A Practical Guide for Building an Enterprise Data Lake

Data lakes are the essential technology for tackling the explosive growth of big data volume, velocity, and variety, moving beyond traditional data warehousing to unlock advanced analytics and machine learning.

This comprehensive book begins by clearly defining the differences between the data lake, lake house, and data mesh architectures and immediately addresses critical governance pitfalls and required upskilling before diving into technical implementation. You will learn the discovery process to define data zones and master ingestion using bulk methods and streaming via Apache Kafka to build Lambda architectures. We then detail ad-hoc data discovery and cataloguing with tools like AWS Glue Data Catalog, followed by practical data transformation using PySpark ETL and orchestration tools to ensure data quality rules. The book concludes by showing you how to enable consumption layers for OLAP engines and machine learning, and finally, how to secure the entire platform with strong security, networking, and budget governance.

Upon completing this practical book, you will possess the competency to not only architect and build a scalable data lake but also to strategically expand its value by treating data as a product, making you a highly effective and confident enterprise data lake professional ready for real-world application.

WHAT YOU WILL LEARN
● Differentiate Data Lake, Lake House, Data Mesh, and Data Fabric semantics.
● Design data zones and cost allocation during the discovery process.
● Implement streaming ingestion using Apache Kafka for Lambda architecture.
● Build PySpark ETL/SQL ELT pipelines with orchestration tools for quality.
● Implement security, networking, and monitoring requirements for governance.

WHO THIS BOOK IS FOR
This practical book is ideal for business/product leaders, architects, and solution engineers. Readers should have foundational knowledge of open-source technologies and major cloud environments like AWS, GCP, or Azure.

1. Evolution Towards Modern Data Lakes
2. Understanding Common Pitfalls Making Data Lakes Unsuccessful
3. Performing a Discovery to Build Your Data Lake
4. Bringing Data into Your Data Lake
5. Understanding and Cataloguing Your Data
6. Transforming Data and Making it Consumption Ready
7. Building the Consumption Layer for Data Lake
8. Expanding Your Data Lake by Turning Your Data into a Product
9. Building Your Security and Governance Layer

Sai Srinivas Sriparasa has been working in software development and data architecture domains for more than 16 years, playing central roles in numerous projects as a technical leader and software engineer, delivering projects in North America and Asia Pacific regions for large companies including Amazon Web Services, Google Cloud Platform, Apple Inc., etc. He has helped deploy over 100 data lakes and has helped many customers deploy various governance architectures, including complex data meshes. He has also trained multiple customers and many architects in designing optimal data architectures.