E4 2017
 

E4 2017 has passed. E4 2018 dates and call for papers will be posted in Fall 2018.

E4 TRAINING DAY: HADOOP FOR DATABASE PROFESSIONALS (CONDENSED VERSION)

INSTRUCTOR: Tanel Poder, gluent.
DATE: June 15, 2017
PRICE: Included in E4 Cost
FORMAT: Webinar

Aimed at IT professionals who have a deep knowledge of RDBMS systems, this half-day session will provide insight into the architecture of Hadoop, and how it compares and contrasts to traditional RDBMS systems, from storage and SQL processing to maintenance and operations. The course will go beyond merely listing the different components of the Hadoop ecosystem and how they fit together. In addition, we will explain why Hadoop will be at the center of the enterprise data universe soon. The training seminar will close with several case studies, providing attendees a first-hand look at how Hadoop solutions have been built in the real world.

This course requires no previous knowledge of Hadoop or Big Data, but rather aims to provide a quick, technical deep-dive into concepts that will help you get started with Hadoop. Detailed Hadoop Administrator topics, such as installation, configuration and optimization are out of scope of this session.

TARGET AUDIENCE:

  • Database Administrators
  • Database Developers
  • Data Architects

COURSE PREREQUISITES:

  • No prior experience or knowledge of Hadoop or Big Data is necessary.
  • This course is lecture based, so there are no hardware or software requirements.

TABLE OF CONTENTS:

Introduction and Fundamentals of Hadoop
Get up to speed on the history of Hadoop, components involved, where the Hadoop architecture and technology are headed, and answer the question “Why Hadoop?”.
  • A brief history of Hadoop and why organizations choose Hadoop
  • What is so different about Hadoop?
  • Discuss hardware and software components of Hadoop
  • Provide details about the Hadoop ecosystem of interchangeable components
  • Remove common misconceptions about Hadoop
  • Reconcile RDBMS-specific terminology with Hadoop terminology
Hadoop Storage, Data Ingestion, and SQL Processing
There are several different techniques for data storage in Hadoop, including cloud based options. There are even more ways to move data into Hadoop. This module will compare the familiar RDBMS approach with the processes and technologies used in Hadoop. Finally, the various SQL on Hadoop technologies will be reviewed and discussed, showing that not all data processing routines must be hand coded in Java.
  • Compare and contrast Hadoop open data storage formats with RDBMS
  • How to get data in and out of Hadoop
  • Discuss databases in Hadoop, including NoSQL engines, and how they are similar and different from a standard relational database
  • Compare and contrast the differences in data processing on RDBMS vs Hadoop
  • Discuss the various native SQL engines, such as Hive, Impala, Presto, and Spark
Hadoop in Action
Learn how Hadoop has been successfully implemented in the real world, for both Big Data and traditional enterprise data. We will show you examples from Hadoop-using real world customers in different industries. This gives you a good idea where Hadoop has been proven out in the real world.
  • Data lake
  • Data hub
  • Analytics acceleration platform
  • Hadoop-first datasets
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