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Simplifying Data Engineering and Analytics with Delta

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

Product type Book

Published in Jul 2022

Publisher Packt

ISBN-13 9781801814867

Pages 334 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Anindita Mahapatra

Table of Contents (18) Chapters

Preface

Section 1 – Introduction to Delta Lake and Data Engineering Principles

Chapter 1: Introduction to Data Engineering

Chapter 2: Data Modeling and ETL

Chapter 3: Delta – The Foundation Block for Big Data

Section 2 – End-to-End Process of Building Delta Pipelines

Chapter 4: Unifying Batch and Streaming with Delta

Chapter 5: Data Consolidation in Delta Lake

Chapter 6: Solving Common Data Pattern Scenarios with Delta

Chapter 7: Delta for Data Warehouse Use Cases

Chapter 8: Handling Atypical Data Scenarios with Delta

Chapter 9: Delta for Reproducible Machine Learning Pipelines

Chapter 10: Delta for Data Products and Services

Section 3 – Operationalizing and Productionalizing Delta Pipelines

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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Planning for DR

Planning for DR requires a balance of cost and time needed for a business to recover from an outage. The shorter the time expectation, the more expensive the DR solution.

It is important to understand two key SLAs for the business use case:

Recovery Time Objective (RTO) refers to the duration in which a business is mandated to recover from an outage. For example, if RTO is 1 hour and it is 30 minutes since the outage, then we have 30 more minutes to recover and bring the operations back online without violating the RTO stipulations.
Recovery Point Objective (RPO) refers to the maximum time period of a disruption after which the loss of data collection and processing will exceed the business's agreed-upon threshold. For example, if backup was done in the last hour and the defined RPO is 2 hours, we still have an hour to recover from the disruption to the business.

In the next section, we will see how to use these values of RTO and...

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Authors (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.

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