Use this guide to one of SQL Server 2019’s most impactful features―Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database.
Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL―taking advantage of skills you have honed for years―and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark.
Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis.
What You Will Learn
- Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments
- Analyze large volumes of data directly from SQL Server and/or Apache Spark
- Manage data stored in HDFS from SQL Server as if it were relational data
- Implement advanced analytics solutions through machine learning and AI
- Expose different data sources as a single logical source using data virtualization
Who This Book Is For
Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments