What this book covers
Chapter 1, Choosing the Optimal Method for Loading Data to Synapse, will help you learn how to choose between different options when loading data into Synapse and the optimal way to perform different data loadings.
Chapter 2, Creating Robust Data Pipelines and Data Transformation, will help you understand Synapse notebooks and its interfaces to create a file that will contain the real code – the logic. You will also learn how to visualize data within a notebook and other big data scenarios.
Chapter 3, Processing Data Optimally across Multiple Nodes, explores the Synapse SQL architecture components and how to leverage the scale-out capabilities to distribute the computational processing of data across multiple nodes.
Chapter 4, Engineering Real-time Analytics with Azure Synapse Link Using Cosmos DB, describes how you can architect and perform real-time analytics with Synapse, integrate Synapse Link for Cosmos DB, and enable the Internet of Things (IoT).
Chapter 5, Data Transformation and Processing with Synapse Notebooks, teaches you how to use Python to read data from Azure Data Lake Storage Gen2 into a Spark DataFrame using Azure Synapse Analytics.
Chapter 6, Enriching Data Using the Azure ML AutoML Regression Model, helps you uncover the power of Azure Machine Learning along with Spark MLlib and Synapse Studio.
Chapter 7, Visualizing and Reporting Petabytes of Data, teaches you how to present data using visualizations with Power BI, integrate Power BI with Synapse, and use the power of the serverless SQL pool for data exploration.
Chapter 8, Data Cataloging and Governance, teaches you how to provide comprehensive data governance for an analytical workload and embed data discovery and classification with Synapse using Azure Purview integration.
Chapter 9, MPP Platform Migration to Synapse, teaches you how to get started with the migration of a legacy data warehouse using Azure Synapse Pathway.
