Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka

Key Features

• Develop modern data skills used in emerging technologies
• Learn pragmatic design methodologies such as Data Mesh and data lakehouses
• Gain a deeper understanding of data governance
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake.

Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market.

By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.

What you will learn

• Understand data patterns including delta architecture
• Discover how to increase performance with Spark internals
• Find out how to design critical data diagrams
• Explore MLOps with tools such as AutoML and MLflow
• Get to grips with building data products in a data mesh
• Discover data governance and build confidence in your data
• Introduce data visualizations and dashboards into your data practice

Who this book is for

This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.

Table of Contents

1. Modern Data Processing Architectures
2. Basics of Data Analytics Engineering
3. Cloud Storage and Processing Concepts
4. Python Batch and Stream Processing with Spark
5. Streaming Data with Kafka
6. Python MLOps
7. Python and SQL based Visualizations
8. Integrating CI into your workflow
9. Data Orchestration
10. Data Governance
11. Introduction to Saturn Insurance, Deploying CI and ELT
12. Data Governance and Dashboards

About the Author

Brian Lipp is a Technology Polyglot, Engineer, and Solution Architect with a wide skillset in many technology domains. His programming background has ranged from R, Python, and Scala, to Go and Rust development. He has worked on Big Data systems, Data Lakes, data warehouses, and backend software engineering. Brian earned a Master of Science, CSIS from Pace University in 2009. He is currently a Sr. Data Engineer working with large Tech firms to build Data Ecosystems.