Apache Storm: Stream Processing And Big Data Analytics

Harness the power of Apache Storm for lightning-fast stream processing and real-time data analytics!

What you'll learn

Understand the architecture and core components of Apache Storm

Configure and install Apache Storm on various platforms

Master stream processing concepts like spouts, bolts, and stream grouping

Develop, deploy, and manage Storm topologies for real-time data analytics

Optimize Storm applications for parallel processing and fault tolerance

Requirements

Basic knowledge of Big Data technologies (e.g., Hadoop). Familiarity with Java programming. Understanding of distributed systems. A computer with at least 4GB RAM.

Description

Apache Storm is a distributed real-time computation system, enabling fast and reliable stream processing. This course, "Mastering Apache Storm: Real-Time Stream Processing and Big Data Analytics," is designed to guide you through the fundamentals of Apache Storm, its architecture, and hands-on implementation for efficient stream processing.Section 1: IntroductionKickstart your journey into real-time stream processing with an overview of Apache Storm.Key Topics Covered:Lecture 1: IntroductionAn overview of stream processing and Apache Storm’s capabilities in handling real-time data.By the end of this section, you’ll understand the basics of stream processing and the role Apache Storm plays in the Big Data landscape.Section 2: HistoryDive into the background and evolution of Apache Storm, understanding its origins and significance in the Big Data ecosystem.Key Topics Covered:Lecture 2: Description of HadoopIntroduction to Hadoop and its role in Big Data processing.Lecture 3: Storm IntroductionAn introduction to Apache Storm and its use cases for real-time data processing.Lecture 4: Apache Storm HistoryThe evolution of Apache Storm and its impact on real-time analytics.By the end of this section, you'll have a historical perspective on Apache Storm and its relevance to Big Data technologies.Section 3: FeaturesExplore the unique features and architecture of Apache Storm that set it apart as a real-time data processing system.Key Topics Covered:Lecture 5: Features of Apache StormOverview of Storm’s features like scalability, fault-tolerance, and distributed processing.Lecture 6: Architecture of Apache StormIntroduction to Storm's architecture, including its core components.Lecture 7: Architecture Explanation in DetailA deep dive into Storm's architecture for efficient data flow management.Lecture 8: TopologyUnderstanding Storm topologies and how they define data flow.Lecture 9: Spouts and BoltsKey components of Storm: Spouts (data sources) and Bolts (data processors).Lecture 10: StreamExplanation of data streams and their role in Storm’s processing model.By the end of this section, you’ll be proficient in the architecture and key components of Apache Storm.Section 4: InstallationLearn how to set up and configure Apache Storm on your system to start processing real-time data streams.Key Topics Covered:Lecture 11: Installation ProcessStep-by-step guide to installing Apache Storm, including system requirements and configurations.By the end of this section, you’ll be able to install and configure Apache Storm on various platforms.Section 5: ConceptsMaster core concepts like stream grouping, task management, and reliability to optimize data processing.Key Topics Covered:Lecture 12: Stream GroupingDifferent types of stream grouping techniques in Storm (Shuffle, Fields, All, etc.).Lecture 13: Stream Grouping ContinueAdvanced stream grouping methods for optimized data flow.Lecture 14: ReliabilityEnsuring message reliability and fault tolerance in Storm topologies.Lecture 15: TasksUnderstanding tasks and their role in Storm’s parallel processing.Lecture 16: WorkersHow workers manage processing units in Storm’s distributed architecture.By the end of this section, you’ll have a strong grasp of core concepts to optimize your Storm topologies.Section 6: Java InstallationGet your development environment ready with Java, Zookeeper, and Eclipse for building Storm applications.Key Topics Covered:Lecture 17: Java Installation and ZookeeperInstalling Java and Zookeeper for Storm’s coordination service.Lecture 18: Zookeeper InstallationStep-by-step guide to setting up Zookeeper, a crucial component for Storm.Lecture 19: Eclipse InstallationSetting up the Eclipse IDE for Java-based Storm development.Lecture 20: Command Line ClientUsing the command line client to manage Storm topologies.Lecture 21: Parallelism in Storm TopologyTechniques for optimizing parallelism in Storm to boost performance.By the end of this section, you’ll be fully equipped with a development environment for building and running Apache Storm applications.Conclusion:This course provides a comprehensive guide to mastering Apache Storm for real-time data processing. By the end of the course, you will be proficient in using Storm to build robust, scalable, and efficient real-time applications.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: History

Lecture 2 Description of Hadoop

Lecture 3 Storm Introduction

Lecture 4 Apache Storm History

Section 3: Features

Lecture 5 Features of Apache Storm

Lecture 6 Architecture of Apache Storm

Lecture 7 Architecture Explanation in Detail

Lecture 8 Topology

Lecture 9 Spouts and Bolts

Lecture 10 Stream

Section 4: Installation

Lecture 11 Installation Process

Section 5: Concepts

Lecture 12 Stream Grouping

Lecture 13 Stream Grouping Continue

Lecture 14 Reliability

Lecture 15 Tasks

Lecture 16 Workers

Section 6: Java Installation

Lecture 17 Java Installation and Zookeeper

Lecture 18 Zookeeper installation

Lecture 19 Eclipse Installation

Lecture 20 Command line Client

Lecture 21 Parallelism in Storm Topology

Big Data Engineers looking to dive into real-time data processing,Data Analysts aiming to harness streaming data for analytics,Software Developers interested in building real-time applications with Apache Storm,IT Professionals and Enthusiasts keen to learn stream processing frameworks