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Batch
Date: Mar
1st @ 8:00AM
Faculty: Mr. Suresh
Duration:
25 Days
Fee: 6000/- INR+ Reg Fee 100/-INR (Last Batch for Discounted Price)
Location
: Maitrivanam, Hyderabad.
Venue
:
DURGA SOFTWARE SOLUTIONS at Maitrivanam
Plot No : 202,
IInd Floor ,
HUDA Maitrivanam,
Ameerpet, Hyderabad-500038
Ph.No : 09246212143.
*
Complete Material Will be Provided by Real Time Expert
Syllabus:
- What is big data?
- Big data challenges?
- How hadoop is related to big data?
- Problems with storing/processing of big data
- Working with traditional large scale systems
- What is hadoop ?
- Hadoop core components – HDFS & MR
- Hadoop eco system – other tools
- Hadoop distributions and differences: Cloudera, Horton works, MapR
- Real time scenarios of hadoop with various use cases.
HDFS (Hadoop Distributed File System)
- DFS vs HDFS and Cluster vs Hadoop Clusters
- Features of HDFS
- HDFS Architecture
- HDFS storage
- blocks, Configuring blocks , default vs custom block sizes
- HDFS architecture
- Replication in HDFS
- Fail over mechanism
- Custom replication and configuring replication factors
- Daemons of Hadoop 1.x :
- NameNode and functionality
- DataNode and functionality
- Secondary Name Node and functionality
- Job Tracker and functionality
- Task Tracker
- Daemons of Hadoop 2.x :
- Name Node, Data Node, Secondary Name Node, Resource Manager, Node Manager
- Hadoop cluster modes
- Single Node vs multi node
- HDFS federation
- High availability
MAP REDUCE
- Map Reduce life cycle
- Communication mechanism of processing daemons
- Input format and Record reader classes
- Success case vs Failure case scenarios
- Retry mechanism in Map Reduce
- Map Reduce programming
- Different phases of Map Reduce algorithm
- Different data types in Map Reduce
- Primitive data types Vs Map Reduce data types
- How to write map reduce programs
- Driver Code
- Importance of driver code in a Map Reduce program
- How to identify the driver code in Map Reduce program
- Different sections of driver code
- Mapper Code
- Importance of Mapper Phase in Map Reduce
- How to write a Mapper class,Methods in Mapper Class
- Reducer Code
- Importance of Reduce Phase in Map Reduce
- How to write a Reducer class,Methods in Reducer Class
- Input split
- Need of input split in Map reduce
- Input Split size vs block size
- Input split vs mappers
- Identity Mapper & Identity Reducer
- Input format’s in Map Reduce
- Text input format
- Key value text input format
- Sequence file input format
- How to use the specific input format in Map Reduce
- Custom input formats and its record readers
- Output format’s in Map Reduce
- Text output format
- Key value text output format
- Sequence file output format
- How to use the specific output format in Map Reduce
- Custom output formats and its record writers
- Map Reduce API
- New API vs Deprecated API
- Combiner in Map Reduce
- Usage of combiner class in map reduce
- Performance trade-offs
- Partitioner in map reduce
- Importance of partitionerclass in map reduce
- Writing custom partitioners
- Compression techniques in map reduce
- Importance of compression in map reduce
- What is CODEC
- Compression types
- GZipCodec
- BZip and BZip2 Codec
- LZOCodec
- Snappy Codec
- map reduce streaming
- data localization
- secondary sorting using map reduce
- enable and disable these techniques for all the job
- enable and disable these techniques for particular job
Hadoop Administration
- Hadoop single node cluster setup
- Operating system installation
- Jdk installation
- SSH configuration
- Dedicated group and user creation
- Hadoop installation
- Different configuration file setting
- Name node format
- Starting the hadoop daemons
- PIG installation (local mode, cluster mode)
- SQOOP installation
- Sqoop installation with mysql client
- Hive installation
- Hbase installation (local mode and clustered mode)
- OOZIE installation
- Mongo DB installation
Course Highlights:
- Dealing with real time scenarios and live examples
- Course curriculum is designed and explained in a standard, where you can clear any Hadoop certification easily.
- Delivering Real time Proof of Concept’s (POC) and working structure of real time projects.
- Providing Top 100 FAQ’s in Hadoop Interviews.
- Both Soft copy and Hard copy of Hadoop material will be given
- Latest updates, discussions on data analytics, new trends in Hadoop technology and its stack.
- Conducting online/offline Exams for students to validate themselves.
Recorded video lectures and Academic projects will be provided at nominal cost for interested students.