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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

Facilitating data sharing with Delta

JDBC/ODBC connections or HTTP connections via REST APIs are good for sharing modest data but may become a bottleneck for larger datasets. Consider the scenario of sharing curated data with external vendors or partners. There are some firms whose business model is centered around data sharing, such as S&P, Bloomberg, FactSet, Nasdaq, and SafeGraph. They aim to be the source of truth for financial datasets, which every other financial institution will be interested in consuming for downstream analysis and to augment their own datasets. Wouldn't it be nice not to have to copy the data multiple times?

It is best to use cloud storage access directly to avoid unnecessary platform-related bottlenecks. That is what Delta sharing attempts to do – provide an open standard to securely and seamlessly share large volumes of data in Parquet/Delta with a wide variety of consumers and an easy way to govern and audit. Consumers can be from pandas...

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