Hadoop Online Training
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Hadoop Course Content
Administrator coaching for Apache Hadoop
Introduction to Big Data and Hadoop
- What is Big Data?
- What area unit the challenges for process Big data?
- What technologies support Big data?
- Distributed systems
- What is Hadoop?
- Why Hadoop?
- History of Hadoop
- Use Cases of Hadoop
- Hadoop eco System
- HDFS
- Map scale back
- Statistics
Understanding the Cluster
- Typical work flow
- Writing files to HDFS
- Reading files from HDFS
- Rack Awareness
- 5 daemons
Best Practices for Cluster Setup
- Best Practices
- How to decide on the proper hadoop distribution
- How to decide on right hardware
Cluster Setup
- Install Pseudo cluster
- Install Multi node cluster
- Configuration
- Setup cluster on Cloud – EC2
- Tools
- Security
- Benchmarking the cluster
Routine Admin procedures
- Metadata & knowledge Backups
- Filesystem check (fsck)
- File system Balancer
- Commissioning and decommissioning nodes
- Upgrading
- Using DFS Admin
Monitoring the Cluster
- Using the online user interfaces
- Hadoop Log files
- Setting the log levels
- Monitoring with Nagios
Install ,Configure and use
- PIG
- HIVE
- HBASE
- Flume and Sqoop
- Zookeeper
Developer coaching for Apache Hadoop
Introduction to Big Data and Hadoop
- What is Big Data?
- What area unit the challenges for process Big data?
- What technologies support Big data?
- Distribution systems.
- What is Hadoop?
- Why Hadoop?
- History of Hadoop
- Use Cases of Hadoop
- Hadoop eco System
- HDFS
- Map scale back
- Statistics
Understanding the Cluster
- Typical work flow
- Writing files to HDFS
- Reading files from HDFS
- Rack Awareness
- 5 daemons
Developing the Map scale back Application
- Configuring development atmosphere – Eclipse
- Writing Unit check
- Running regionally
- Running on Cluster
- MapReduce workflows
How MapReduce Works
- Anatomy of a MapReduce job run
- Failures
- Job programing
- Shuffle and type
- Task Execution
MapReduce varieties and Formats
- MapReduce varieties
- Input Formats – Input splits & records, text input, binary input, multiple inputs & information input
- Output Formats – text Output, binary output, multiple outputs, lazy output and information output
MapReduce options
- Counters
- Sorting
- Joins – Map aspect and scale back aspect
- Side knowledge Distribution
- MapReduce Combiner
- MapReduce Partitioner
- MapReduce Distributed Cache
Hive and PIG
- Fundamentals
- When to Use PIG and HIVE
- Concepts
HBASE
- CAP Theorem
- Hbase design and ideas
- Programming
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