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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

Compensating for missing and out-of-range data

There will be cases where some columns may have missing data. The business use case will determine how serious it is and what to do about it. If a field is being used as an input to a model, it needs a data point. Here are some strategies regarding what you can do:

  • Drop the affected records. This is OK when you do not need to use the information for downstream workloads.
  • Flag the row/column by adding a marker value (for example, -1). This allows you to see missing data later on without violating a schema:
  • Perform basic imputing so that you have a "best guess" regarding what the data could have been, often by using the mean of non-missing data: 
    • The following is an example of filling default values for specific columns:
  • The following is an example of using the "average strategy" to impute the values of the specified columns:
...

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