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 businessesBook DescriptionNavigate the world of data analytics with Fundamentals of Analytics Engineering-guiding you from foundational concepts to advanced techniques of data ingestion and warehousing, data lakehouse, and data modeling. Written by a team of 7 industry experts, this book helps you to transform raw data into structured insights.In this book, you'll discover how to clean, filter, aggregate, and reformat data, and seamlessly serve it across diverse platforms. With practical guidance, you'll also learn how to build a simple data platform using Airbyte for ingestion, DuckDB for warehousing, dbt for transformations, and Tableau for visualization. From data quality and observability to fostering collaboration on codebases, you'll discover effective strategies for ensuring data integrity and driving collaborative success. As you advance, you'll become well-versed with the CI/CD principles for automated code building, testing, and deployment-laying the foundation for consistent and reliable pipelines. And with invaluable insights into gathering business requirements, documenting complex business logic, and the importance of data governance, you'll develop a holistic understanding of the analytics lifecycle.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 designGain insights into the use of cloud-based analytics platforms and tools for scalable data processingUnderstand the principles of data governance and collaborative codingComprehend data quality management in analytics engineeringGain practical skills in using analytics engineering tools to conquer real-world data challengesWho 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.About the AuthorJuan Manuel Perafan 8 years of experience in the realm of analytics (5 years as a consultant). Juan was the first analytics engineer hired by Xebia back in 2020. Making him one of the earliest adopters of this way of working.Besides helping his clients realizing the value of their data, Juan is also very active in the data community. He has spoken at dozens of conferences and meetups around the world (including Coalesce 2023). Additionally, he is the founder of the Analytics Engineering meetup in the Netherlands as well as the Dutch dbt meetupRicardo, an Analytics Engineer with a strong background in data engineering and analysis, is a quick learner and tech enthusiast. With a Master's in IT Management specializing in Data Science, he excels in using various programming languages and tools to deliver valuable insights. Ricardo, experienced in diverse industries like energy, transport, and fintech, is adept at finding alternative solutions for optimal results. As an Analytics Engineer, he focuses on driving value from data through efficient data modeling, using best practices, automating tasks and improving data qualityDumky 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.Tai?s is a versatile data professional with experience in a diverse range of organizations - from big corporations to scale-ups. Before her move to Xebia, she had the chance to develop distinct data products, such as dashboards and machine learning implementations. Currently, she has been focusing on end-to-end analytics as an Analytics Engineer. With a mixed background in engineering and business, her mission is to contribute to data democratization in organizations, by helping them to overcome challenges when working with data at scaleFanny has a multidisciplinary background across various industries, giving her a unique perspective on analytics workflows, from engineering pipelines to driving value for the business.As a consultant, Fanny helps companies translate opportunities and business needs into technical solutions, implement analytics engineering best practices to streamline their pipelines, and treat data as a product. She is an avid promoter of data democratization, through technology and literacy Table of ContentsWhat is Analytics Engineering?The Modern Data StackData IngestionData WarehousesData ModelingData TransformationServing DataHands-on: Building a Data PlatformData Quality & ObservabilityWriting Code in a TeamWriting Robust PipelinesGathering Business RequirementsDocumenting Business LogicData Governance