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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 Anindita Mahapatra
Profile icon 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 Other Books You May Enjoy

Provisioning a multi-tenant infrastructure

The administrator is tasked with setting up the infrastructure for the tenants of an environment. One question that often arises is what should be the optimum balance of collaboration and isolation is. Creating single deployments and putting everyone there could lead to hitting rate limits and is not a sustainable strategy. Since we have the luxury of elasticity of the cloud, we can turn on as many environments as we wish to isolate data and users and provide better blast radius control in case of a security breach. Conversely, creating too many environments leads to harder governance and maintenance challenges, collaboration suffers, and the enablement cycle could be much longer.

Let’s examine the various scenarios:

  • Separate development, staging, and production environments.
  • Disaster recovery requires setting a parallel production environment in a different region.
  • Different lines of business units want a separate...
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