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

Visualizing data on streaming data frames

When working with streams of data in Structured Streaming data frames, we can visualize real-time data using the display function. This function is different from other visualizing functions because it allows us to specify options such as processingTime and checkpointLocation due to the real-time nature of the data. These options are set in order to manage the exact point in time we are visualizing and should be always be set in production in order to know exactly the state of the data that we are seeing.

In the following code example, we first define a Structured Streaming dataframe, and then we use the display function to show the state of the data every 5 seconds of processing time, on a specific checkpoint location:

streaming_df = spark.readStream.format("rate").load()
display(streaming_df.groupBy().count(), processingTime = "5 seconds", checkpointLocation = "<checkpoint-path>")

Specifically...