with daily operational issues. The problem is only getting worse as massive data volumes, data pipeline complexity, and new technologies conspire to undermine the business value of data systems.

This white paper reviews how large enterprises can apply data observability to successfully architect, operate, and optimize complex data systems at scale. Data observability is an emerging approach that monitors and correlates data events across application, data, and infrastructure layers to predict, prevent, and resolve issues before they occur. Companies which employ data observability frameworks and tools improve data performance, cost efficiency, and return on data investment.
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