Big Data Engineer

Big Data, Hadoop, and Python for Beginners

Module 1: Introduction to Big Data

1. Understanding Big Data
2. Exploring Big Data Sources
3. Categorizing Big Data
4. Characteristics of Big Data
5. Real-world Use-cases of Big Data

Module 2: Linux Basics

1. Creating and Managing Directories
2. Navigating Directories
3. Accessing Manual Pages and Help
4. Working with Vi Editor
5. File Management: Creating, Copying, Renaming, Removing, and Moving Files
6. Listing Files and Directories
7. Displaying File Contents

Module 3: Introduction to Python

1. What is Python?
2. Python Installation
3. Python Basics: Variables, Data Types, and Operators
4. Conditionals and Loops
5. Break and Continue Statements
6. Functions and Modules
7. File Handling: Reading and Writing Files

Module 4: Advanced Python Concepts

1. Classes and Objects
2. Inheritance and Multiple Inheritance
3. Exception Handling with Try-Except
4. Using Finally in Exception Handling

Module 5: Python Strings and Collections

1. String Basics and Methods

2. Splitting and Joining Strings
3. String Formatting Functions
4. List Basics and Methods
5. Tuples, Sets, and Frozen Sets
6. Dictionaries
7. Iterators, Generators, and Decorators
8. List, Set, and Dictionary Comprehensions

Module 6: Introduction to Hadoop

1. Comparing Traditional RDBMS and Hadoop
2. Understanding Hadoop
3. The History of Hadoop
4. Hadoop Architecture Fundamentals

Module 7: HDFS

1. Overview of HDFS Components
2. Understanding Block Placement and Rack Awareness
3. HDFS Read/Write Operations
4. Limitations of Hadoop 1.X
5. Introduction to Hadoop 2.X
6. High Availability in Hadoop

Module 8: MapReduce

1. Introduction to MapReduce (MRv1)
2. MapReduce Workflow
3. Communication between Job Tracker and Task Tracker
4. Anatomy of a MapReduce Job Submission
5. Introduction to MapReduce-2 (YARN)
6. Limitations of MRv1 and YARN Architecture
7. Node Manager and Resource Manager

Module 9: Apache Hive

1. Introduction to Hive
2. Hive Use-cases and Limitations
3. Hive Architecture and Meta Store DB
4. Working with Internal and External Tables
5. Hive Operations

6. Static and Dynamic Partitioning
7. Bucketing and Bucketing with Sorting
8. File Formats in Hive
9. Hive Performance Tuning

Module 10: Apache Sqoop

1. Introduction to Sqoop
2. Sqoop Architecture
3. Listing Databases and Tables
4. Setting Passwords and Using Options File
5. SqoopEval
6. Sqoop Import: Target Directory and Warehouse Directory
7. Managing Mappers in Sqoop
8. Sqoop Import Life Cycle
9. Using Split-by Clause
10. Importing All Tables
11. Importing and Exporting Hive Tables

Module 11: Introduction to Apache Spark

1. Understanding Apache Spark and Its Applications
2. Spark History and Unification
3. Comparing Spark Ecosystem with Hadoop
4. Integrating Spark with Hadoop
5. Introduction to Spark's Python and Scala Shells
6. Spark Standalone Cluster Architecture and Application Flow

Module 12: Programming with RDDs, DataFrames

1. RDD Basics, Characteristics, and Creation
2. RDD Operations: Transformations and Actions
3. RDD Types and Lazy Evaluation
4. Persistence (Caching)
5. Advanced Spark Programming
6. Accumulators and Fault Tolerance
7. Broadcast Variables
8. Custom Partitioning
9. Working with Different File Formats
10. Hadoop Input and Output Formats
11. Connecting to Diverse Data Sources

Module 13: Spark SQL

1. Linking with Spark SQL
2. Initializing Spark SQL
3. DataFrames and Caching
4. Case Classes and Inferred Schema
5. Loading and Saving Data
6. Apache Hive Integration
7. Data Sources: Parquet and JSON
8. Spark SQL User-Defined Functions (UDFs)

Module 14: Kafka and Spark Streaming

1. Introduction to Kafka
2. Kafka Producer and Consumer APIs
3. Ingesting Web Server Logs into Kafka
4. Introduction to Spark Streaming
5. Introduction to HBase
6. Integrating Kafka, Spark Streaming, and HBase

Module 15: Spark on Amazon Web Services (AWS)

1. AWS Overview
2. Setting Up an AWS Account
3. Configuring Cygwin on Windows
4. Understanding AWS Pricing
5. Creating and Managing EC2 Instances
6. AWS CLI Setup in Windows
7. S3 Bucket Management
8. AWS Security: IAM Users, Groups, and Policies
9. AWS Best Practices
10. Creating Roles for EC2 Instances
11. Introduction to Amazon EMR
12. EMR Concepts
13. Setting Up EMR with Spark Cluster
14. Submitting Spark Jobs on EMR Cluster
15. Validating Results and Terminating EMR Cluster

Module 16: Interview Preparation

1. Real-Time Projects

2. Deployment on Multiple Platforms
3. Project Explanation for Interviews
4. Data Engineer Roles and Responsibilities
5. Day-to-Day Work of a Data Engineer
6. One-on-One Resume Discussion
7. Mock Interviews for Each Student
8. Real-Time Interview Questions

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