Apache Spark Etl Frameworks And Real–Time Data Streaming

What you'll learn
Understand the fundamentals of Apache Spark, including Spark Context, RDDs, and transformations
Build and manage Spark clusters on single and multi-node setups
Develop efficient Spark applications using RDD transformations and actions
Master ETL processes by building scalable frameworks with Spark
Implement real-time data streaming and analytics using Spark Streaming
Leverage Scala for Spark applications, including handling Twitter streaming data
Optimize data processing with accumulators, broadcast variables, and advanced configurations
Requirements
Basic knowledge of Python and Java programming
Familiarity with basic Linux commands and shell scripting
Understanding of big data concepts is a plus, but not mandatory
A computer with at least 8GB RAM for running Spark and VirtualBox setups
Description
Introduction:Apache Spark is a powerful open-source engine for large-scale data processing, capable of handling both batch and real-time analytics. This comprehensive course, "Mastering Apache Spark: From Fundamentals to Advanced ETL and Real-Time Data Streaming," is designed to take you from a beginner to an advanced level, covering core concepts, hands-on projects, and real-world applications. You'll gain in-depth knowledge of Spark's capabilities, including RDDs, transformations, actions, Spark Streaming, and more. By the end of this course, you'll be equipped with the skills to build scalable data processing solutions using Spark.Section 1: Apache Spark FundamentalsThis section introduces you to the basics of Apache Spark, setting the foundation for understanding its powerful data processing capabilities. You'll explore Spark Context, the role of RDDs, transformations, and actions. With hands-on examples, you'll learn how to work with Spark's core components and perform essential data manipulations.Key Topics Covered:Introduction to Spark Context and ComponentsUnderstanding and using RDDs (Resilient Distributed Datasets)Applying filter functions and transformations on RDDsPersistence and caching of RDDs for optimized performanceWorking with various file formats in SparkBy the end of this section, you'll have a solid understanding of Spark's core features and how to leverage RDDs for efficient data processing.Section 2: Learning Spark ProgrammingDive deeper into Spark programming with a focus on configuration, resource allocation, and cluster setup. You'll learn how to create Spark clusters on both single and multi-node setups using VirtualBox. This section also covers advanced RDD operations, including transformations, actions, accumulators, and broadcast variables.Key Topics Covered:Setting up Spark on single and multi-node clustersAdvanced RDD operations and data partitioningWorking with Python arrays, file handling, and Spark configurationsUtilizing accumulators and broadcast variables for optimized performanceWriting and optimizing Spark applicationsBy the end of this section, you'll be proficient in writing efficient Spark programs and managing cluster resources effectively.Section 3: Project on Apache Spark - Building an ETL FrameworkApply your knowledge by building a robust ETL (Extract, Transform, Load) framework using Apache Spark. This project-based section guides you through setting up the project structure, exploring datasets, and performing complex transformations. You'll learn how to handle incremental data loads, making your ETL pipelines more efficient.Project Breakdown:Setting up the project environment and installing necessary packagesPerforming data exploration and transformationImplementing incremental data loading for optimized ETL processesFinalizing the ETL framework for production useBy the end of this project, you'll have hands-on experience in building a scalable ETL framework using Apache Spark, a critical skill for data engineers.Section 4: Apache Spark Advanced TopicsThis advanced section covers Spark's capabilities beyond batch processing, focusing on real-time data streaming, Scala integration, and connecting Spark to external data sources like Twitter. You'll learn how to process live streaming data, set up windowed computations, and utilize Spark Streaming for real-time analytics.Key Topics Covered:Introduction to Spark Streaming for processing real-time dataConnecting to Twitter API for real-time data analysisUnderstanding window operations and checkpointing in SparkScala programming essentials, including pattern matching, collections, and case classesImplementing streaming applications with Maven and ScalaBy the end of this section, you'll be able to build real-time data processing applications using Spark Streaming and integrate Scala for high-performance analytics.Conclusion:Upon completing this course, you'll have mastered the fundamentals and advanced features of Apache Spark, including batch processing, real-time streaming, and ETL pipeline development. You'll be prepared to tackle real-world data engineering challenges and enhance your career in big data analytics.
Overview
Section 1: Apache Spark Fundamentals
Lecture 1 Introduction to Apache Spark
Lecture 2 Spark Context
Lecture 3 Spark Components
Lecture 4 Introduction to Spark RDD Basics
Lecture 5 Use of Filter Function
Lecture 6 RDD Transformations in Spark
Lecture 7 RDD Transformations in Spark Continues
Lecture 8 RDD Persistence in Spark
Lecture 9 Group Sort and Actions on Pair RDDs
Lecture 10 Spark File Formats
Lecture 11 Spark File Formats Continues
Section 2: Learning Spark Programming
Lecture 12 Introduction to Apache Spark
Lecture 13 Installation
Lecture 14 Launching Spark Cluster With Single Node
Lecture 15 Basics of Configurations-Resource Allocation
Lecture 16 Installation Virtualbox in Spark
Lecture 17 Creating a New System on the Virtualbox
Lecture 18 Creating a Spark Cluster on Multiple Node
Lecture 19 Creating a Spark Cluster on Multiple Node Continues
Lecture 20 Spark RDD Theory
Lecture 21 Basic RDD Operation
Lecture 22 RDD with Python Array
Lecture 23 Spark Transformation and Actions
Lecture 24 Functions of Flat Map
Lecture 25 Group By Key
Lecture 26 SortBy Key and SortBy
Lecture 27 Functions of Coalescel
Lecture 28 Actions of Transformation
Lecture 29 Count By Value
Lecture 30 Understanding Foreach
Lecture 31 Creating RDDs through Parallelize
Lecture 32 Text File Method for Reading the Files
Lecture 33 Reading the Text Files
Lecture 34 File Handling and RDD Partitions
Lecture 35 Writing Spark Code and Application
Lecture 36 Analyzing the Current Directory Output
Lecture 37 Rewriting the Spark Applications
Lecture 38 Creating the Variable and Accessing the Spark
Lecture 39 Options While Launching Spark
Lecture 40 Functions
Lecture 41 Functions Continue
Lecture 42 Global Variables
Lecture 43 Global Variables Continue
Lecture 44 Accumulators
Lecture 45 Accumulators-Custom Data Types
Lecture 46 Broadcast Variables
Lecture 47 Broadcast Variables Continued
Lecture 48 Create a Dictionary
Lecture 49 RDD Persistence
Lecture 50 Create RDD Youtube
Lecture 51 Storage Level
Lecture 52 RDD are Srialized and Persisted
Lecture 53 Miscellaneous
Lecture 54 Best Practices
Lecture 55 Apache Spark Conclusion
Section 3: Project on Apache Spark - Building an ETL Framework
Lecture 56 Introduction to Project
Lecture 57 Installation of Packages
Lecture 58 Installation of Packages Continue
Lecture 59 Setting up Project Structure
Lecture 60 Exploring Dataset
Lecture 61 Entire Load and Transformations Part 1
Lecture 62 Entire Load and Transformations Part 2
Lecture 63 Entire Load and Transformations Part 3
Lecture 64 Entire Load and Transformations Part 4
Lecture 65 Incremental Load
Lecture 66 Incremental Load Continue
Section 4: Apache Spark Advanced Topics
Lecture 67 Introduction to Connecting to Twitter Using Spark
Lecture 68 Flowchart of Spark
Lecture 69 Components of Spark
Lecture 70 Different Services Running on YARN
Lecture 71 Introduction to Scala
Lecture 72 Case Classes and Pattern Matching
Lecture 73 Installation of Scala
Lecture 74 Variables and Functions
Lecture 75 Variables and Functions Continued
Lecture 76 Loops
Lecture 77 Collections
Lecture 78 More on Collections
Lecture 79 Abstract Class
Lecture 80 Example of the Abstract Class
Lecture 81 Trait
Lecture 82 Example of the Trait
Lecture 83 Exception
Lecture 84 Practical Example of Exceptions
Lecture 85 Customize Exceptions of Scala Project
Lecture 86 Modifiers
Lecture 87 Strings
Lecture 88 Methods in Strings
Lecture 89 Methods in Strings Continued
Lecture 90 Array
Lecture 91 RDD in Spark
Lecture 92 RDD in Spark Continued
Lecture 93 Different Operations
Lecture 94 Transformation Operations
Lecture 95 Action Operations
Lecture 96 Action Operations Continued
Lecture 97 Introduction Spark Streaming
Lecture 98 How to Process the Live Streaming Data
Lecture 99 How to Process the Live Streaming Data Continued
Lecture 100 Windowed Wordcount
Lecture 101 Windowed Wordcount Example
Lecture 102 Check Pointing in Spark
Lecture 103 Check Pointing in Spark Example
Lecture 104 Maven Creation
Lecture 105 Create Scala Project
Lecture 106 Difference between Hadoop 1.x and 2.x
Lecture 107 Connection to Twitter Using Spark Streaming
Lecture 108 How to Connect Twitter Using Spark Application
Lecture 109 More on Connect Twitter Using Spark Application
Data Engineers looking to enhance their skills in big data processing with Spark,Data Scientists aiming to scale their data pipelines using Spark's capabilities,Software Developers interested in mastering distributed data processing,IT Professionals and Analysts seeking to gain hands-on experience in Spark for big data projects,Students and Enthusiasts looking to break into the field of data engineering and big data analytics

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