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

Automating schema inference

The spark-tensorflow-connector library, which integrates Spark with TensorFlow, supports automatic schema inference when reading TensorFlow records into Spark DataFrames. Schema inference is an expensive operation because it requires an extra reading pass through the data, and therefore it's good practice to specify it as it will improve the overall performance of our pipeline.

The following Python code example demonstrates how we can do this on some test data we create as an example:

  1. Our first step is to define the schema of our data:
    from pyspark.sql.types import *
    path = "test-output.tfrecord"
    fields = [StructField("id", IntegerType()), 
    StructField("IntegerCol", IntegerType()),
    StructField("LongCol", LongType()), 
    StructField("FloatCol", FloatType()),
    StructField("DoubleCol", DoubleType()), 
    StructField("VectorCol", ArrayType(DoubleType(),