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
