Introduction to BigData
- Which data is called as BigData
- What are business use cases for BigData
- BigData requirement for traditional Data warehousing and BI space
- BigData solutions
Introduction to Hadoop
- The amount of data processing in today’s life
- What Hadoop is why it is important
- Hadoop comparison with traditional systems
- Hadoop history
- Hadoop main components and architecture
Hadoop Distributed File System (HDFS)
- HDFS overview and design
- HDFS architecture
- HDFS file storage
- Component failures and recoveries
- Block placement
- Balancing the Hadoop cluster
- Different Hadoop deployment types
- Hadoop distribution options
- Hadoop competitors
- Hadoop installation procedure
- Distributed cluster architecture
- Lab: Hadoop Installation
Working with HDFS
- Ways of accessing data in HDFS
- Common HDFS operations and commands
- Different HDFS commands
- Internals of a file read in HDFS
- Data copying with ‘distcp’
- Lab: Working with HDFS
Hadoop Cluster Configuration
- Hadoop configuration overview and important configuration file
- Configuration parameters and values
- HDFS parameters
- MapReduce parameters
- Hadoop environment setup
- ‘Include’ and ‘Exclude’ configuration files
- Lab: MapReduce Performance Tuning
Hadoop Administration and Maintenance
- Namenode/Datanode directory structures and files
- Filesystem image and Edit log
- The Checkpoint Procedure
- Namenode failure and recovery procedure
- Safe Mode
- Metadata and Data backup
- Potential problems and solutions / What to look for
- Adding and removing nodes
- Lab: MapReduce Filesystem Recovery
- How to schedule Hadoop Jobs on the same cluster
- Default Hadoop FIFO Schedule
- Fair Scheduler and its configuration
- What MapReduce is and why it is popular
- The Big Picture of the MapReduce
- MapReduce process and terminology
- MapReduce components failures and recoveries
- Working with MapReduce
- Lab: Working with MapReduce
Programming MapReduce Jobs
- Java MapReduce implementation
- Map() and Reduce() methods
- Java MapReduce calling code
- Lab: Programming Word Count
Input/Output Formats and Conversion Between Different Formats
- Default Input and Output formats
- Sequence File structure
- Sequence File Input and Output formats
- Sequence File access via Java API and HDS
- Lab: Input Format
- Lab: Format Conversion
- Joining Data Sets in MapReduce Jobs
- How to write a Map-Side Join
- How to write a Reduce-Side Join
- MapReduce Counters
- Built-in and user-defined counters
- Retrieving MapReduce counters
- Lab: Map-Side Join
- Lab: Reduce-Side Join
Introduction to Hive, Hbase, Flume, Sqoop, Oozie and Pig
- Hive as a data warehouse infrastructure
- Hbase as the Hadoop Database
- Using Pig as a scripting language for Hadoop
Hadoop Case studies
- How different organizations use Hadoop cluster in their infrastructure
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