The New Essential Guide

to Data Engineering

With AI being woven into the fabric of every business, data has never been more important. There is, after all, no AI strategy without a data strategy.

But where does a data strategy start? With a vision — and a strong data engineering function.

Simply put: Data engineers are the lifeblood of any data-driven organization. They accelerate time to value and clear bottlenecks that slow productivity. Getting data pipelines AI ready is critical to be able to leverage AI and ML models and to adopt agentic workflows. So it is essential to understand and reevaluate the business impact of data engineering.

This guide is designed to help you do just that. In it, you will discover:

• How to achieve transactional integrity and governance by embracing open standards and architectures, such as the data lakehouse and Apache Iceberg™.
• Strategies to modernize legacy pipelines by onboarding existing Spark workloads to a managed engine, driving performance optimization and significant cost savings.
• How to build efficient pipelines — with guidance on when to choose ETL vs. ELT or batch vs. streaming and how to design for real-time performance without breaking the bank.
• The principles of DataOps to streamline data lifecycle management, ensure verifiable, high-quality information and reduce pipeline downtime.
• Practical approaches to manage all data modalities (structured, semistructured and unstructured) within a single unified platform so AI teams can move from proof of concept to production.

Covering everything from basics to best practices, this updated Essential Guide to Data Engineering provides a clear blueprint to help reduce tool sprawl, improve reliability and make your data — your organization — AI ready.