Introduction to Hadoop and Big Data

Course Description

This hands-on three-day course introduces Hadoop and Big Data from both business and technical perspectives. Participants explore how distributed data storage and processing can support analysis and data-driven decision-making. The course examines practical enterprise applications, the Hadoop ecosystem, platform and tool choices, and the people and processes needed for adoption. It takes a vendor-neutral approach to the opportunities and challenges involved in integrating Hadoop with existing IT environments.
3 Days
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Prerequisites

There are no prerequisites for this course.

Who Should Attend

This course is suitable for managers, product managers, project managers, architects, developers and administrators who want to understand how Hadoop and Big Data could support their projects.

Learning Objectives

Explain Big Data concepts and the reasons for adopting Hadoop.
Describe the roles of MapReduce, HDFS and NoSQL in the Hadoop ecosystem.
Identify enterprise applications for Hadoop and large-scale data analysis.
Discuss Hadoop platforms, services and supporting tools.
Recognise the skills, staffing and training needed for Hadoop projects.
Consider integration, security and organisational challenges when planning adoption.
Evaluate opportunities for using Big Data to support business decisions.

Hadoop and Big Data Foundations

Understanding Big Data and its business context
Exploring the motivation for Hadoop and key adoption indicators
Introducing MapReduce and the Hadoop Distributed File System (HDFS)
Understanding the relationship between Hadoop, NoSQL and the wider ecosystem

Business Applications and Use Cases

Examining who uses Hadoop and the reasons for adoption
Exploring Facebook messaging and user analytics as examples
Using application logs to inform promotional decisions
Considering extract, transform and load (ETL), log processing and other enterprise applications

Hadoop Platforms, Vendors and Tools

Exploring the Hadoop vendor landscape through Cloudera, Hortonworks and MapR
Considering Amazon Elastic MapReduce (EMR)
Introducing Pig, Hive and Sqoop
Reviewing tools such as Datameer and Karmasphere
Considering the roles of Oracle, IBM and SAP
Understanding release stability and version selection

People, Skills and Enterprise Integration

Understanding the roles of data scientists, statisticians and analysts
Identifying data infrastructure and Hadoop engineering skills
Planning training for IT teams
Integrating Hadoop with enterprise development and testing processes
Considering shadow IT and cloud deployment
Working with systems integrators and consultants

Adoption Challenges and Opportunities

Understanding data flows across the enterprise
Considering existing Oracle, IBM and Microsoft software investments
Building a case for management support
Exploring opportunities offered by open-source technology
Considering multi-tenancy and data security in Hadoop

Review and Future Directions

Reviewing the key concepts, business applications and adoption considerations
Discussing the future of Hadoop and Big Data
HadoopBig DataHDFSMapReduceNoSQLHivePigSqoopdata analytics