Book Image

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

By : Manoj Kukreja
5 (2)
Book Image

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

5 (2)
By: Manoj Kukreja

Overview of this book

In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks.
Table of Contents (17 chapters)
1
Section 1: Modern Data Engineering and Tools
5
Section 2: Data Pipelines and Stages of Data Engineering
11
Section 3: Data Engineering Challenges and Effective Deployment Strategies

Monitoring pipelines

To monitor the hourly runs using the panel on the left-hand side, click on Monitor. Then, click on Trigger runs:

Figure 9.13 – The first trigger run for the Electroniz master pipeline

Notice how the master pipeline successfully runs every hour to ingest the incremental data and recompute the aggregations. Hopefully, this should make Electroniz a very happy customer. As a data engineer, you should be proud of your achievements. Getting this far involves a lot of work.

In an ideal world after the deployment, pipelines should run automatically and seamlessly. The reality is that failures will happen, so you need to be prepared for them well in advance. After all, your pipelines ingest data from varying sources that may or may not be under your direct control. One day, the database that your pipeline ingests from might be down for maintenance, or a REST API that you subscribe to might go offline.

Important

A smart data engineer...