Apache Spark online training institute in hyderabad

Apache Spark

Apache Spark is a lightning-fast cluster computing technology, designed for fast computation. It is based on Hadoop MapReduce and it extends the MapReduce model to efficiently use it for more types of computations, which includes interactive queries and stream processing. The main feature of Spark is its in-memory cluster computing that increases the processing speed of an application.

Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming. Apart from supporting all these workload in a respective system, it reduces the management burden of maintaining separate tools.

Evolution of Apache Spark

Spark is one of Hadoop’s sub project developed in 2009 in UC Berkeley’s AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was donated to Apache software foundation in 2013, and now Apache Spark has become a top level Apache project from Feb-2014.

Features of Apache Spark

Apache Spark has following features.

  • Speed − Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk. This is possible by reducing number of read/write operations to disk. It stores the intermediate processing data in memory.
  • Supports multiple languages − Spark provides built-in APIs in Java, Scala, or Python. Therefore, you can write applications in different languages. Spark comes up with 80 high-level operators for interactive querying.
  • Advanced Analytics − Spark not only supports ‘Map’ and ‘reduce’. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms.


famous online training on Hadoop in hyderabad

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Hadoop Architecture

Hadoop framework includes following four modules:

  • Hadoop Common: These are Java libraries and utilities required by other Hadoop modules. These libraries provides filesystem and OS level abstractions and contains the necessary Java files and scripts required to start Hadoop.
  • Hadoop YARN: This is a framework for job scheduling and cluster resource management.
  • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
  • Hadoop MapReduce: This is YARN-based system for parallel processing of large data sets.


Big Data Hadoop Certification Training in Hyderabad, India

What skills will you learn with our Big Data Hadoop Certification Training?

Big Data Hadoop training in Hyderabad will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. You will learn to:

Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark with this Hadoop course.

  • Understand Hadoop Distributed File System (HDFS) and YARN architecture, and learn how to work with them for storage and resource management
  • Understand MapReduce and its characteristics and assimilate advanced MapReduce concepts
  • Ingest data using Sqoop and Flume
  • Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
  • Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
  • Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
  • Understand and work with HBase, its architecture and data storage, and learn the difference between HBase and RDBMS
  • Gain a working knowledge of Pig and its components
  • Do functional programming in Spark, and implement and build Spark applications
  • Understand resilient distribution datasets (RDD) in detail
  • Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
  • Understand the common use cases of Spark and various interactive algorithms
  • Learn Spark SQL, creating, transforming, and querying data frames
  • Prepare for Cloudera CCA175 Big Data certification