Big Data Hadoop with Spark Developer (BDHS) Training Logo

Big Data Hadoop with Spark Developer (BDHS) Training

Live Online & Classroom Enterprise Training

Big Data Hadoop with Spark Developer focuses on building and managing big data solutions using Hadoop and Apache Spark. It covers data processing, storage, and developing scalable applications for large-scale data analytics.

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What is Big Data Hadoop Spark Developer Training about?

This course provides a comprehensive introduction to big data development using Hadoop and Apache Spark, the two most widely used frameworks in the big data ecosystem. Participants will learn about HDFS, MapReduce, YARN, Hive, Pig, and Sqoop, followed by Spark’s Core, SQL, Streaming, and MLlib components. By integrating Hadoop’s robust storage capabilities with Spark’s powerful in-memory analytics engine, learners will gain the ability to develop and deploy scalable big data applications for real-world use cases.

What are the objectives of Big Data Hadoop Spark Developer Training ?

  • Understand Hadoop architecture and ecosystem tools (HDFS, MapReduce, Hive, Pig, Sqoop). 
  • Use YARN for resource management and job scheduling. 
  • Process and analyze datasets using Spark Core, RDDs, and DataFrames. 
  • Implement real-time analytics with Spark Streaming. 
  • Apply Spark MLlib for machine learning and predictive analytics.

Who is Big Data Hadoop Spark Developer Training for?

  • Big data developers and data engineers. 
  • Software engineers transitioning into big data roles. 
  • Data analysts and scientists working with massive datasets. 
  • IT professionals exploring big data frameworks. 
  • Students and professionals aiming for careers in big data and analytics.

What are the prerequisites for Big Data Hadoop Spark Developer Training?

Prerequisites:  

  • Basic programming knowledge (Java, Python, or Scala).  
  • Understanding of SQL and relational databases. 
  • Familiarity with distributed computing concepts. 
  • Basic knowledge of Linux/Unix commands. 
  • Optional: prior exposure to data analysis or ETL workflows. 


Learning Path: 

  • Introduction to Big Data, Hadoop ecosystem, and HDFS. 
  • MapReduce programming and YARN for cluster management. 
  • Hive, Pig, and Sqoop for data storage and integration. 
  • Apache Spark: Core, RDDs, DataFrames, and Datasets. 
  • Advanced Spark: Streaming, SQL, and MLlib applications. 


Related Courses: 

  • Big Data Analytics Using Spark 
  • Data Engineering with PySpark 
  • Apache Kafka for Real-Time Data Processing 
  • Data Warehousing and BI Analytics 

Available Training Modes

Live Online Training

5 Days

Course Outline Expand All

Expand All

  • Introduction to Big Data
  • Distributed Systems
  • Introduction to Hadoop
  • Overview of Hadoop Distributed File System(HDFS)
  • HDFS Architecture and Components
  • HDFS Command Lines
  • Overview of YARN
  • Data Ingestion
  • Big Data Ingestion Tools: Apache Flume
  • Using Sqoop Commands to Import and Export Data
  • Big Data Ingestion Tools: Apache Sqoop
  • Big Data Ingestion Tools: Apache Kafka
  • Distributed Processing with MapReduce
  • Mapper Class
  • Reducer Class
  • MapReduce Program
  • Creating a JAR File
  • Executing MapReduce Program
  • MapReduce Joins
  • Pig
  • Apache Pig
  • Basics of Regression
  • Apache Hive
  • Hive Architecture
  • Basic Queries in Hive
  • Hive Metastore
  • Hive DDL and DML
  • Hive Data Types and File Formats
  • Creating a New Data File in Hive
  • Advance Datatypes in Hive
  • Hive Data Serialization
  • Hive Optimization
  • Hive Joins
  • Hive Partitioning
  • Bucketing
  • Creating Buckets in Hive
  • Impala
  • Introduction to NoSQL
  • YARN Tuning
  • Introduction to HBase
  • Introduction to Scala
  • Functional Programming in Scala
  • Programming in Scala
  • Scala Literals
  • Declare a Variable and Accept the Values from Console
  • Trait, Classes, Objects and Functions in Scala
  • Creating Trait and Implementing Methods using Trait
  • Collections and Types
  • Scala Arrays
  • Scala Lists
  • Scala Map
  • A Background check on Spark
  • Components of Spark
  • Application of In-memory Processing
  • Hadoop Ecosystem vs. Spark
  • Advantages of Spark
  • Architecture of Spark
  • Spark Shell
  • Introduction to RDD
  • Setup Spark Project in IDE
  • Creating Spark RDD
  • Pairing Spark RDD
  • RDD Operation
  • RDD Operations
  • Caching and Persistence
  • Lineage and DAG
  • Debugging
  • Partitioning, Scheduling, Shuffling
  • Graph Processing
  • Machine Learning
  • Spark SQL
  • Spark SQL Architecture
  • DataFrames
  • Data Frame and Data set
  • Executing Spark SQL Queries
  • Overview of MLlib
  • MLlib Pipelines
  • Overview of Streaming
  • Spark Streaming
  • Introduction to DStreams
  • Transformation on DStreams
  • Design Patterns for using ForeachRDD
  • Stream Management Operation
  • Windowing Operations
  • Join Operations: Stream-Dataset Join
  • Structured Spark Streaming
  • Structured Streaming Architecture & Components
  • Output Sinks
  • Structured Streaming APIs
  • Constructing Columns in Structured Streaming
  • Windowed Operations on Event-time
  • Spark Streaming and Structured Streaming
  • Introduction to Spark GraphX
  • Graph in Spark
  • Graph Operators
  • Join Operators
  • Graph Parallel System
  • Algorithm in Spark
  • Pregel API
  • Spark GraphX

Who is the instructor for this training?

The trainer for this Big Data Hadoop with Spark Developer (BDHS) Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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