What this book covers
Chapter 1, Modern Data Processing Architecture, provides a significant introduction to designing data architecture and understanding the types of data processing engines.
Chapter 2, Understanding Data Analytics, provides an overview of the world of data analytics and modeling for various data types.
Chapter 3, Apache Spark Deep Dive, provides a thorough understanding of how Apache Spark works and the background knowledge needed to write Spark code.
Chapter 4, Batch and Stream Processing with Apache Spark, provides a solid foundation to work with Spark for batch workloads and structured streaming data pipelines.
Chapter 5, Streaming Data with Kafka, provides a hands-on introduction to Kafka and its uses in data pipelines, including Kafka Connect and Apache Spark.
Chapter 6, MLOps , provides an engineer with all the needed background and hands-on knowledge to develop, train, and deploy ML/AI models using the latest tooling.
Chapter 7, Data and Information Visualization, explains how to develop ad hoc data visualization and common dashboards in your data platform.
Chapter 8, Integrating Continuous Integration into Your Workflow, delves deep into how to build Python applications in a CI workflow using GitHub, Jenkins, and Databricks.
Chapter 9, Orchestrating Your Data Workflows, gives practical hands-on experience with Databricks workflows that transfer to other orchestration tools.
Chapter 10, Data Governance, explores controlling access to data and dealing with data quality issues.
Chapter 11, Building Out the Ground Work, establishes a foundation for our project using GitHub, Python, Terraform, and PyPi among others.
Chapter 12, Completing Our Project, completes our project, building out GitHub actions, Pre-commit, design diagrams, and lots of Python.
