Learn Hadoop and BigData with real examples and architecture of Hadoop
What you'll learn:
- Understand what is BigData and Hadoop
- What is HDFS and Map Reduce
- You get practical knowledge how HDFS and Map Reduce Examples
- Understand what the job of a Hadoop Developer /Tester looks like
- This course is meant for students willing to next generation of IT
- Understanding V's of Big Data
- Evolution of Hadoop
Large data collections that can't be processed using conventional data processing technologies are what are known as big data or Hadoop.
These data sets are made up of data from various sources and are processed and analysed to get useful information.
Large e-commerce companies employ big data to compile data on user preferences and browsing habits in order to offer relevant suggestions and search results to specific users.
Content of the Hadoop Course Big Data and Hadoop
Introduction to Hadoop and big data
Java 1.8 and Ubuntu installation on VM Workstation 11
Versioning and configuration of Hadoop
only one node Installation of Hadoop 1.2.1 on a multi-node Ubuntu 14.4.1 Installation of Hadoop 1.2.1 using Linux and Hadoop commands on Ubuntu 14.4.1
Block positioning and cluster architecture
Hadoop Local Mode Modes
Complete Distribution Mode
Master Daemons for Hadoop (Name Node, Secondary Name Node, Job Tracker)
Master Daemons (Job tracker, Task tracker)
HDFS Commands in Hadoop
Accessing HDFS Using CLI
Understanding Map-Reduce Streamline Framework Example to Word Count
Utilizing Eclipse Luna HDFS to create a Map-Reduce programme. Method using the Read-Write Process Map-Reduce Life Cycle
the comparative and (Java)
Using a custom output file to analyse a temperature dataset Map -Reduce
Partitioner & Combiner Custom
Using the local and pseudo-distributed modes of Map-Reduce.
Java Enum (Advanced Map-Reduce) Custom and Dynamic Counters
Multi-node MapReduce execution Hashtable Cluster
Distribution of Custom Writable Site Data Using Configuration, DistributedCache, and Stringifie Input Formatters
Input Formatter for NLine
Sorting Reverse Sorting Secondary Sorting Compression Technique XML Input Formatter
Use of the Sequence File Format
Implementing the AVRO File Format
MR Unit: Reduce
Utilizing NYSE DataSets
Utilizing Map-Reduce in Cloudera Box while Working with Million Song DataSets
Installation & Introduction of the Hive
Hive Commands Exploring Internal and External Table Partitions Data Types in Hive
forms of complex data
UDF in Hive Integrated UDF
Java to Hive Connection Joins in Hive Working with HWI Bucket Map-side Custom UDF Thrift Server More commands to join
Running Hive with SortBy Distribute By Lateral View in Cloudera
Basics and Installations of Sqoop
Advance Imports for Data Import from Oracle to HDFS
Running Sqoop in Cloudera in Real-Time UseCase Exporting Data from HDFS to Oracle
Installation and Introduction of the PIG
Working With Complex Datatypes: WordCount in Pig NYSE in Pig
Distinctive Command Group Filter Order Join Flatten Co-group Union Illustrate Miscellaneous
Describe UDFs in Dry and Pig Parameter Substitution
Run Pig Macros Using Cloudera for Pig
Setting up Oozie
Oozie running Map-Reduce
Oozie is running Pig and Sqoop.
Big Data Hadoop FAQ’s:
Hadoop is made up of four main components: HDFS, MapReduce, YARN, and Hadoop Common.
Apache Hadoop developed as a solution to "Big Data" as an issue. The framework Apache Hadoop offers us a number of tools or services to store and process Big Data. Big Data analysis and business decision-making are aided, which cannot be done effectively and efficiently with conventional methods.
Pig Latin can handle both simple data types like tuples, bags, and maps as well as complicated data types like int, float, long, and double. Atomic or scalar data types are the fundamental data types used in all programming languages. Examples include string, int, float, long, double, char, and byte. Tuple, Map, and Bag are examples of complex data types.
As part of the Hadoop stack, "Oozie" is integrated and supports a variety of Hadoop tasks, including "Java MapReduce," "Streaming MapReduce," "Pig," "Hive," and "Sqoop."