Fundamentals of Analytics Engineering (eBook)

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.

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Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineeringKey FeaturesDiscover how analytics engineering aligns with your organization's data strategyAccess insights shared by a team of seven industry expertsTackle common analytics engineering problems faced by modern businessesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten 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.What you will learnDesign and implement data pipelines from ingestion to serving dataExplore best practices for data modeling and schema designScale data processing with cloud based analytics platforms and toolsUnderstand the principles of data quality management and data governanceStreamline code base with best practices like collaborative coding, version control, reviews and standardsAutomate and orchestrate data pipelinesDrive business adoption with effective scoping and prioritization of analytics use casesWho this book is forThis book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.]]>