course detail

PySpark

PySpark Training in Chennai

BITA Academy The leader in IT Training and Certifications in Chennai offers PySpark training for IT Professionals and freshers. You will learn how to develop Spark apps for your Big Data Using Python only if you complete PySpark Training from the Best PySpark Training Course Institute in Chennai.

Important Things you should know about PySpark Training in Chennai

Apache Spark is one of the fastest and most efficient general engines for large-scale data processing. In this course, you will learn how to develop Spark applications for your Big Data using Python and a stable Hadoop distribution. It is mandatory for one to learn Big Data Platforms such as Spark and Hadoop to handle large scale data sets. You will learn how to process data at scales when you develop Spark App with Python. By learning PySpark developer Course in Chennai, you will explore RDD API, the important feature of Spark. You will learn how to become more proficient using Spark SQL and DataFrames. Spark, as a big data processing engine is at least 10 to 100 times faster than Hadoop.

Why is basics of Python knowledge mandate for PySpark Course in Chennai?

Python is a powerful programming language for handling large scale of data. Spark is a distributed processing engine that allows you to process your data efficiently. It was developed in Scala language, which is very much similar to Java. It compiles Program code in to byte code for the JVM in Spark big data process. Apache Spark Community released PySpark to support Spark with Python and the developers find it very useful now. Apache Spark is one of the most active projects and developed in response to limitations of MapReduce. Spark can be 10 to 100 times faster than MapReduce, which combined with the power of Python allows you to create big data applications, which is easy to code. Lot of improvements done in Data Set and Data Frame API’s in the latest Spark 2 Version.

So when you complete PySpark Training Course in Chennai, You will have deep knowledge to develop large-scale data apps that enable you to work with Big Data.

PySpark Developer Course Exams and Certification

BITA Academy certification is recognized by all major IT companies around the globe. We Provide certificate for all students after completion of PySpark course. Your resume will have an extra value add if you have a PySpark course completion certificate from us or from any of the reputed PySpark training institute in chennai.

 

PySpark Training Course Syllabus

PART 1: Introduction to Big Data Hadoop 

  • What is Big Data?
  •  Big Data Customer Scenarios
  • Limitations and Solutions of Existing Data Analytics Architecture
  • How Hadoop Solves the Big Data Problem?
  • What is Hadoop?
  • Key Characteristics of Hadoop
  • Hadoop Ecosystem and HDFS
  • Hadoop Core Components
  • Rack Awareness and Block Replication
  • YARN and its advantage
  • Hadoop Cluster and its architecture
  • Hadoop: Different Cluster modes
  • Big Data Analytics with Batch and Real Time Processing

PART 2: Why do we need to use Spark with Python?

  • History of Spark
  • Why do we need Spark?
  • How Spark differs from its competitors

PART 3: How to get an Environment and Data?

  • CDH + Stack Overflow
  • Prerequisites and known issues
  • Upgrading Cloudera Manager and CDH
  • How to install Spark?
  • Stack Overflow and Stack Exchange Dumps
  • Preparing your Big Data

PART 4: Basics of Python

  • History of Python
  • The Python Shell
  • Syntax, Variables, Types and Operators
  • Compound Variables: List, Tuples and Dictionaries
  • Code Blocks, Functions, Loops, Generators and Flow Control
  • Map, Filter, Group and Reduce
  • Enter PySpark: Spark in the Shell

PART 5: Functions and Modules in Python

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda functions
  • Object Oriented Concepts
  • Standard Libraries
  • Modules used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation

PART 6: Overview of Spark

  • Introduction
  • Spark, Word Count, Operations and Transformations
  • Fine Grained Transformations and Scalability
  • How Word Count works?
  • Parallelism by Partitioning Data
  • Spark Performance
  • Narrow and Wide Transformations
  • Lazy Execution, Lineage, Directed Acyclic Graph (DAG) and Fault Tolerance
  • The Spark Libraries and Spark Packages

PART 7: Deep Dive on Spark

  • Spark Architecture
  • Storage in Spark and supported Data formats
  • Low Level and High Level Spark API
  • Performance Optimization: Tungsten and Catalyst
  • Deep Dive on Spark Configuration
  • Spark on Yarn: The Cluster Manager
  • Spark with Cloudera Manager and YARN UI
  • Visualizing your Spark App: Web UI and History Server

PART 8: The Core of Spark – RDD’s

  • Deep Dive on Spark Core
  • Spark Context: Entry Point to Spark App
  • RDD and Pair RDD – Resilient Distributed Datasets
  • Creating RDD with Parallelize
  • Partition, Repartition, Saving as Text and HUE
  • How to create RDD’s from External Data Sets?
  • How to create RDD’s with transformations?
  • Lambda functions in Spark
  • A quick look at Map, FlatMap, Filter and Sort
  • Why do we need Actions?
  • Partition Operations: MapPartitions and PartitionBy
  • Sampling your Data
  • Set Operations
  • Combining, Aggregating, Reducing and Grouping on PairRDD’s
  • Comparison of ReduceByKey and GroupByKey
  • How to group Data in to buckets with Histogram?
  • Caching and Data Persistence
  • Accumulators and Broadcast Variables
  • Developing self-contained SpyPark App, Package and Files
  • Disadvantages of RDD

PART 9: DataFrames and Spark SQL

  • How to Create Data Frames?
  • DataFrames to RDD’s
  • Loading Data Frames: Text and CSV
  • Schemas
  • Parquet and JSON Data Loading
  • Rows, Columns, Expressions and Operators
  • Working with Columns
  • User Defined Functions on Spark SQL

PART 10: Deep Dive on DataFrames and SQL

  • Querying, Sorting and Filtering DataFrames
  • How to handle missing or corrupt Data?
  • Saving DataFrames
  • How to query using temporary views?
  • Loading Files and Views into DataFrames using SparkSQL
  • Hive Support and External Databases
  • Aggregating, Grouping and Joining
  • The Catalog API
  • A quick look at Data

PART 11: Apache Spark Streaming

  • Why Streaming is necessary?
  • What is Spark Streaming?
  • Spark Streaming features and workflow
  • Streaming Context and DStreams
  • Transformation on DStreams

Why PySpark is trending now in recent days?

Enterprises and Humans need storage space for data to be analyzed and stored. Spark was open sourced in 2010 and at that time, it had only about 1600 lines of code. It was donated to Apache Software foundation in 2013 and it became a top level project in 2014. Spark is moving strong in to the future and the important feature to be noted in Spark is Resilient Distributed Data Sets. Java API and Python API that is added in the latest version of Spark is a big advantage as Python has several libraries and frameworks to perform data mining. You will learn the key benefits once you learn PySpark developer course in Chennai. Python is easy to use and read with an elegant syntax to perform machine learning operations.

PySpark stores data in data frames which is a collection of structured or semi structured data. DataFrames is distributed and a user can get the dataframe from RDD or Schema. So it is important for IT professionals to learn PySpark if they want to start their Career in Big Data field. Feel free to contact us if you have any queries. We are here to help you. We offer special discounts for college students and freshers. Call us if you need a free demo or a session. We Wish you all the best.

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