You're reading from Simplifying Data Engineering and Analytics with Delta

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Published inJul 2022

PublisherPackt

ISBN-139781801814867

Edition1st Edition

Concepts

Big Data

Author (1)

Anindita Mahapatra

Section 3 – Operationalizing and Productionalizing Delta Pipelines

A lot of ML projects fail to see the light of production. Beyond a POC, there are several considerations to take a pipeline to production and ensure it is resilient and runs 24*7, adapting to changes in data patterns and business needs to continuously provide value. Cost and performance are important considerations as data scales. Automation is mandatory to continuously refine and evolve a pipeline. ML models once unleashed are living assets and their life cycle management is yet another layer of the underlying data pipeline that needs to be monitored and managed to ensure they produce the expected ROI for the business.

This section includes the following chapters:

Chapter 11, Operationalizing Data and ML Pipelines
Chapter 12, Optimizing Cost and Performance with Delta
Chapter 13, Managing Your Data Journey

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Simplifying Data Engineering and Analytics with Delta

Published in: Jul 2022Publisher: PacktISBN-13: 9781801814867

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Author (1)

Anindita Mahapatra

Anindita Mahapatra is a Solutions Architect at Databricks in the data and AI space helping clients across all industry verticals reap value from their data infrastructure investments. She teaches a data engineering and analytics course at Harvard University as part of their extension school program. She has extensive big data and Hadoop consulting experience from Thinkbig/Teradata prior to which she was managing development of algorithmic app discovery and promotion for both Nokia and Microsoft AppStores. She holds a Masters degree in Liberal Arts and Management from Harvard Extension School, a Masters in Computer Science from Boston University and a Bachelors in Computer Science from BITS Pilani, India.
Read more about Anindita Mahapatra

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