spark online training in hyderabad

spark online training in hyderabad

Spark Online Training in Hyderabad


Apache Spark  Online Training in Hyderabad will give you expertise to perform large-scale Data Processing using RDD, Spark Streaming, SparkSQL, MLlib, GraphX and Scala with Real Life use-cases on Banking and Telecom domain

Faculty : Real Time Expert  |  Duration : 20hrs   |   Material : Yes   |

Itabhyas videos tutorials

Itabhyas online training is the Best Spark Online Training in Hyderabad, Bengaluru, Chennai.

Spark is an open source processing engine built around speed, ease of use, and analytics. If you have large amounts of data that requires low latency processing that a typical MapReduce program cannot provide, Spark is the way to go.

Apache Spark is another increasingly popular alternative to replace MapReduce with a more performant execution engine but still use Hadoop HDFS as storage engine for large data sets.

Spark Architecture

From architecture perspective Apache Spark is based on two key concepts; Resilient Distributed Datasets (RDD) and directed acyclic graph (DAG) execution engine. With regards to datasets, Spark supports two types of RDDs: parallelized collections that are based on existing Scala collections and Hadoop datasets that are created from the files stored on HDFS. RDDs support two kinds of operations: transformations and actions. Transformations create new datasets from the input (e.g. map or filter operations are transformations), whereas actions return a value after executing calculations on the dataset (e.g. reduce or count operations are actions).
The DAG engine helps to eliminate the MapReduce multi-stage execution model and offers significant performance improvements.

Spark Online Training batches will start every week. Make a call on +91-9030403937 or send a mail to for

Spark Online Training in Hyderabad, Bangalore, Chennai, India.

Spark Online Training Course Content

1. Why Spark?
  • Problems with Traditional Large-Scale Systems
  • Introducing Spark
2. Spark Basics
  • What is Apache Spark?
  • Using the Spark Shell
  • Resilient Distributed Datasets (RDDs)
  • Functional Programming with Spark
3. Working with RDDs
  • RDD Operations
  • Key-Value Pair RDDs
  • MapReduce and Pair RDD Operations
4. The Hadoop Distributed File System
  • Why HDFS?
  • HDFS Architecture
  • Using HDFS
5. Running Spark on a Cluster
  • A Spark Standalone Cluster
  • The Spark Standalone Web UI
6. Parallel Programming with Spark
  • RDD Partitions and HDFS Data Locality
  • Working with Partitions
  • Executing Parallel Operations
7. Caching and Persistence
  • RDD Lineage
  • Caching Overview
  • Distributed Persistence
8. Writing Spark Applications
  • Spark Applications vs. Spark Shell
  • Creating the SparkContext
  • Configuring Spark Properties
  • Building and Running a Spark Application
  • Logging
9. Spark, Hadoop, and the Enterprise Data Center
  • Spark and the Hadoop Ecosystem
  • Spark and MapReduce

10. Spark Streaming

  • Example: Streaming Word Count
  • Other Streaming Operations
  • Sliding Window Operations
  • Developing Spark Streaming Applications
11. Common Spark Algorithms
  • Iterative Algorithms
  • Graph Analysis
  • Machine Learning
12. Improving Spark Performance
  • Shared Variables: Broadcast Variables
  • Shared Variables: Accumulators
  • Common Performance Issues