Hadoop Developer with Spark (CCA175) Certification Training Course | Meesha Software

Hadoop Developer with Spark Certification Training Course: CCA175

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  • " Lectures

Hadoop Developer with Spark Certification Training Course: CCA175

  • "
  • " Lectures

Hadoop Developer with Spark Course Overview

Students will have the ability to construct strong data processing systems using Apache Hadoop if they get the Hadoop Developer with Spark Course certification. Students who successfully finish this course will have the ability to grasp workflow execution as well as interact with APIs by carrying out joint operations and developing code for MapReduce. This class will provide an outstanding practice environment for addressing the real-world challenges that Hadoop developers must overcome. Companies in every region of the world are looking for workers with Hadoop certification and expertise since "Big Data" has become the industry buzzword. The analysis of big data is becoming more important to many major companies since it helps them enhance their performance. As a result, Big Data Hadoop specialists with extensive experience are in high demand across the business.

The post of Hadoop Developer with Spark is now one of the most sought-after and well-rewarded technical jobs in the world. According to research by McKinsey, the United States would be the only country to experience a shortage of almost 190,000 data scientists, as well as 1.5 million data analysts and Big Data managers, by the year 2018.

Who should take part in this Hadoop Developer with Spark Course class?

Training in Hadoop is recommended for those who have.

  • Developers
  • Engineers
  • Officers of the Law and Order
  • Any competent expert who has prior experience in programming and has a fundamental understanding of SQL and Linux command syntax.

Course Objectives:

  • You will be able to learn how to distribute data, store data, and process data in a Hadoop cluster if you get the Hadoop certification.
  • After finishing this course, you will be able to quickly create applications using Apache Spark, set up such apps, and deploy them on a Hadoop cluster.
  • Gain an understanding of how to do interactive data analysis using the Spark shell.
  • Processing a live data stream may be done using Spark Streaming.
  • Discover several methods to analyze structured data and query it by using Spark SQL.
  • You will learn how to utilize Flume and Kafka to import data for Spark Streaming by taking this Hadoop training course.
  • Due to the limited availability of this program, it may take up to three weeks to organize the necessary logistics.

You are going to learn:

Module 1: Introduction to Apache Hadoop and the Hadoop Ecosystem is covered in this Module of the course.

  • Apache Hadoop Overview
  • The Receiving and Storing of Data
  • Data Processing
  • Investigation of the Data and Its Meaning
  • Other Methods for Studying Ecosystems
  • Introduction to the Practical Activities That You Will Be Performing

Module 2: File Storage in Hadoop, Module 2 of the Apache Hadoop

  • Components of the Hadoop Cluster Provided by Apache
  • The Architecture of HDFS
  • Using HDFS

Module 3: Distributed Processing on an Apache Hadoop Cluster, which is the Module's point.

  • Architecture YARN (YARN)
  • Collaboration With the YARN

Module 4: The Fundamentals of Apache Spark.

  • What exactly is this Apache Spark?
  • Beginning the Lighting of the Spark Shell
  • Implementation of the Spark Shell
  • How to Begin Working with Datasets and DataFrames
  • DataFrame Operations

Module 5: Working with Dataframes and Schemas is Covered in the Module.

  • Developing Dataframes Using Existing Data Sources
  • The saving of DataFrames to their respective Data Sources
  • DataFrame Schemas
  • Execution that is both eager and lazy

Module 6: Analyzing Data Using DataFrame Queries Is Covered In Module.

  • Utilizing Column Expressions in the Process of Querying DataFrames
  • queries based on grouping and aggregation
  • Joining DataFrames

Module 7: RDD Overview

  • RDD Overview
  • Data Sources for RDDs
  • RDDs can be Created and Saved.
  • RDD Operations [RDD]

Module 8: Data Transformation Using RDDs is Covered in Module.

  • Functions of Writing and Transformation are Being Passed
  • Execution of the Transformation
  • Converting Between Relational Data Structures (RDDs) and Data Frames

Module 9: The Aggregating Data using Pair RDDs point of Module.

  • Key-Value Pair RDDs
  • Map-Reduce
  • Operations on Other Pairs of RDD

Module 10: Querying Tables and Views with Apache Spark SQL is the point of the Module.

  • Performing SQL Queries on Tables Within Spark
  • Utilizing Queries on Views and Files
  • The API for the Catalog.
  • Analyzing the similarities and differences between Spark SQL, Apache Impala, and Apache Hive-on-Spark

Module 11: Working with Datasets in Scala is the point of Module.

  • The concepts of Datasets and DataFrames
  • Putting together datasets
  • Loading and Saving Datasets
  • Operations on a Dataset

Module 12: Writing, Configuring, and Managing Applications Utilizing Apache Spark is the point of the Module.

  • Composing an Application for Spark
  • The Process of Developing and Operating an Application
  • The mode of application deployment
  • The user interface of the Spark Web Application
  • The Process of Configuring Application Properties

Module 13: Distributed Processing, which is covered in Module.

  • A Look at Apache Spark Deployed on a Cluster
  • RDD Partitions
  • Using Partitioning in Queries as an Example
  • The Process and Its Steps
  • Planning for the Carriage Out of Work
  • Execution Plan for a Catalyst, for Instance
  • Example: RDD Execution Plan

Module 14: Distributed Data Persistence is the point of the Module.

  • The persistence of DataFrames and Datasets
  • Levels of Persistence Within Storage
  • Viewing Persisted RDDs

Module 15:Patterns That Are Common in the Processing of Data Using Apache Spark.

  • Examples of Common Uses for Apache Spark
  • Iterative Algorithms Utilizing the Apache Spark Platform
  • The Art of Machine Learning
  • K-means is one example.

Module 16: Patterns That Are Common in the Processing of Data Using Apache Spark.

  • An Explanation of Apache Spark Streaming
  • Streaming Request Count Is An Example Of This.
  • DStreams
  • Applications for Streaming Media Development

Module 17: Processing Multiple Batches Using Apache Spark Streaming is Covered.

  • Processing Done in Multiple Batches
  • Time Slicing
  • The Workings of the State
  • Operations Regarding Sliding Windows
  • In-depth Look into Structured Streaming

Module 18: The Apache Spark Streaming: Data Sources Module is the 18th module.

  • An Explanation of Streaming Data Sources

  • Data Sources Powered by Apache Flume and Apache Kafka

  • Use of a Kafka Direct Data Source as an Illustration

Course Curriculum

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  • Exp. 4 year

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