Run queries and analysis on big data clusters across relational and non relational databases
Key Features
● Connect to Hadoop, Azure, Spark, Oracle, Teradata, Cassandra, MongoDB, CosmosDB, MySQL, PostgreSQL, MariaDB, and SAP HANA.
● Numerous techniques on how to query data and troubleshoot Polybase for better data analytics.
● Exclusive coverage on Azure Synapse Analytics and building Big Data clusters.
Description
This book brings exciting coverage on establishing and managing data virtualization using polybase. This book teaches how to configure polybase on almost all relational and nonrelational databases. You will learn to set up the test environment for any tool or software instantly without hassle. You will practice how to design and build some of the high performing data warehousing solutions and that too in a few minutes of time.
You will almost become an expert in connecting to all databases including hadoop, cassandra, MySQL, PostgreSQL, MariaDB and Oracle database. This book also brings exclusive coverage on how to build data clusters on Azure and using Azure Synapse Analytics. By the end of this book, you just don’t administer the polybase for managing big data clusters but rather you learn to optimize and boost the performance for enabling data analytics and ease of data accessibility.
What you will learn
● Learn to configure Polybase and process Transact SQL queries with ease.
● Create a Docker container with SQL Server 2019 on Windows and Polybase.
● Establish SQL Server instance with any other software or tool using Polybase.
● Connect with Cassandra, MongoDB, MySQL, PostgreSQL, MariaDB, and IBM DB2.
Who this book is for
This book is for database developers and administrators familiar with the SQL language and command prompt. Managers and decision-makers will also find this book useful. No prior knowledge of any other technology or language is required.
Table of Contents
1. What is Data Virtualization (Polybase)
2. History of Polybase
3. Polybase current state
4. Differences with other technologies
5. Usage
6. Future
7. SQL Server
8. Hadoop Cloudera and Hortonworks
9. Windows Azure Storage Blob
10. Spark
11. From Azure Synapse Analytics
12. From Big Data Clusters
13. Oracle
14. Teradata
15. Cassandra
16. MongoDB
17. CosmosDB
18. MySQL
19. PostgreSQL
20. MariaDB
21. SAP HANA
22. IBM DB2
23. Excel
About the Authors
Pablo Echeverria is a talented database and software developer. He tuned long-running queries in Oracle and SQL achieving an execution time of under one-second, reducing resource usage up to 10%, and streamlined client processes, reducing work time by 50%. He is a critical thinker who focuses on implementation and testing. He loves learning and connecting new technologies.
LinkedIn profile: https://www.linkedin.com/in/pablo-echeverria/
Blog Link: https://www.sqlservercentral.com/author/pabechevb
