Big Data Analytics using Hadoop and Spark

Introduction:

The Big Data Analytics using Hadoop and Spark course is designed to equip participants with the necessary skills to manage and analyze massive datasets using the most popular big data technologies—Hadoop and Apache Spark. As companies generate increasing amounts of data, these frameworks have become essential for processing, storing, and analyzing large datasets quickly and efficiently. This course focuses on understanding big data concepts, Hadoop ecosystem components, and Spark's in-memory processing capabilities, enabling participants to perform complex analytics in real-time.

Course Objective:

By the end of this course, participants will:

• Understand the key concepts of Big Data, Hadoop, and Apache Spark.

• Gain practical experience in processing large datasets using Hadoop's HDFS and MapReduce.

• Learn to implement Spark's in-memory computing for faster data processing.

• Explore Hadoop ecosystem components like Hive, Pig, HBase, and YARN.

• Master working with structured and unstructured data for Big Data analytics.

• Learn how to set up a Hadoop cluster and optimize Spark applications.

• Perform real-time data analytics using Apache Spark Streaming.

• Develop an understanding of big data storage, processing, and visualization for actionable business insights.

Course Outline:

Module 1: Introduction to Big Data and Hadoop Ecosystem

• Overview of Big Data: Volume, velocity, variety, and veracity.

• Introduction to Hadoop and its importance in big data management.

• Core Hadoop components: HDFS (Hadoop Distributed File System) and MapReduce.

• Understanding the Hadoop ecosystem: Hive, Pig, HBase, and YARN.

• Hands-On: Setting up Hadoop on a local machine and understanding the architecture.

Module 2: Hadoop Distributed File System (HDFS)

• Overview of HDFS and its role in storing large datasets.

• Key HDFS concepts: Blocks, replication, and fault tolerance.

• Using HDFS commands to manage files in a distributed environment.

• Hands-On: Uploading and retrieving data from HDFS.

Module 3: Data Processing with MapReduce

• Understanding the MapReduce programming model for parallel data processing.

• Writing basic MapReduce jobs using Java or Python.

• Optimizing MapReduce jobs for better performance.

• Hands-On: Implementing a MapReduce job to analyze large datasets.

Module 4: Introduction to Apache Spark

• Overview of Apache Spark and its advantages over MapReduce.

• In-memory computing and how it enhances data processing speed.

• Core components of Spark: Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.

• Hands-On: Setting up Apache Spark and performing basic transformations and actions on RDDs (Resilient Distributed Datasets).

Module 5: Data Processing with Spark Core and RDDs

• Working with RDDs: Creating, transforming, and performing actions on RDDs.

• Understanding lazy evaluation in Spark and optimizing jobs.

• Partitioning in Spark for distributed data processing.

• Hands-On: Implementing an RDD-based big data application in Spark.

Module 6: Structured Data Processing with Spark SQL

• Introduction to Spark SQL and working with structured data.

• Creating and querying DataFrames and Datasets.

• Using Spark SQL for complex query execution and performance tuning.

• Hands-On: Writing SQL queries in Spark and integrating with databases.

Module 7: Real-Time Data Processing with Spark Streaming

• Understanding real-time data processing and the concept of micro-batching in Spark.

• Working with Spark Streaming to process data streams in real time.

• Integrating Spark Streaming with sources like Kafka, Flume, and HDFS.

• Hands-On: Implementing a real-time data processing pipeline using Spark Streaming.

Module 8: Machine Learning with Spark MLlib

• Introduction to MLlib, Spark's machine learning library.

• Implementing popular algorithms: Classification, regression, clustering, and collaborative filtering.

• Building and tuning machine learning models using MLlib.

• Hands-On: Implementing machine learning algorithms on large datasets using Spark MLlib.

Module 9: Managing Big Data with Hive and Pig

• Introduction to Hive: A data warehouse infrastructure for Hadoop.

• Writing HiveQL queries for big data analysis.

• Using Pig for high-level data processing in Hadoop.

• Hands-On: Writing Hive queries and Pig scripts to analyze large datasets in Hadoop.

Module 10: Big Data Storage with HBase and Cassandra

• Introduction to HBase: A NoSQL database for Hadoop.

• Understanding column-family databases and how HBase stores data.

• Basics of Cassandra and its distributed storage model.

• Hands-On: Setting up HBase and performing CRUD operations on big data.

Module 11: Optimizing and Tuning Spark Applications

• Understanding Spark's execution model and tuning for performance.

• Optimizing memory usage and partitioning in Spark jobs.

• Using Spark’s Catalyst optimizer for query performance improvement.

• Hands-On: Tuning Spark jobs for real-world performance improvements.

Module 12: Cluster Management with YARN and Spark

• Overview of YARN (Yet Another Resource Negotiator) in Hadoop.

• Managing resources and scheduling jobs in a Hadoop cluster.

• Introduction to Spark Standalone Cluster and integrating with YARN.

• Hands-On: Setting up a Spark cluster on YARN and managing jobs.

Final Project and Certification Preparation:

• Final project: Designing and implementing a big data analytics solution using Hadoop and Spark.

• Practice test for certification exams like Cloudera Certified Associate (CCA) and Hortonworks Certified Associate (HCA).

• Exam preparation and guidance for big data certifications.

Course Duration: 40-60 hours of instructor-led or self-paced learning.
Delivery Mode: Instructor-led online/live sessions or self-paced learning modules.
Target Audience: Data engineers, data analysts, developers, and IT professionals looking to specialize in big data analytics using Hadoop and Spark.