Apache Spark With Scala/Python And Apache Storm Certification Training Course


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Course Duration

50 Days.


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Apache Spark With Scala/Python And Apache Storm online training is an open source cluster computing big data framework. It provides faster and more general data processing platform engine. It is basically designed for fast computation. It works with file system to distribute data cluster and process that data in parallel. It covers wide range of workloads like batch applications, iterative algorithms, interactive queries, complex analytics and streaming.

Apache Spark With Scala/Python And Apache Storm Certification is the 2nd great framework of Big Data analytics. The popularity of Spark and Scala is gradually increases, which increase its demand. It is mainly used for data processing, querying, and generating analytics reports in a faster way. As compared to MapReduce, Apache Spark has high speed In-memory data processing engine.

Benefits of Attending Apache Spark With Scala/Python And Apache Storm Training

Apache Spark is an open-source data processing engine to store and process data in real-time across various clusters of computers using simple programming constructs. It has fewer lines of code and supports authentication via a shared secret. It can also run on YARN leveraging the capability of Kerberos. In general spark is a fast data processing engine. The main reason of spark being faster is that it process the data in-memory.

Spark processes data 100 times faster than MapReduce as it is done in-memory. important features of Apache Spark Fast processing, In-memory computing, Fault-tolerant and Flexible in nature. when you submit your job to spark, it takes all the data in memory from the disk and performs all the jobs tasks and clean the memory once all tasks complete.

Apache Spark has the following key features:

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  • What is Apache Spark
  • Understanding Lambda Architecture for Big Data Solutions
  • Role of Apache Spark in an ideal Lambda Architecture
  • Understanding Apache Spark Stack
  • Spark Versions
  • Storage Layers in Spark
  • Downloading Apache Spark
  • Installing Spark in a Single Node
  • Understanding Spark Execution Modes
  • Batch Analytics
  • Real Time Analytics Options
  • Exploring Spark Shells
  • Introduction to Spark Core
  • Setting up Spark as a Standalone Cluster
  • Setting up Spark with Hadoop YARN Cluster
  • Basics of Python
  • Basics of Scala
  • Understanding the Basic component of Spark -RDD
  • Creating RDDs
  • Operations in RDD
  • Creating functions in Spark and passing parameters
  • Understanding RDD Transformations and Actions
  • Understanding RDD Persistence and Caching
  • Examples for RDDs
  • Installation of Anaconda Python
  • Installation of Jupiter Notebook
  • Working with Jupiter Notebook
  • Installation of Zeppelin
  • Working with Zeppelin notebooks
  • Anatomy of Hadoop Cluster, Installing and Configuring Plain Hadoop
  • Batch v/s Real time
  • Limitations of Hadoop
  • Understanding the Key/Value Pair Paradigm
  • Creating a Pair RDD
  • Understanding Transformations on Pair RDDs
  • Understanding Actions on Pair RDDs
  • Understanding Data Partitioning in RDDs
  • Understanding Default File Formats supported in Spark
  • Understanding File systems supported by Spark
  • Loading data from the local file system
  • Loading data from HDFS using default Mechanism
  • Spark Properties
  • Spark UI
  • Logging in Spark
  • Checkpoints in Spark
  • Creating a HiveContext
  • Inferring schema with case classes
  • Programmatically specifying the schema
  • Understanding how to load and save in Parquet, JSON, RDBMS and any arbitrary source ( JDBC/ODBC)
  • Understanding DataFrames
  • Working with DataFrames
  • Understanding the role of Spark Streaming
  • Batch versus Real-time data processing
  • Architecture of Spark Streaming
  • First Spark Streaming program in Java with packaging and deploying
  • Anatomy of Hadoop Cluster, Installing and Configuring Plain Hadoop
  • What is Big Data Analytics
  • Batch v/s Real time
  • Limitations of Hadoop
  • Storm for Real Time Analytics
  • Installation of Storm
  • Components of Storm
  • Properties of Storm
  • Storm Running Modes
  • Creating First Storm Topology
  • Topologies in Storm
  • Getting Data
  • Bolt Lifecycle
  • Bolt Structure
  • Reliable vs Unreliable Bolts



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