Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering
Key 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 eBook
Book 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 cases
Who 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
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.
Fanny 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
Jovan, an Analytics Engineer, specializes in data modeling and building analytical dashboards. Passionate about delivering end-to-end analytics solutions and enabling self-service analytics, he has a background in business and data science. With skills ranging from machine learning to dashboarding, Jovan has democratized data across diverse industries. Proficient in various tools and programming languages, he has extensive experience with the modern data stack. Jovan enjoys providing trainings in dbt and Power BI, sharing his knowledge generously
Juan 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 meetup
Lasse has been working in the dataspace since 2018, starting out as a Data Engineer at a large airline, then switching towards Cloud Engineering for a consultancy and working for different clients in the retailing and healthcare space. Since 2021, he's an Analytics Engineer at Xebia Data, merging software/platform engineering with analytics passion. As a consultant Lasse has seen many different clients, ranging from retail, healthcare, ridesharing industry, and trading companies. He has implemented multiple data platforms and worked in all three major clouds, leveraging his knowledge of data and analytics to provide value
Ricardo, 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 quality
Taís 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 scale
