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)
1
Section 1: Introducing Databricks
4
Section 2: Data Pipelines with Databricks
9
Section 3: Machine and Deep Learning with Databricks

Batching table read and writes

When performing DDL operations such as merge and update on several large tables stored in databases with high concurrency, the transaction log can become blocked and lead to real outages in the data warehouse. All SQL statements are atomic, which means that modifications that take a long time will cause data to be locked for as long as the process is being executed, which can be a problem for real-time databases. To reduce the computational burden of these operations, we can optimize some of them so that they can run on smaller, easier-to-handle batches that only lock resources for brief periods.

Let's see how we can implement batch reads and writes in Delta Lake, thanks to the options provided by the Apache Spark API.

Creating a table

We can create Delta Lake tables either by using the Apache Spark DataFrameWriter or by using DDL commands such as CREATE TABLE. Let's take a look:

  • Delta Lake tables are created in the metastore...