Azure Synapse Analytics Cookbook: Implement a limitless analytical platform using effective recipes for Azure Synapse

by Gaurav Agarwal, Meenakshi Muralidharan, Rohini Srivathsa

Length: 238 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-04-29
ISBN-10: 1803231505
ISBN-13: 9781803231501
Sales Rank: #766797 (See Top 100 Books)

Whether you’re an Azure veteran or just getting started, get the most out of your data with effective recipes for Azure Synapse

Key Features

Discover new techniques for using Azure Synapse, regardless of your level of expertise
Integrate Azure Synapse with other data sources to create a unified experience for your analytical needs using Microsoft Azure
Learn how to embed data governance and classification with Synapse Analytics by integrating Azure Purview

Book Description

As data warehouse management becomes increasingly integral to successful organizations, choosing and running the right solution is more important than ever. Microsoft Azure Synapse is an enterprise-grade, cloud-based data warehousing platform, and this book holds the key to using Synapse to its full potential. If you want the skills and confidence to create a robust enterprise analytical platform, this cookbook is a great place to start.

You’ll learn and execute enterprise-level deployments on medium-to-large data platforms. Using the step-by-step recipes and accompanying theory covered in this book, you’ll understand how to integrate various services with Synapse to make it a robust solution for all your data needs. Whether you’re new to Azure Synapse or just getting started, you’ll find the instructions you need to solve any problem you may face, including using Azure services for data visualization as well as for artificial intelligence (AI) and machine learning (ML) solutions.

By the end of this Azure book, you’ll have the skills you need to implement an enterprise-grade analytical platform, enabling your organization to explore and manage heterogeneous data workloads and employ various data integration services to solve real-time industry problems.

What you will learn

Discover the optimal approach for loading and managing data
Work with notebooks for various tasks, including ML
Run real-time analytics using Azure Synapse Link for Cosmos DB
Perform exploratory data analytics using Apache Spark
Read and write DataFrames into Parquet files using PySpark
Create reports on various metrics for monitoring key KPIs
Combine Power BI and Serverless for distributed analysis
Enhance your Synapse analysis with data visualizations

Who this book is for

This book is for data architects, data engineers, and developers who want to learn and understand the main concepts of Azure Synapse analytics and implement them in real-world scenarios.

Azure Synapse Analytics Cookbook
Foreword
Contributors
About the authors
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
    Share Your Thoughts
Chapter 1: Choosing the Optimal Method for Loading Data to Synapse
    Choosing a data loading option 
        Getting ready
        How to do it…
        How it works…
        There's more…
    Achieving parallelism in data loading using PolyBase
    Moving and transforming using a data flow
        Getting ready
        How to do it…
        How it works…
    Adding a trigger to a data flow pipeline
        Getting ready
        How to do it…
        How it works…
    Unsupported data loading scenarios
        How to do it…
        There's more…
    Data loading best practices
        How to do it…
Chapter 2: Creating Robust Data Pipelines and Data Transformation
    Reading and writing data from ADLS Gen2 using PySpark
        Getting ready
        How to do it…
        How it works…
    Visualizing data in a Synapse notebook
        Getting ready
        How to do it…
        How it works…
Chapter 3: Processing Data Optimally across Multiple Nodes
    Working with the resource consumption model of Synapse SQL
        Architecture components of Synapse SQL
        Resource consumption
    Optimizing analytics with dedicated SQL pool and working on data distribution
        Understanding columnstore storage details
        Knowing when to use round-robin, hash-distributed, and replicated distributions
        Knowing when to partition a table
        Checking for skewed data and space usage
        Best practices
        Workload management for dedicated SQL pool
    Working with serverless SQL pool
        Getting ready
        How to do it…
        There's more…
    Processing and querying very large datasets
        Getting ready
        How to do it…
    Script for statistics in Synapse SQL 
        How to do it…
        There's more…
Chapter 4: Engineering Real-Time Analytics with Azure Synapse Link Using Cosmos DB
    Integrating an Azure Synapse ETL pipeline with Cosmos DB 
        Introducing Cosmos DB
        Azure Synapse Link integration
        Supported features of Azure Synapse Link
        Azure Synapse runtime support
        Structured streaming support
        Network and data security support for Azure Synapse Link with Cosmos DB
    Setting up Azure Cosmos DB analytical store
        Getting ready
        How to do it…
    Enabling Azure Synapse Link and connecting Azure Cosmos DB to Azure Synapse
        Getting ready
        How to do it…
    IoT end-to-end solutions and getting real-time insights
        Getting ready
        How to do it…
    Use cases using Synapse Link
Chapter 5: Data Transformation and Processing with Synapse Notebooks
    Landing data in ADLS Gen2
        Getting ready
        How to do it…
    Exploring data with ADLS Gen2 to pandas DataFrame in Synapse notebook
        Getting ready
        How to do it…
        There's more…
    Processing data from a PySpark notebook within Synapse
        How to do it…
    Performing read-write operations to a Parquet file using Spark in Synapse
        Getting ready
        How to do it…
    Analytics with Spark
        Getting ready
        How it works…
Chapter 6: Enriching Data Using the Azure ML AutoML Regression Model
    Training a model using AutoML in Synapse
        Getting ready
        How to do it…
        How it works…
    Building a regression model from Azure Machine Learning in Synapse Studio
        Getting ready
        How to do it…
        How it works…
    Modeling and scoring using SQL pools
        Getting ready
        How to do it…
        How it works…
    An overview of Spark MLlib and Azure Synapse 
    Integrating AI and Cognitive Services
        Getting ready
        How to do it…
        How it works…
Chapter 7: Visualizing and Reporting Petabytes of Data
    Combining Power BI and aserverless SQL pool
        Getting ready
        How to do it…
        How it works…
    Working on a composite model
        Getting ready
        How to do it…
        How it works…
    Using materialized views to improve performance
        Getting ready
        How to do it…
        How it works…
Chapter 8: Data Cataloging and Governance
    Configuring your Azure Purview account for Synapse SQL pool
        Getting ready
        How to do it…
        How it works…
    Scanning data using the Purview data catalog
        Getting ready
        How to do it…
        How it works…
    Enumerating resources within Synapse Studio
        Getting ready
        How to do it…
        How it works…
Chapter 9: MPP Platform Migration to Synapse
    Understanding data migration challenges
        Tables and databases
        Data modeling
        Data Manipulation Language statements
        Functions, stored procedures, sequences, and triggers
    Configuring Azure Synapse Pathway
        Getting ready
        How to do it…
        How it works…
    Evaluating a data source to be migrated
        Getting ready
        How to do it…
    Generating a data migration assessment
        Getting ready
        How to do it…
    Supported data sources for migration
        IBM Netezza and Azure Synapse platform differences
        Oracle Exadata and Azure Synapse platform differences
        Snowflake and Azure Synapse platform differences
        Microsoft SQL Server and Azure Synapse platform differences
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