Fundamentals of Analytics Engineering - Dumky de Wilde

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Résumé :
Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineeringKey Features - Discover how analytics engineering aligns with your organization's data strategy - Access insights shared by a team of seven industry experts - Tackle common analytics engineering problems faced by modern businesses - Purchase of the print or Kindle book includes a free PDF eBookBook Description 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.What you will learn - Design and implement data pipelines from ingestion to serving data - Explore best practices for data modeling and schema design - Scale data processing with cloud based analytics platforms and tools - Understand the principles of data quality management and data governance - Streamline code base with best practices like collaborative coding, version control, reviews and standards - Automate and orchestrate data pipelines - Drive business adoption with effective scoping and prioritization of analytics use casesWho this book is for This 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.Table of Contents - What is Analytics Engineering? - The Modern Data Stack - Data Ingestion - Data Warehouses - Data Modeling - Data Transformation - Serving Data - Hands-on: Building a Data Platform - Data Quality & Observability - Writing Code in a Team - Writing Robust Pipelines - Gathering Business Requirements - Documenting Business Logic - Data Governance...

Biographie:
Dumky is an award-winning analytics engineer with close to 10 years of experience in setting up data pipelines, data models and cloud infrastructure. Dumky has worked with a multitude of clients from government to fintech and retail. His background is in marketing analytics and web tracking implementations, but he has since branched out to include other areas and deliver value from data and analytics across the entire organization.

Sommaire:
Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineeringKey Features - Discover how analytics engineering aligns with your organization's data strategy - Access insights shared by a team of seven industry experts - Tackle common analytics engineering problems faced by modern businesses - Purchase of the print or Kindle book includes a free PDF eBookBook Description 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.What you will learn - Design and implement data pipelines from ingestion to serving data - Explore best practices for data modeling and schema design - Scale data processing with cloud based analytics platforms and tools - Understand the principles of data quality management and data governance - Streamline code base with best practices like collaborative coding, version control, reviews and standards - Automate and orchestrate data pipelines - Drive business adoption with effective scoping and prioritization of analytics use casesWho this book is for This 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.Table of Contents - What is Analytics Engineering? - The Modern Data Stack - Data Ingestion - Data Warehouses - Data Modeling - Data Transformation - Serving Data - Hands-on: Building a Data Platform - Data Quality & Observability - Writing Code in a Team - Writing Robust Pipelines - Gathering Business Requirements - Documenting Business Logic - Data Governance...