What this book covers
Chapter 1, Data Ingestion and Data Extraction with Apache Spark, explores the fundamental processes of data ingestion and extraction using Apache Spark. From connecting to various data sources to efficiently extracting and loading data, you will gain hands-on experience in leveraging Apache Spark’s capabilities for seamless data integration.
Chapter 2, Data Transformation and Data Manipulation with Apache Spark, delves into the transformative power of Apache Spark, focusing on data transformation and manipulation techniques. You will learn how to harness Spark’s robust functionalities for reshaping and optimizing data, ensuring it aligns with specific business requirements and analytical needs.
Chapter 3, Data Management with Delta Lake, delves into Delta Lake, a critical component for effective data management. You will discover how to leverage Delta Lake’s ACID transactions and versioning capabilities to ensure data reliability, consistency, and efficient management within the Lakehouse architecture.
Chapter 4, Ingesting Streaming Data, initiates the exploration of ingesting streaming data using Apache Spark. It covers the basics of streaming data ingestion, setting the stage for understanding real-time data processing and analysis.
Chapter 5, Processing Streaming Data, completes the exploration of streaming data by focusing on advanced techniques and best practices for processing real-time data with Apache Spark. You will gain insights into handling dynamic data streams and maintaining data integrity in dynamic, fast-paced environments.
Chapter 6, Performance Tuning with Apache Spark, delves into the intricacies of performance tuning in Apache Spark. From optimizing code to fine-tuning configurations, you will learn practical strategies to enhance the efficiency and speed of Spark applications, ensuring optimal performance for large-scale data processing.
Chapter 7, Performance Tuning in Delta Lake, builds upon performance tuning principles and focuses specifically on optimizing Delta Lake workflows. You will gain insights into techniques for improving the speed and efficiency of data transactions, making data management within the Lakehouse architecture more performant.
Chapter 8, Orchestration and Scheduling Data Pipeline with Databricks Workflows, guides you through the orchestration and scheduling of workflows in Databricks. From designing automated data pipelines to scheduling tasks efficiently, you will learn how to streamline your data engineering processes and ensure the timely execution of critical workflows.
Chapter 9, Building Data Pipelines with Delta Live Tables, helps you explore the innovative Delta Live Tables, showing how to build robust and dynamic data pipelines. The focus is on leveraging Delta Live Tables to simplify data pipeline development, enhance collaboration, and ensure data consistency in real time.
Chapter 10, Data Governance with Unity Catalog, introduces the concept of data governance using Unity Catalog in Databricks. You will discover how to implement effective data governance practices, including metadata management, data lineage tracking, and access control, to ensure data quality and compliance.
Chapter 11, Implementing DataOps and DevOps on Databricks, addresses the integration of DataOps and DevOps practices within the Databricks environment. You will learn how to implement collaborative and automated development and deployment processes, fostering a culture of continuous improvement and efficiency in data engineering workflows.
