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"Big Book of Data Engineering: Your essential guide to data engineering best practices"

As data volume and complexity grow, engineers face challenges managing pipelines with fragmented tools. This guide covers scaling ETL pipelines, orchestrating analytics workloads, implementing observability, and optimizing costs. Includes real-world examples from Healthcare, Financial Services, Retail and Entertainment. Download the guide.

As data volume and complexity increase, engineers are left figuring out how to manage, monitor and maintain fragile pipelines while also handling fragmented tools.

The Big Book of Data Engineering—4th Edition equips you with cutting-edge methods for building pipelines faster and leveraging an intelligent data platform to deliver high-quality data for your AI, BI and analytics workloads.

This practical guide provides an overview of data engineering and the challenges faced today, as well as expert deep dives into:

• Patterns for scaling ETL pipelines effectively
• Orchestrating data, analytics and AI workloads
• Implementing observability for your data pipelines
• Strategies for optimizing Databricks investments and reducing data costs
• Guidance on using Lakeflow and the Data Intelligence Platform to manage data pipelines
• Plus, you’ll discover how companies in Healthcare, Financial Services, Retail and Entertainment are building intelligent batch and streaming data pipelines.

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