Big Data Hadoop and Spark Developer - eLearning

Big Data Hadoop and Spark Developer - eLearning

The Big Data Hadoop and Spark Developer Course is designed to provide you with an in-depth understanding of Apache Spark fundamentals and the Hadoop framework, equipping you with the skills needed to excel as a Big Data Developer. Through this program, you will gain hands-on knowledge of the Hadoop ecosystem and its integration with Spark, enabling you to process and analyze massive datasets efficiently. Learn how the multiple components of Hadoop, such as HDFS and MapReduce, fit seamlessly into the big data processing cycle, preparing you for success in today's data-driven world.

WHAT IS INCLUDED?

• Course and material are in English
• Intermediate for aspiring data engineer
• 1 year access to the self-paced study eLearning platform 24/7
• 11 hours of video content
• 50 hours study time recommended
• Simulation test, Virtual lab and Course-end Project
• No exam for the course but student will get certification of training completion

COURSE OBJECTIVES

• Learn how to navigate the Hadoop ecosystem and understand how to optimize its use
• Ingest data using Sqoop, Flume, and Kafka.
• Implement partitioning, bucketing, and indexing in Hive
• Work with RDD in Apache Spark
• Process real-time streaming data and Perform DataFrame operations in Spark using SQL queries
• Implement User-Defined Functions (UDF) and User-Defined Attribute Functions (UDAF) in Spark

Target Audience

Ideal for a wide range of professionals and individuals who want to advance their careers in big data analytics, data engineering, and data science.

Prerequisites: It is recommended that you have knowledge of Core Java and SQL

• Analytics professionals
• Senior IT professionals
• Testing and mainframe professionals
• Data management professionals
• Business intelligence professionals
• Project managers
• Graduates looking to begin a career in big data analytics

Prerequisites: It is recommended that you have knowledge of Core Java and SQL

Course content

Introduction to Big Data and Hadoop

- Introduction to Big Data and Hadoop
- Introduction to Big Data
- Big Data Analytics
- What is Big Data?
- Four vs of Big Data
- Case Study Royal Bank of Scotland
- Challenges of Traditional System
- Distributed Systems
- Introduction to Hadoop
- Components of Hadoop Ecosystem Part One
- Components of Hadoop Ecosystem Part Two
- Components of Hadoop Ecosystem Part Three
- Commercial Hadoop Distributions
- Demo: Walkthrough of Simplilearn Cloudlab
- Key Takeaways
- Knowledge Check

Hadoop Architecture Distributed Storage (HDFS) and YARN

- Hadoop Architecture Distributed Storage (HDFS) and YARN
- What is HDFS
- Need for HDFS
- Regular File System vs HDFS
- Characteristics of HDFS
- HDFS Architecture and Components
- High Availability Cluster Implementations
- HDFS Component File System Namespace
- Data Block Split
- Data Replication Topology
- HDFS Command Line
- Demo: Common HDFS Commands
- Practice Project: HDFS Command Line
- Yarn Introduction
- Yarn Use Case
- Yarn and its Architecture
- Resource Manager
- How Resource Manager Operates
- Application Master
- How Yarn Runs an Application
- Tools for Yarn Developers
- Demo: Walkthrough of Cluster Part One
- Demo: Walkthrough of Cluster Part Two
- Key Takeaways Knowledge Check
- Practice Project: Hadoop Architecture, distributed Storage (HDFS) and Yarn

Data Ingestion into Big Data Systems and ETL

- Data Ingestion Into Big Data Systems and Etl
- Data Ingestion Overview Part One
- Data Ingestion Overview Part Two
- Apache Sqoop
- Sqoop and Its Uses
- Sqoop Processing
- Sqoop Import Process
- Sqoop Connectors
- Demo: Importing and Exporting Data from MySQL to HDFS
- Practice Project: Apache Sqoop
- Apache Flume
- Flume Model
- Scalability in Flume
- Components in Flume’s Architecture
- Configuring Flume Components
- Demo: Ingest Twitter Data
- Apache Kafka Aggregating User Activity Using Kafka
- Kafka Data Model
- Partitions
- Apache Kafka Architecture
- Demo: Setup Kafka Cluster
- Producer Side API Example
- Consumer Side API
- Consumer Side API Example
- Kafka Connect
- Demo: Creating Sample Kafka Data Pipeline Using Producer and Consumer
- Key Takeaways
- Knowledge Check
- Practice Project: Data Ingestion Into Big Data Systems and ETL

Distributed Processing MapReduce Framework and Pig

- Distributed Processing Mapreduce Framework and Pig
- Distributed Processing in Mapreduce
- Word Count Example
- Map Execution Phases
- Map Execution Distributed Two Node Environment
- Mapreduce Jobs
- Hadoop Mapreduce Job Work Interaction
- Setting Up the Environment for Mapreduce Development
- Set of Classes
- Creating a New Project
- Advanced Mapreduce
- Data Types in Hadoop
- Output formats in Mapreduce
- Using Distributed Cache
- Joins in MapReduce
- Replicated Join
- Introduction to Pig
- Components of Pig
- Pig Data Model
- Pig Interactive Modes
- Pig Operations
- Various Relations Performed by Developers
- Demo: Analyzing Web Log Data Using Mapreduce
- Demo: Analyzing Sales Data and Solving Kpis Using Pig Practice Project: Apache Pig
- Demo: Wordcount
- Key Takeaways
- Knowledge Check
- Practice Project: Distributed Processing - Mapreduce Framework and Pig

Apache Hive

- Apache Hive
- Hive SQL over Hadoop MapReduce
- Hive Architecture
- Interfaces to Run Hive Queries
- Running Beeline from Command Line
- Hive Metastore
- Hive DDL and DML
- Creating New Table
- Data Types Validation of Data
- File Format Types
- Data Serialization
- Hive Table and Avro Schema
- Hive Optimization Partitioning Bucketing and Sampling
- Non-Partitioned Table
- Data Insertion
- Dynamic Partitioning in Hive
- Bucketing
- What Do Buckets Do?
- Hive Analytics UDF and UDAF
- Other Functions of Hive
- Demo: Real-time Analysis and Data Filtration
- Demo: Real-World Problem
- Demo: Data Representation and Import Using Hive
- Key Takeaways
- Knowledge Check
- Practice Project: Apache Hive

NoSQL databases HBase

- NoSQL Databases HBase
- NoSQL Introduction
- Demo: Yarn Tuning
- Hbase Overview
- Hbase Architecture
- Data Model
- Connecting to HBase
- Practice Project: HBase Shell
- Key Takeaways
- Knowledge Check
- Practice Project: NoSQL Databases - HBase

Basics of Functional Programming and Scala

- Basics of Functional Programming and Scala
- Introduction to Scala
- Demo: Scala Installation
- Functional Programming
- Programming With Scala
- Demo: Basic Literals and Arithmetic Programming
- Demo: Logical Operators
- Type Inference Classes Objects and Functions in Scala
- Demo: Type Inference Functions Anonymous Function and Class
- Collections
- Types of Collections
- Demo: Five Types of Collections
- Demo: Operations on List Scala REPL
- Demo: Features of Scala REPL
- Key Takeaways
- Knowledge Check
- Practice Project: Apache Hive

Apache Spark Next - Generation Big Data Framework

- Apache Spark Next-Generation Big Data Framework
- History of Spark
- Limitations of Mapreduce in Hadoop
- Introduction to Apache Spark
- Components of Spark
- Application of In-memory Processing
- Hadoop Ecosystem vs Spark
- Advantages of Spark
- Spark Architecture
- Spark Cluster in Real World
- Demo: Running a Scala Programs in Spark Shell
- Demo: Setting Up Execution Environment in IDE
- Demo: Spark Web UI
- Key Takeaways
- Knowledge Check
- Practice Project: Apache Spark Next-Generation Big Data Framework

Spark Core Processing RDD

- Introduction to Spark RDD
- RDD in Spark
- Creating Spark RDD
- Pair RDD
- RDD Operations
- Demo: Spark Transformation Detailed Exploration Using Scala Examples
- Demo: Spark Action Detailed Exploration Using Scala
- Caching and Persistence
- Storage Levels
- Lineage and DAG
- Need for DAG
- Debugging in Spark
- Partitioning in Spark
- Scheduling in Spark
- Shuffling in Spark
- Sort Shuffle Aggregating Data With Paired RDD
- Demo: Spark Application With Data Written Back to HDFS and Spark UI
- Demo: Changing Spark Application Parameters
- Demo: Handling Different File Formats
- Demo: Spark RDD With Real-world Application
- Demo: Optimizing Spark Jobs
- Key Takeaways
- Knowledge Check
- Practice Project: Spark Core Processing RDD

Spark SQL Processing of Data Frames

- Spark SQL Processing DataFrames
- Spark SQL Introduction
- Spark SQL Architecture
- Dataframes
- Demo: Handling Various Data Formats
- Demo: Implement Various Dataframe Operations
- Demo: UDF and UDAF
- Interoperating With RDDs
- Demo: Process Dataframe Using SQL Query
- RDD vs Dataframe vs Dataset
- Practice Project: Processing Dataframes
- Key Takeaways
- Knowledge Check
- Practice Project: Spark SQL - Processing Dataframes

Spark MLib Modelling BigData with Spark

- Spark Mlib Modeling Big Data With Spark
- Role of Data Scientist and Data Analyst in Big Data
- Analytics in Spark
- Machine Learning
- Supervised Learning
- Demo: Classification of Linear SVM
- Demo: Linear Regression With Real World Case Studies
- Unsupervised Learning
- Demo: Unsupervised Clustering K-means
- Reinforcement Learning
- Semi-supervised Learning
- Overview of Mlib
- Mlib Pipelines
- Key Takeaways
- Knowledge Check
- Practice Project: Spark Mlib - Modelling Big data With Spark

Stream Processing Frameworks and Spark Streaming

- Streaming Overview
- Real-time Processing of Big Data
- Data Processing Architectures
- Demo: Real-time Data Processing Spark Streaming
- Demo: Writing Spark Streaming Application
- Introduction to DStreams
- Transformations on DStreams
- Design Patterns for Using Foreachrdd
- State Operations
- Windowing Operations
- Join Operations Stream-dataset Join
- Demo: Windowing of Real-time Data Processing Streaming Sources
- Demo: Processing Twitter Streaming Data
- Structured Spark Streaming-
- Use Case Banking Transactions
- Structured Streaming Architecture Model and Its Components
- Output Sinks
- Structured Streaming APIs
- Constructing Columns in Structured Streaming
- Windowed Operations on Event-time
- Use Cases
- Demo: Streaming Pipeline
- Practice Project: Spark Streaming
- Key Takeaways
- Knowledge Check
- Practice Project: Stream Processing Frameworks and Spark Streaming

Spark GraphX

- Spark GraphX
- Introduction to Graph
- GraphX in Spark
- GraphX Operators
- Join Operators
- GraphX Parallel System
- Algorithms in Spark
- Pregel API
- Use Case of GraphX
- Demo: GraphX Vertex Predicate
- Demo: Page Rank Algorithm
- Key Takeaways
- Knowledge Check
- Practice Project: Spark GraphX Project Assistance