Full Stack Data Engineering with Big Data Bootcamp with Job Assurance [English]
Big Data and Data Engineering Course Key Highlights
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Big Data and Data Engineering Course Curriculum
💻 Python
💻 SQL
🔵 Big Data Hadoop
🔵 PySpark
💻 Kafka
💻 Power BI
💻 AWS
💻 MongoDB
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1. Introduction to Python
- Why Learn Python Programming?
- What is Python?
- Python Applications
- Platform Dependency
- Features of Python
- Limitations of Python
- History of Python
2. Writing Our First Python Program
- Python Installation
- Installing PyCharm
- What is IDLE?
- Python Code Execution Flow
- Hello World Program
3. Data Types in Python
- Statements, Indentation, and Comments
- Print and Input Functions
- Identifiers, Variables, and Data Types
4. Input and Output in Python
- Input/Output Functions
- Formatted Strings and Replacement Operators
- Using the format() Method
5. Operators in Python
- Types of Operators
- Number System Conversion
6. Control Statements in Python
- If-Else and Elif Statements
- Loops and Patterns
7. Loops in Python
- While and For Loops
- Nested Loops
8. Strings in Python
- String Basics
- String Built-in Functions
9. Collections in Python
- Lists, Tuples, Sets, and Dictionaries
- Frozen Sets and Byte Arrays
10. Python Functions
- Creating Functions
- Arguments and Parameters
- Global Variables and eval() Function
11. Recursion in Python
- Recursive Programming
- Solve problems like factorial, Fibonacci series, and reversing numbers using recursion
12. Lambda Functions in Python
- Anonymous Functions
- Using Lambda with Filter and Map
13. Arrays in Python
- Array Basics
- Using NumPy for Arrays
14. Searching and Sorting in Python
- Binary Search
- Bubble Sort
15. Object-Oriented Programming (OOP) in Python
- Understanding OOP Concepts
- Special Methods and Overloading
- Static and Inner Classes
- Inheritance and Abstract Classes
16. Exception Handling and File Handling in Python
- Try-Except-Finally Blocks
- User-Defined Exceptions
- Working with Files
- Using Pickle and CSV
- Advanced File Operations
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1. Introduction to SQL
- Why SQL?
- Importance of SQL
- What is SQL?
- Key features of SQL
- Purpose of SQL
- How is SQL Used?
2. DBMS and RDBMS
- Database Management System (DBMS)
- Relational Database Management System (RDBMS)
- Differences Between DBMS and RDBMS
3. Understanding Databases
- Database vs. Database Server
- What is a Single Database?
- What is a Database Server?
- Client-Server Architecture
- MySQL Installation
4. SQL Commands – Part 1
- What Are Commands in SQL?
- Types of SQL Commands
- MySQL Data Types
- DDL Commands
5. SQL Commands – Part 2
- DML Command Statements
- Work with INSERT, SELECT, DELETE, and UPDATE commands.
- Installing SQLyog
- Using MySQL Workbench
- Relational Operators
6. Working with SQL
- Aggregate Functions
- Nested Queries
7. Hands-On SQL
- ORDER BY Clause
- GROUP BY and HAVING Clause
- SQL Aliases
8. Constraints in SQL – Part 1
- What Are Constraints?
- NULL Values
- IS NULL and IS NOT NULL
9. Constraints in SQL – Part 2
- Primary Key and Foreign Key
- CHECK and DEFAULT Constraints
- NULL Function
- Auto Increment
10. Joins in SQL
- What Are Joins?
- Types of Joins
- Why Use Joins?
- Joining Tables in SQL
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1. The Big Picture of Big Data
- What is Big Data?
- Necessity of Big Data and Hadoop in the Industry
- Paradigm Shift to Big Data Tools
- Dimensions of Big Data
- Data Explosion in the Industry
- Big Data Implementations
- Technologies for Handling Big Data
- Limitations of Traditional Systems
- Future of Big Data
2. Demystifying Hadoop
- Why Hadoop is Central to Big Data
- Introduction to Hadoop Framework
- Hadoop Architecture and Design Principles
- Components of the Hadoop Ecosystem
- Hadoop Flavors
3. Setup and Installation of Hadoop
- Single-Node Hadoop Cluster Setup
- Hadoop Environment Setup
- Pseudo-Distributed Mode
- Multi-Node Cluster Setup
- Cloud Setup
- Troubleshooting
4. HDFS – The Storage Layer
- Introduction to HDFS
- HDFS Daemons and Architecture
- Data Flow and Storage Mechanism
- HDFS Features
- Adding and Commissioning Nodes
- HDFS APIs and Web UI
5. A Deep Dive into MapReduce
- What is MapReduce?
- MapReduce Execution Flow
- Components of MapReduce
- Word Count Example
- Optimizing MapReduce Jobs
- Fault-Tolerance and Data Locality
- Working with Combiners
6. Hive – Data Analysis Tool
- Apache Hive
- Introduction and architecture of Hadoop Hive
- Hive shell and running HQL queries
- Hive DDL and DML operations
- Hive execution flow
- Schema design and Hive operations
7. Deep Dive into Hive
- Difference between Schema-on-Read and Schema-on-Write in Hive
- Need for RDBMS
- Limitations of the default meta-store
- Using SerDe to handle different types of data
- Optimization of performance using partitioning
- Different Hive applications
- Use cases of Hive
8. Data Collection using Sqoop
- Introduction to Apache Sqoop
- Need for Apache Sqoop
- Working of Sqoop
- Importing data from RDBMS to HDFS
- Exporting data to RDBMS from HDFS
- Conversion of data import/export queries into MapReduce jobs
9. NoSQL Database – HBase
- Introduction to Apache HBase
- Internals of the HBase architecture
- The HBase Master and Slave Model
- Column-oriented, 3-dimensional, schema-less datastores
- Data modeling in Hadoop HBase
- Storing multiple versions of data
- Data high-availability and reliability
- HBase vs HDFS
- HBase vs RDBMS
- Work with HBase using the shell
10. Data Collection using Flume
- Introduction to Apache Flume
- Flume Architecture and Components
- Reliable and Scalable Data Collection
- Multi-Tier Flume Flows
- Collecting Data with Flume
11. Apache YARN & advanced concepts in the latest version
- Introduction to YARN
- YARN and its ecosystem
- Daemon architecture in YARN
- Master of YARN
- Slave of YARN
- Requesting resources from the application master
- Dynamic slots
- Application execution flow
- MapReduce version 2 application over Yarn
- Hadoop Federation and Namenode HA
12. Real-Life Project on Big Data
A live Big Data Hadoop project based on industry use-cases using Hadoop components like Pig, HBase, MapReduce, and Hive.
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1. Apache Spark Frameworks
- Introduction to Spark Components and Architecture
- Spark Deployment Modes
- Spark Web UI
2. PySpark
- Introduction to PySpark Shell
- Submitting PySpark Jobs
- Writing Your First PySpark Job Using Jupyter Notebook
3. Spark RDDs
- Introduction to Spark RDDs (Resilient Distributed Datasets)
- Challenges in Traditional Computing
- Creating RDDs
- RDD Persistence and Caching
4. Dive into RDDs
- General Operations on RDDs
- Key-Value Pairs in RDDs
- RDD Lineage
- Partitioning in RDDs
- Passing Functions to Spark
5. PySpark SQL and DataFrames
- Introduction to Spark SQL
- Spark SQL Architecture
- User-Defined Functions (UDFs)
- DataFrames
- Loading Data from Different Sources
- Performance Tuning in Spark SQL
- Spark-Hive Integration
6. PySpark Streaming
- Introduction to Spark Streaming
- Spark Streaming Workflow
- StreamingContext Initialization
- Working with DStreams
- Windowed Operators
- Stateful Operators
7. PySpark Machine Learning (MLlib)
- Introduction to Machine Learning
- Introduction to MLlib
- Features and Tools of MLlib
8. Machine Learning Algorithm in PySpark
- Types of Machine Learning Algorithms
- Supervised Learning: Classification, regression, and more.
- Unsupervised Learning: Clustering and dimensionality reduction techniques.
- MLlib Workflow Utilities
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1. Unveiling Apache Kafka
- Introduction to Big Data
- Messaging Queue Essentials
- The Demand for Distributed Messaging Queues
- Traditional Messaging Solutions
- The Case for Apache Kafka
2. Introduction to Kafka
- What is Apache Kafka?
- Kafka Features and Key Terminologies
- Kafka’s High-Level Architecture
- Real-World Applications
3. Kafka Architecture in Depth
- Understanding Kafka Internals
- Key Kafka Components
- Kafka Versions
4. Kafka Brokers Demystified
- How Kafka Brokers Operate
- Broker Deployment Strategies
- Managing Multiple Brokers on a Single Machine
- Decommissioning Brokers
5. Kafka Producers Explained
- Basics of Kafka Producers
- Kafka Producer Architecture
- Partitioning Strategies
- Working with Producer Java API
- Synchronous vs. Asynchronous Producers
- Producer Configurations
6. Understanding Kafka Consumers
- Fundamentals of Kafka Consumers
- Consumer Queuing and Broadcasting
- Kafka Consumer Java API
- Hands-On Activities
7. Kafka Mirroring
- What is Kafka Mirroring?
- How Mirroring Works
- The Role of Mirror Maker
8. Kafka Topics and Partitions
- Managing Kafka Topics
- Performance Optimization
- Partitioning Explained
- Partition Reassignment
9. Replication in Kafka
- Ensuring High Availability
- In-Sync Replication (ISR)
- Types of Replication
- Hands-On Practice
10. Introduction to Zookeeper
- What is Zookeeper?
- Leader Election in Kafka
- Zookeeper Architecture
- Setting Up Zookeeper
11. Kafka Installation and Setup
- Preparing the Kafka Environment
- Configuring Kafka Components
- Single-Node and Multi-Node Deployment
- Balancing Leadership and Scaling Clusters
- Troubleshooting Kafka Clusters
12. Advanced Kafka Operations
- Working with Multiple Topics
- Application Development
- Data Buffering in Kafka
- Best Practices
- Consumer Types and Grouping
- Multi-Threaded Consumers
- Advanced Consumer Configurations
- Log Segmentation and Data Retention
- Monitoring Kafka Clusters
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1. Introduction to Power BI
- What is BI and Power BI?
- Downloading and Installing Power BI
- Understanding the Power BI Workflow
- Power BI Desktop UI
2. Power Query in Power BI
- Introduction to Power Query
- Power Query Editor
- Power Query Data Types
- Text Operations in Power Query
- Column and Data Preview Operations
- Group By Functionality
3. Data Transformation in Power BI
- Conditional Columns
- Pivot and Unpivot Columns
- Merge and Append Queries
- Append Using Folder Data Source
4. Data Modeling and DAX in Power BI
- Data Modeling Part 1
- Data Modeling Part 2
- Introduction to DAX (Data Analysis Expressions) – Part 1
- DAX Advanced Concepts – Part 2
- DAX in Practice – Part 3
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1. Introduction to AWS
- Understanding Cloud Computing and AWS
- AWS Global Infrastructure
- AWS Management Console
- Account Setup
2. AWS Fundamentals
- AWS Service Categories
- Regions and Availability Zones
- Identity and Access Management (IAM)
- AWS Pricing Models
3. Compute Services
- Amazon EC2
- Auto Scaling and Elastic Load Balancing
- Amazon ECS and Kubernetes
- AWS Lambda
4. Storage and Content Delivery
- Amazon S3 and Glacier
- Amazon EBS and Instance Store
- CloudFront
5. Database Services
- Amazon RDS
- Amazon DynamoDB
- Amazon Redshift
- Amazon Aurora
6. Networking and Content Delivery
- Amazon VPC
- Security Groups and NACLs
- On-Premises Connectivity
- Amazon Route 53
7. Security and Identity Services
- IAM Advanced Concepts
- Data Encryption
- Security Best Practices
- AWS CloudTrail
8. Application Services
- Amazon SNS and SQS
- AWS Step Functions
- Amazon Kinesis
9. DevOps and Automation
- AWS CloudFormation
- AWS CodePipeline
- AWS OpsWorks
10. Cost Management
- AWS Billing and Cost Allocation
- Cost Optimization
- AWS Trusted Advisor
11. Case Studies and Practical Scenarios
- Scalable Web Applications
- Migrating Applications to AWS
- Disaster Recovery
12. AWS Certification Preparation
- Certification Tracks
- Exam Tips
- Practice Exams
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1. Introduction to MongoDB
- What is MongoDB?
- NoSQL vs. SQL Databases
- MongoDB Features and Advantages
- Installation and Setup
2. MongoDB Data Modeling
- JSON and BSON Data Formats
- Schema Design in MongoDB
- Data Types and Field Conventions
- Indexing and Its Importance
3. MongoDB CRUD Operations
- Creating and Inserting Documents
- Querying Documents with find()
- Updating Documents
- Deleting Documents
- Aggregation Pipeline for Data Transformation
4. Querying and Indexing
- Query Optimization and Plans
- Types of Indexes
- Index Strategies
- Query Profiling and Optimizatio
5. Data Aggregation
- Basics of Aggregation Framework
- Grouping and Projection Stages
- Conditional Operators
- Pipeline Optimization
- Real-World Aggregation Examples
6. Advanced MongoDB Features
- Transactions and Multi-Document ACID Transactions
- Change Streams
- Full-Text Search
- Geospatial Queries
- Time-Series Data and Aggregation
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Data Engineering with Big Data Roadmap
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Our learners are working in leading organizations
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TechVidvan’s Career Services
Career Support
Complete support from our dedicated placement team until you get a job.
Dedicated Instructor
Expert data science career instructor to guide you throughout the program.
Resume Building
Create professional, ATS-friendly resumes to impress recruiters.
Lifetime Learning Access
Get lifetime access to all courses for continuous learning and growth.
Job Application
Streamline your job applications with expert guidance and tools.
Job Guarantee Programs
Secure your future with programs guaranteeing jobs or money back.
TechVidvan Internship
Gain real-world experience with career-boosting internships.
Job Portal Access
Offer exclusive access to job listings through a dedicated job portal.
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Why Join TechVidvan’s Bootcamp
Big Data and Data Engineering Course FAQs
What is a Big Data and Data Engineering Course?
This Big Data and Data Engineering course covers essential skills for managing, processing, and analyzing large datasets in real-time environments.
Why Should I Learn Big Data and Data Engineering?
Big Data and Data Engineering skills are highly valued, with increasing demand in fields like finance, healthcare, e-commerce, and more.
What Are the Career Opportunities After Completing a Big Data Course?
Careers include Big Data Engineer, Data Architect, Data Analyst, Machine Learning Engineer, and more.
Do I Need Programming Knowledge for a Big Data Course?
Basic knowledge of programming, especially in Python or Java, is helpful for understanding data processing and engineering concepts.
How is Big Data Different from Data Science?
Big Data focuses on handling large volumes of data, while Data Science emphasizes extracting insights and creating models from data.
Is There Demand for Big Data and Data Engineering Skills?
Yes, companies in various sectors are actively hiring Big Data professionals to manage and analyze data for decision-making.
Can I Take This Data Engineering Course with No Prior Experience?
Yes, this Big Data and Data Engineering course is designed for beginners and covers all necessary fundamentals and tools.
Are There Hands-On Projects in This Big Data Course?
Yes, the course includes hands-on projects that allow you to build data pipelines, process data in real time, and work on cloud platforms.
What’s the Difference Between Big Data Engineer and Data Scientist?
Data Engineers focus on data infrastructure and processing, while Data Scientists analyze data and build predictive models.
Is Certification Available After Completing the Big Data Course?
Yes, a certification is provided upon completion, which can be valuable for job applications and career growth.
