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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
1
Section 1: Introducing Databricks
Section 1: Introducing Databricks
Free Chapter
2
Chapter 1: Introduction to Azure Databricks
Chapter 1: Introduction to Azure Databricks
Technical requirements
Introducing Apache Spark
Introducing Azure Databricks
Discovering core concepts and terminology
Interacting with the Azure Databricks workspace
Using Azure Databricks notebooks
Exploring data management
Exploring computation management
Exploring authentication and authorization
Summary
3
Chapter 2: Creating an Azure Databricks Workspace
Chapter 2: Creating an Azure Databricks Workspace
Technical requirements
Using the Azure portal UI
Examining Azure Databricks authentication
Working with VNets in Azure Databricks
Azure Resource Manager templates
Setting up the Azure Databricks CLI
Summary
4
Section 2: Data Pipelines with Databricks
Section 2: Data Pipelines with Databricks
5
Chapter 3: Creating ETL Operations with Azure Databricks
Chapter 3: Creating ETL Operations with Azure Databricks
Technical requirements
Using ADLS Gen2
Using S3 with Azure Databricks
Using Azure Blob storage with Azure Databricks
Transforming and cleaning data
Orchestrating jobs with Azure Databricks
Scheduling jobs with Azure Databricks
Summary
6
Chapter 4: Delta Lake with Azure Databricks
Chapter 4: Delta Lake with Azure Databricks
Technical requirements
Introducing Delta Lake
Ingesting data using Delta Lake
Batching table read and writes
Streaming table read and writes
Summary
7
Chapter 5: Introducing Delta Engine
Chapter 5: Introducing Delta Engine
Technical requirements
Optimizing file management with Delta Engine
Optimizing queries using DFP
Using Bloom filters
Optimizing join performance
Summary
8
Chapter 6: Introducing Structured Streaming
Chapter 6: Introducing Structured Streaming
Technical requirements
Structured Streaming model
Using the Structured Streaming API
Using different sources with continous streams
Triggering streaming query executions
Visualizing data on streaming data frames
Example on Structured Streaming
Summary
9
Section 3: Machine and Deep Learning with Databricks
Section 3: Machine and Deep Learning with Databricks
10
Chapter 7: Using Python Libraries in Azure Databricks
Chapter 7: Using Python Libraries in Azure Databricks
Technical requirements
Installing libraries in Azure Databricks
PySpark API
pandas DataFrame API (Koalas)
Visualizing data
Summary
11
Chapter 8: Databricks Runtime for Machine Learning
Chapter 8: Databricks Runtime for Machine Learning
Loading data
Feature engineering
Time-series data sources
Handling missing values
Extracting features from text
Training machine learning models on tabular data
Summary
12
Chapter 9: Databricks Runtime for Deep Learning
Chapter 9: Databricks Runtime for Deep Learning
Technical requirements
Loading data for deep learning
Managing data using TFRecords
Automating schema inference
Using Petastorm for distributed learning
Reading a dataset
Data preprocessing and featurization
Summary
13
Chapter 10: Model Tracking and Tuning in Azure Databricks
Chapter 10: Model Tracking and Tuning in Azure Databricks
Technical requirements
Tuning hyperparameters with AutoML
Automating model tracking with MLflow
Hyperparameter tuning with Hyperopt
Optimizing model selection with scikit-learn, Hyperopt, and MLflow
Summary
14
Chapter 11: Managing and Serving Models with MLflow and MLeap
Chapter 11: Managing and Serving Models with MLflow and MLeap
Technical requirements
Managing machine learning models
Model Registry example
Exporting and loading pipelines with MLeap
Serving models with MLflow
Summary
15
Chapter 12: Distributed Deep Learning in Azure Databricks
Chapter 12: Distributed Deep Learning in Azure Databricks
Technical requirements
Distributed training for deep learning
Using the Horovod distributed learning library in Azure Databricks
Using the Spark TensorFlow Distributor package
Summary
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16
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Chapter 1: Introduction to Azure Databricks

Modern information systems work with massive amounts of data, with a constant flow that increases every day at an exponential rate. This flow comes from different sources, including sales information, transactional data, social media, and more. Organizations have to work with this information in processes that include transformation and aggregation to develop applications that seek to extract value from this data.

Apache Spark was developed to process this massive amount of data. Azure Databricks is built on top of Apache Spark, abstracting most of the complexities of implementing it, and with all the benefits that come with integration with other Azure services. This book aims to provide an introduction to Azure Databricks and explore the applications it has in modern data pipelines to transform, visualize, and extract insights from large amounts of data in a distributed computation environment.

In this introductory chapter, we will explore these topics:

  • Introducing Apache Spark
  • Introducing Azure Databricks
  • Discovering core concepts and terminology
  • Interacting with the Azure Databricks workspace
  • Using Azure Databricks notebooks
  • Exploring data management
  • Exploring computation management
  • Exploring authentication and authorization

These concepts will help us to later understand all of the aspects of the execution of our jobs in Azure Databricks and to move easily between all its assets.

Previous Section
End of Section 1
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