Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

Arrow left icon
Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Authors (3):
Arrow left icon
 R Patil R Patil
Author Profile Icon R Patil
R Patil
 Shindgikar Shindgikar
Author Profile Icon Shindgikar
Shindgikar
 Kumar Kumar
Author Profile Icon Kumar
Kumar
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles FREE CHAPTER 2. Hadoop Life Cycle Management 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Hive architecture

The following is a representation of Hive architecture:

The preceding diagram shows that Hive architecture is divided into three parts—that is, clients, services, and metastore. The Hive SQL is executed as follows:

  • Hive SQL query: A Hive query can be submitted to the Hive server using one of these ways: WebUI, JDBC/ODBC application, and Hive CLI. For a thrift-based application, it will provide a thrift client for communication.
  • Query execution: Once the Hive server receives the query, it is compiled, converted into an optimized query plan for better performance, and converted into a MapReduce job. During this process, the Hive Server interacts with the metastore for query metadata.
  • Job execution: The MapReduce job is executed on the Hadoop cluster.
lock icon The rest of the chapter is locked
Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Modern Big Data Processing with Hadoop
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Modal Close icon
Modal Close icon