Hadoop Developer with Spark Training | Master Big Data Processing

Hadoop Developer with Spark

The Hadoop Developer with Spark course provides a comprehensive learning experience for developers eager to master big data processing with Apache Hadoop and Apache Spark. This hands-on training is designed to help you understand the core concepts of distributed computing, big data frameworks, and data processing using Hadoop and Spark. You’ll gain practical knowledge on how to leverage these technologies for large-scale data storage, management, and processing. By the end of the course, you'll be equipped to develop and implement big data applications that scale seamlessly on the Hadoop ecosystem and Spark platform.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
December 22^nd - 26^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 40 Hours) Guaranteed-to-Run		10% Off $2,000 $1,800 Fast Filling! Hurry Up.
January 03^rd - 17^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 40 Hours)		20% Off $2,000 $1,600
January 05^th - 09^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 40 Hours)		20% Off $2,000 $1,600
January 12^th - 16^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 40 Hours)		20% Off $2,000 $1,600
January 19^th - 30^th 06:00 AM - 10:00 PM (CST) Live Virtual Classroom (Duration : 40 Hours)		20% Off $2,000 $1,600
January 26^th - 30^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 40 Hours) Guaranteed-to-Run		20% Off $2,000 $1,600

Course Prerequisites

Basic programming skills, preferably in Java or Scala
Familiarity with SQL and relational databases
Basic understanding of Linux and command-line operations
Fundamental knowledge of big data concepts is beneficial
Prior experience with Hadoop or Apache Spark is recommended but not mandatory

Learning Objectives

By the end of this course, participants will be able to:

Understand the architecture and components of Hadoop and Apache Spark
Set up and configure Hadoop and Spark clusters
Build Spark applications using RDDs, DataFrames, and Spark SQL
Process real-time data with Spark Streaming and integrate it with other systems
Optimize the performance of Spark jobs and data pipelines
Deploy and manage big data applications on Hadoop and Spark clusters
Implement machine learning and graph processing with Spark MLlib and GraphX
Work with the Hadoop ecosystem, including Hive, HBase, and Sqoop

Target Audience

This course is designed for developers, data engineers, and professionals interested in learning how to develop, process, and manage big data applications using Hadoop and Apache Spark. The target audience includes:

Hadoop Developers
Data Engineers
Spark Developers
Big Data Professionals
Software Developers looking to work with big data processing tools
IT professionals interested in building big data solutions with Apache Hadoop and Apache Spark

Course Modules

Introduction to Hadoop and Spark
- Overview of Hadoop and Apache Spark and their role in big data processing
- Hadoop architecture, components, and its ecosystem (HDFS, MapReduce, YARN)
- Spark architecture and its integration with Hadoop
- Difference between Hadoop MapReduce and Spark processing models
Setting Up Hadoop and Spark Environments
- Installing and configuring Hadoop and Spark
- Understanding the Hadoop Distributed File System (HDFS)
- Setting up and managing a Spark cluster on YARN or Mesos
- Working with Spark shell and interactive analysis
Data Processing with Hadoop and Spark
- Understanding data processing workflows in Hadoop (MapReduce) vs. Spark (RDDs, DataFrames, Datasets)
- Writing Hadoop jobs with MapReduce and Spark applications
- Leveraging Spark SQL and DataFrames for structured data processing
- Working with Spark Streaming for real-time data processing
Spark Core Concepts
- Understanding RDDs (Resilient Distributed Datasets) and Transformations
- Using Actions, Caching, and Persisting for performance optimization
- Implementing Spark SQL for querying data with Hive and Parquet
- Advanced operations like Joins, Aggregations, and GroupBy
Integrating Hadoop with Spark
- Connecting Hadoop ecosystem tools like Hive, HBase, and Sqoop with Spark
- Using Spark with HDFS for efficient data storage and processing
- Integrating with Apache Kafka for data streaming and ingestion
- Best practices for data loading and data writing in Hadoop/Spark ecosystems
Performance Tuning and Optimization
- Understanding Spark performance and tuning concepts
- Optimizing RDDs, DataFrames, and Spark jobs for improved performance
- Memory management and garbage collection strategies
- Efficient use of Spark’s Catalyst optimizer and Tungsten execution engine
Real-time Data Processing with Spark Streaming
- Introduction to Spark Streaming for real-time data processing
- Working with DStreams and structured streaming in Spark
- Implementing real-time data pipelines with Spark Streaming
- Integrating with Kafka, Flume, and Kinesis for real-time data ingestion
Advanced Hadoop and Spark Topics
- Implementing machine learning with MLlib in Spark
- Using GraphX for graph processing with Spark
- Data lineage, versioning, and audit tracking in big data applications
- Security in the Hadoop ecosystem (Kerberos, HDFS encryption)
Deploying Big Data Applications on Hadoop and Spark
- Deploying Spark jobs to the cluster using YARN
- Running and monitoring Hadoop MapReduce and Spark applications on the cluster
- Troubleshooting and debugging Spark and Hadoop applications
- Managing data pipelines and automating workflows with Apache Airflow

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Course: SC-200: Microsoft Security Operations Analyst
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Course: AZ-700: Designing and Implementing Microsoft Azure Networking Solutions
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