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Simplifying Data Engineering and Analytics with Delta

Simplifying Data Engineering and Analytics with Delta

By : Anindita Mahapatra
4.9 (15)
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Simplifying Data Engineering and Analytics with Delta

Simplifying Data Engineering and Analytics with Delta

4.9 (15)
By: Anindita Mahapatra

Overview of this book

Delta helps you generate reliable insights at scale and simplifies architecture around data pipelines, allowing you to focus primarily on refining the use cases being worked on. This is especially important when you consider that existing architecture is frequently reused for new use cases. In this book, you’ll learn about the principles of distributed computing, data modeling techniques, and big data design patterns and templates that help solve end-to-end data flow problems for common scenarios and are reusable across use cases and industry verticals. You’ll also learn how to recover from errors and the best practices around handling structured, semi-structured, and unstructured data using Delta. After that, you’ll get to grips with features such as ACID transactions on big data, disciplined schema evolution, time travel to help rewind a dataset to a different time or version, and unified batch and streaming capabilities that will help you build agile and robust data products. By the end of this Delta book, you’ll be able to use Delta as the foundational block for creating analytics-ready data that fuels all AI/BI use cases.
Table of Contents (18 chapters)
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1
Section 1 – Introduction to Delta Lake and Data Engineering Principles
5
Section 2 – End-to-End Process of Building Delta Pipelines
13
Section 3 – Operationalizing and Productionalizing Delta Pipelines

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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