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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

Automating Workflows

In the previous chapter, you got acquainted with challenges surrounding ownership and accountability in a development team, ensuring coding standards are adhered to and task tracking. We had a look at how to do a code review and got to know some best practices around writing documentation for easily onboarding new colleagues. We briefly mentioned Continuous Integration/Continuous Deployment or CI/CD as a way to do functionality tests and formatting checks on your code and deploy changes into production in an automated fashion. In this chapter, you will learn about data orchestration and automating workflows, and we will give you all the details you need to know to successfully build a CI/CD pipeline as an analytics engineer.

By the end of this chapter, you will have gained a comprehensive understanding of several key concepts and practices. Firstly, you will understand the critical role of CI/CD within the realm of DataOps. Additionally, you will have a clear...

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