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Fundamentals of Analytics Engineering

Fundamentals of Analytics Engineering

By : Dumky De Wilde, Kassapian, Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira
4.7 (3)
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Fundamentals of Analytics Engineering

Fundamentals of Analytics Engineering

4.7 (3)
By: Dumky De Wilde, Kassapian, Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira

Overview of this book

Written by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.
Table of Contents (23 chapters)
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1
Prologue
Free Chapter
2
Part 1:Introduction to Analytics Engineering
5
Part 2: Building Data Pipelines
11
Part 3: Hands-On Guide to Building a Data Platform
13
Part 4: DataOps
17
Part 5: Data Strategy
21
Index

Data Ingestion

A data platform is useless without any actual data in it. To access your data, combine it with other sources, enrich it, or share it across an organization, you will first need to get that data into your data platform. This is the process we call data ingestion. Data ingestion comes in all sorts and forms. Everyone is familiar with the age-old process of emailing Excel sheets back and forth, but luckily, there are more advanced and consistent ways of adding data to your platform.

Whether clicking your way through a managed ingestion tool such as Fivetran, Stitch, or Airbyte, or writing scripts to handle the parallel processing of multiple real-time data streams in a distributed system such as Spark, learning the steps of a data ingestion pipeline will help you build robust solutions. Building such a solution will help you guarantee the quality of your data, keep your stakeholders happy, and allow you to spend less time debugging and fixing broken code and more time...

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