This big data training course will provide a technical overview of Apache Hadoop for project managers, business managers and data analysts. Students will understand the overall big data space, technologies involved and will get a detailed overview of Apache Hadoop. The course will expose students to real world use cases to comprehend the capabilities of Apache Hadoop. Students will also learn about YARN and HDFS and how to develop applications and analyze Big Data stored in Apache Hadoop using Apache Pig and Apache Hive. Each topic will provide hands on experience to the students. The course is developed and taught by certified Hadoop consultants who have a passion for teaching and help deliver value to various clients using Big Data and Hadoop technologies on a daily basis.
• Learn about the big data ecosystem
• Understand the benefits and ROI you can get from your existing data
• Learn about Hadoop and how it is transforming the workspace
• Learn about MapReduce and Hadoop Distributed File system
• Learn about using Hadoop to identify new business opportunities
• Learn about using Hadoop to improve data management processes • Learn about using Hadoop to clarify results
• Learn about using Hadoop to expand your data sources
• Learn about scaling your current workflow to handle more users and lower your overall performance cost
• Learn about the various technologies that comprise the Hadoop ecosystem
Anybody who is involved with databases, data analysis, wondering how to deal with the mountains of data (anywhere gigabytes of user/log data etc. to petabytes) will benefit from this program.
This course is perfect for:
• Business Analysts
• Software Engineers
• Project Managers
• Data Analysts
• Business Customers
• Team Leaders
• System Analysts
Pre-requisite: No prior knowledge of big data and/or Hadoop is required for this class. Some prior programming experience is a plus for this class, but not necessary.
- Introduction to Big Data
Big Data – beyond the obvious trends
Implications for enterprise computing
Exponentially increasing data
Big data sources
Machine to machine
Data warehousing, business intelligence, analytics, predictive statistics, data science
- Survey of Big Data technologies
First generation systems
Second generation systems
Columnar databases with compression
Data warehousing appliances
Visualizing and understanding data with processing
How do technologies like mongodb, MarkLogic and couchdb fit in?
What is polyglot persistence?
- Introduction to Hadoop
What is Hadoop? Who are the major vendors?
A dive into the Hadoop Ecosystem
Benefits of using Hadoop
How to use Hadoop within your infrastructure?
Where do we use Hadoop?
Where do we look at options besides Hadoop?
- Introduction to MapReduce
What is MapReduce?
Why do you need MapReduce?
Using Mapreduce with Java and Ruby
Lab: How to use MapReduce in Hadoop?
- Introduction to Yarn
What is Yarn?
What are the advantages of using Yarn over classical MapReduce?
Using Yarn with Java and Ruby
Lab: How to use Yarn within Hadoop?
- Introduction to HDFS
What is HDFS?
Why do you need a distributed file system?
How is a distributed file system different from a traditional file system?
What is unique about HDFS when compared to other file systems?
HDFS and reliability?
Does it offer support for compressions, checksums and data integrity?
Lab: Overview of HDFS commands
- Data Transformation
Why do you need to transform data?
What is Pig?
Use cases for Pig
Lab: Hands on activities with Pig
- Structured Data Analysis?
How do you handle structured data with Hadoop?
What is Hive/HCatalog?
Use cases for Hive/HCatalog
Lab: Hands on activities with Hive/HCatalog
- Loading data into Hadoop
How do you move your existing data into Hadoop?
What is Sqoop?
Lab: Hands on activities with Sqoop
- Automating workflows in Hadoop
Benefits of Automation
What is oozie?
Automatically running workflows
Setting up workflow triggers
Lab: Demonstration of oozie
- Exploring opportunities in your own organization
Understanding how to ask questions
Tying possibilities to your own business drivers
Real world examples