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Solving Spark data observability & optimization with definity

Solving Spark data observability & optimization with definity

Apache Spark is powerful, but let’s be honest - it can also be a nightmare to run in production, chasing data incidents, pipeline issues, and inefficient performance… Data monitoring is not enough anymore, we need full-stack data observability.

In this live webinar, Andreas Kretz and Roy Daniel explore some of the biggest challenges that data engineering teams developing Spark pipelines at high-scale are facing today, and walk through live examples of how definity helps to solve them, for example:

  • Monitoring data & pipelines in real-time and proactively preventing data incidents
  • Minimizing data engineers’ effort and expertise to debug issues
  • Cutting platform costs and optimizing pipeline run-times effectively
  • Accelerating platform upgrades and code-changes with CI validation