Modern Data Architectures with Python: A practical guide to building and deploying data pipelines, data warehouses & data lakes

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

Modern Data Processing Architectures
Basics of Data Analytics Engineering
Cloud Storage and Processing Concepts
Python Batch and Stream Processing with Spark
Streaming Data with Kafka
Python MLOps
Python and SQL based Visualizations
Integrating CI into your workflow
Data Orchestration
Data Governance
Introduction to Saturn Insurance, Deploying CI and ELT
Data Governance and Dashboards