Book Image

Distributed Data Systems with Azure Databricks

By : Alan Bernardo Palacio
Book Image

Distributed Data Systems with Azure Databricks

By: Alan Bernardo Palacio

Overview of this book

Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
Table of Contents (17 chapters)
Section 1: Introducing Databricks
Section 2: Data Pipelines with Databricks
Section 3: Machine and Deep Learning with Databricks

Introducing Delta Lake

Using a data lake has become the de facto solution for many data engineering tasks. This storage layer is composed of files that can be arranged in a historical way instead of tables in a data warehouse. This has the benefit of decoupling storage from computing, which is the great advantage of data lakes. They are much cheaper than a database. The data that's stored in the data lake has no primary and foreign keys, making it hard to extract the information stored on it. Therefore, data lakes are seen as a solution where we only append new data. When trying to query or delete records, we need to go through all the files in the data lake, which could be a very resource-intensive and slow task.

This leads to data lakes being hard to update, and they may have problems when we try to use them in cases where data needs to be frequently queried. This includes customer or transactional data, financial applications that require robust data handling, or when we...