book

Azure Databricks Cookbook

Name: Azure Databricks Cookbook
ISBN: 9781789809718

by Phani Raj, Vinod Jaiswal

September 2021

Beginner to intermediate

452 pages

8h 46m

English

Packt Publishing

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Azure Databricks Cookbook
ContributorsAbout the authorsAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedSectionsGetting readyHow to do it…How it works…There's more…See alsoGet in touchShare Your Thoughts
Chapter 1: Creating an Azure Databricks Service
Technical requirementsCreating a Databricks workspace in the Azure portal Getting readyHow to do it…How it works…Creating a Databricks service using the Azure CLI (command-line interface)Getting readyHow to do it…How it works…There's more…Creating a Databricks service using Azure Resource Manager (ARM) templatesGetting readyHow to do it…How it works…Adding users and groups to the workspaceGetting readyHow to do it…How it works…There's more…Creating a cluster from the user interface (UI)Getting readyHow to do it…How it works…There's more…Getting started with notebooks and jobs in Azure DatabricksGetting readyHow to do it… How it works…Authenticating to Databricks using a PATGetting readyHow to do it…How it works…There's more…
Chapter 2: Reading and Writing Data from and to Various Azure Services and File Formats
Technical requirementsMounting ADLS Gen2 and Azure Blob storage to Azure DBFSGetting readyHow to do it…How it works…There's more…Reading and writing data from and to Azure Blob storageGetting readyHow to do it…How it works…There's more…Reading and writing data from and to ADLS Gen2Getting readyHow to do it…How it works…Reading and writing data from and to an Azure SQL database using native connectorsGetting readyHow to do it…How it works…Reading and writing data from and to Azure Synapse SQL (dedicated SQL pool) using native connectors Getting readyHow to do it…How it works…Reading and writing data from and to Azure Cosmos DB Getting readyHow to do it…How it works…Reading and writing data from and to CSV and Parquet Getting readyHow to do it…How it works…Reading and writing data from and to JSON, including nested JSON Getting readyHow to do it…How it works…
Chapter 3: Understanding Spark Query Execution
Technical requirementsIntroduction to jobs, stages, and tasksGetting readyHow to do it…How it works…Checking the execution details of all the executed Spark queries via the Spark UIGetting readyHow to do it…How it works…Deep diving into schema inferenceGetting readyHow to do it…How it works…There's more…Looking into the query execution planGetting readyHow to do it…How it works…How joins work in Spark Getting readyHow to do it…How it works…There's more…Learning about input partitions Getting readyHow to do it…How it works…Learning about output partitions Getting readyHow to do it…How it works…Learning about shuffle partitionsGetting readyHow to do it…How it works…Storage benefits of different file typesGetting readyHow to do it…How it works…
Chapter 4: Working with Streaming Data
Technical requirementsReading streaming data from Apache KafkaGetting readyHow to do it…How it works…Reading streaming data from Azure Event HubsGetting readyHow to do it…How it works…Reading data from Event Hubs for KafkaGetting readyHow to do it…How it works…Streaming data from log filesGetting readyHow to do it…How it works…Understanding trigger optionsGetting readyHow to do it…How it works…Understanding window aggregation on streaming dataGetting readyHow to do it…How it works…Understanding offsets and checkpointsGetting readyHow to do it…How it works…
Chapter 5: Integrating with Azure Key Vault, App Configuration, and Log Analytics
Technical requirementsCreating an Azure Key Vault to store secrets using the UIGetting readyHow to do it…How it works…Creating an Azure Key Vault to store secrets using ARM templatesGetting readyHow to do it…How it works…Using Azure Key Vault secrets in Azure DatabricksGetting readyHow to do it…How it works…Creating an App Configuration resourceGetting readyHow to do it…How it works…Using App Configuration in an Azure Databricks notebookGetting readyHow to do it…How it works…Creating a Log Analytics workspaceGetting readyHow to do it…How it works…Integrating a Log Analytics workspace with Azure DatabricksGetting readyHow to do it…How it works…
Chapter 6: Exploring Delta Lake in Azure Databricks
Technical requirementsDelta table operations – create, read, and writeGetting readyHow to do it…How it works…There's more…Streaming reads and writes to Delta tablesGetting readyHow to do it…How it works…Delta table data formatGetting readyHow to do it…How it works…There's more…Handling concurrencyGetting readyHow to do it…How it works…Delta table performance optimizationGetting readyHow to do it…How it works…Constraints in Delta tablesGetting readyHow to do it…How it works…Versioning in Delta tablesGetting readyHow to do it…How it works…
Chapter 7: Implementing Near-Real-Time Analytics and Building a Modern Data Warehouse
Technical requirementsUnderstanding the scenario for an end-to-end (E2E) solutionGetting readyHow to do it…How it works…Creating required Azure resources for the E2E demonstrationGetting readyHow to do it…How it works…Simulating a workload for streaming dataGetting readyHow to do it…How it works…Processing streaming and batch data using Structured StreamingGetting readyHow to do it… How it works…Understanding the various stages of transforming dataGetting readyHow to do it…How it works…Loading the transformed data into Azure Cosmos DB and a Synapse dedicated poolGetting readyHow to do it…How it works…Creating a visualization and dashboard in a notebook for near-real-time analyticsGetting readyHow to do it…How it works…Creating a visualization in Power BI for near-real-time analyticsGetting readyHow to do it…How it works…Using Azure Data Factory (ADF) to orchestrate the E2E pipeline Getting readyHow to do it…How it works…
Chapter 8: Azure Databricks SQL Analytics
Technical requirementsHow to create a user in SQL AnalyticsGetting readyHow to do it…How it works…Creating SQL endpoints Getting readyHow to do it…How it works…Granting access to objects to the user Getting readyHow to do it…How it works…Running SQL queries in SQL AnalyticsGetting readyHow to do it… How it works…Using query parameters and filtersGetting readyHow to do it…How it works…Introduction to visualizations in SQL AnalyticsGetting readyHow to do it…Creating dashboards in SQL AnalyticsGetting readyHow to do it…How it works…Connecting Power BI to SQL AnalyticsGetting readyHow to do it…

Chapter 9: DevOps Integrations and Implementing CI/CD for Azure Databricks
Technical requirementsHow to integrate Azure DevOps with an Azure Databricks notebookGetting readyHow to do it…Using GitHub for Azure Databricks notebook version control Getting readyHow to do it…How it works…Understanding the CI/CD process for Azure DatabricksGetting readyHow to do it…How it works…How to set up an Azure DevOps pipeline for deploying notebooksGetting readyHow to do it… How it works…Deploying notebooks to multiple environmentsGetting readyHow to do it…How it works…Enabling CI/CD in an Azure DevOps build and release pipelineGetting readyHow to do it…Deploying an Azure Databricks service using an Azure DevOps release pipelineGetting readyHow to do it…
Chapter 10: Understanding Security and Monitoring in Azure Databricks
Technical requirementsUnderstanding and creating RBAC in Azure for ADLS Gen-2Getting readyHow to do it…Creating ACLs using Storage Explorer and PowerShellGetting readyHow to do it…How it works…How to configure credential passthroughGetting readyHow to do it…How to restrict data access to users using RBACGetting readyHow to do it… How to restrict data access to users using ACLsGetting readyHow to do it…Deploying Azure Databricks in a VNet and accessing a secure storage accountGetting readyHow to do it…There's more…Using Ganglia reports for cluster healthGetting readyHow to do it…Cluster access controlGetting readyHow to do it…
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Content preview from Azure Databricks Cookbook

Chapter 4: Working with Streaming Data

As data ingestion pipelines evolve and change, we see a lot of streaming sources, such as Azure Event Hubs and Apache Kafka, being used as sources or sinks as part of data pipeline applications. Streaming data such as temperature sensor data and vehicle sensor data has become common these days. We need to build our data pipeline application in such a way that we can process streaming data in real time and at scale. Azure Databricks provides a great set of APIs, including Spark Structured Streaming, to process these events in real time. We can store the streaming data in various sinks, including Databricks File System (DBFS), in various file formats, in various streaming systems, such as Event Hubs and Kafka, ...

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Azure Databricks Cookbook

by Phani Raj, Vinod Jaiswal

Chapter 4: Working with Streaming Data

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