big-data-and-hadoop

Big Data and Hadoop

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DESCRIPTION

Hadoop is an open source programming framework based on Java. It has been developed primarily for storing & processing extremely large unstructured data in a distributed computing environment. With Hadoop, applications can be run on thousands of distributed commodities hardware called as nodes and can handle thousands of terabytes. Its distributed file system facilitates very fast data transfer rates among nodes over network. The inbuild redundancy allows to recover from node failures.

Hadoop has emerged as the foundation of big data and its processing such as analytics, handling humongous amount of data generated from internet of things (IoT) sensors.

  • Master fundamentals of Hadoop 2.7 and YARN and write applications using them
  • Setting up Pseudo node and Multi node cluster on Amazon EC2
  • Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
  • Learn Spark, Spark RDD, Graphx, MLlib writing Spark applications
  • Master Hadoop administration activities like cluster managing,monitoring,administration and troubleshooting
  • Configuring ETL tools like Pentaho/Talend to work with MapReduce, Hive, Pig, etc
  • Detailed understanding of Big Data analytics
  • Hadoop testing applications using MR Unit and other automation tools.
  • Work with Avro data formats
  • Practice real-life projects using Hadoop and Apache Spark
  • Be equipped to clear Big Data Hadoop Certification.

Hadoop

        + Hadoop Distributed File System (HDFS) – storing data across thousands of commodity servers with high data transfer rate supported. 

        + Hadoop's Yet Another Resource Negotiator (YARN) – resource management & scheduling for user applications.

        + MapReduce – programming interface to handle large distributed data processing – mapping data and reducing it to result.

HBase – An open source, nonrelational, distributed database.

Apache Flume – Collect, aggregate, and move huge volume of streaming data into HDFS.

Apache Hive – A data warehouse data provides data summarization, query, and analysis. 

Apache Pig – A high level open source platform for creating parallel programs that run-on Hadoop.

Apache Sqoop – A tool to transfer bulk data between Hadoop and structured data stores (RDBMS) 

Apache oozie – Workflow scheduler for managing Hadoop jobs. 

Apache Spark – A fast engine for big data processing capable of streaming and supporting SQL, Machine Learning, and graph processing. 

Apache Zookeper – An open source configuration, synchronization, and naming registry service for large distributed systems. 

NoSQL – “Not only” or “Non-relational” SQL for storage and retrieval of data which is modelled unlike tabular relations as in relational databases. 

        + Cassandra or MongoDB

We will share the reading material before the lectures
  • Java
  • Basics of Linux

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