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Data Observability: The Next Frontier of Data Engineering

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Best practices for applying data observability at scale - Barr Moses

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From null values and duplicate rows, to modeling errors and schema changes, data pipelines can break for millions of reasons. In the same way that software developers tackle application downtime, data professionals deal with their own set of availability challenges -- In other words, data downtime, the periods of time when your data is partial, erroneous, missing, or otherwise inaccurate.

The solution? Data observability, the next frontier of data engineering. In this podcast, Barr Moses, CEO and co-founder of Monte Carlo, will discuss how best-in-class data teams are applying an end-to-end approach to data observability to identify, resolve, and prevent broken pipelines, and in the process, restore trust in data.

We'll cover:

  • The rise of “data downtime”
  • What is data observability and why do we need it?
  • Best practices for applying data observability at scale
  • How to set your own data reliability SLAs and SLOs

About the guest:

Barr Moses is CEO & Co-Founder of Monte Carlo, a data reliability company backed by Accel, GGV, Redpoint, and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and among other functions, built the data/analytics team. Prior to that, she was a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science.

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