Numbers That Speak Our Success
70,000+
Career Carved
675+
Hiring Partners
98%
Satisfaction Rate
425%
Highest Hike
Success Stories – They Believed, Learned & Achieved!
Success Isn’t Luck – It’s Learning. They Chose to Upskill – Now They’re in the World’s Best Companies!
“I focused on building strong analytical thinking, and that clarity helped me grow into more impactful roles.”
Angela Binoy 
“Handling complex data challenges boosted my confidence step by step — and that clarity guided my professional journey.”
Sonal Saxena
“I stopped doubting my potential the day I started building projects — that’s when everything began to change for me.”
Avina Garg
“Working on practical AI problems helped me understand the depth of this field — that clarity shaped my growth.”
Shashank Yadav
“Once I focused on improving my logic and structure, everything else started falling into place — and my growth followed naturally.”
Pranit Kumar
“Working on end-to-end data tasks gave me the clarity I was missing — that practical learning shaped my path forward.”
Nishchay Agrawal
Our learners are working in leading organizations
Gain industry-ready skills and earn an official certificate from DataFlair and IBM.
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Full Stack Data Engineering with Big Data Course Key Highlights
40+ Hrs Live
Interactive Sessions
365+
Practical Examples
170+ Hrs
Self-paced Videos
15+
Industry Mentors & SME
09+
Tools & Technologies
65+
Interactive Quizzes
45+
Real-time Projects
70+
Industry Case Studies
790+
Interview Questions
875+
Big Data Tutorials
130+
Coding Assignments
98%
Course Recommendation
Full Stack Big Data and Data Engineering Course Curriculum
💻 Python
💻 SQL
💻 Big Data with Hadoop
💻 Data Engineering
💻 Kafka
💻 Power BI
💻 AWS
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
1. Understanding SQL
- Introduction to SQL
- SQL Prerequisites
- What is SQL
- Purpose of SQL
- MySQL Server Installation
- SQLYog Installation
- Difference Between DBMS and RDBMS
- SQL Database and Database Server
- Comments in SQL
2. Commands in SQL Part – 1
- What are SQL Commands
- SQL DDL Statements
- SQL DDL Statements – Alter and Drop Command
- Practical Implementation of SQL DDL Command
- SQL DML Statements
- SQL DML Statements – Delete and Update Command
- Practical Implementation of SQL DML Command
3. Commands in SQL Part – 2
- SQL TCL Command
- Practical Implementation of TCL Command
- SQL DCL Command
- Practical Implementation of DCL Command
4. Data Types in SQL
- What are Data Type in SQL
- String Data Type in SQL
- Numerical Data Type in SQL
- Date Data Type in SQL
- Practical Implementation of Date Data Type in SQL
5. Operators in SQL
- What are Operators in SQL
- Arithmetic Operator in SQL
- Relational Operator in SQL
- Logical Operator in SQL
- Practical Implementation of Arithmetic, Relational and Logical Operators
- SQL String Operators
- SQL Like and Not Like Command
6. Clauses in SQL Part – 1
- Constraints in SQL
- Practical Implementation of Constraints in SQL
- Order by Clause in SQL
- Practical Implementation of Order by Clause
- GROUP BY and HAVING in SQL
- Practical Implementation of GROUP BY and HAVING in SQL
7. Clauses in SQL Part – 2
- Like Command in SQL
- Practical Implementation of Like Command in SQL
- Between and In Command in SQL
- Practical Implementation of Between and In Command
- ANY and ALL Operators
- Alias in SQL
- Practical Implementation of Alias in SQL
8. SQL Joins
- What are Joins in SQL
- Practical Implementation of SQL Joins
- Practical Implementatin of Self Join
9. SQL Keys
- Primary Key and Foreign Key Constraint in SQL
- Practical Implementation of Primary Key and Foreign Key Constraint
10. Advanced SQL
- Injection in SQL
- Practical Implementation of Injection
- Null Functions
- Check Constraint in SQL
- Practical Implementation of Check Constraint
11. Deep Dive in SQL
- Default Constraint in SQL
- Practical Implementation of Default Constraint
- Null Value in SQL
- Practical Implementations of Null Value in SQL
- Auto Increment in SQL
- Practical Implementation of Auto Increment in SQL
12. Functions in SQL Part – 1
- Aggregate Functions in SQL
- Practical Implementation of Aggregate Functions
- String Functions
- Nested Query in SQL
- Practical Implementation of Nested Query
13. Functions in SQL Part – 2
- SQL Window Function
- Practical Implementation of Window Function
- Bulk Insert in SQL
- Backup and Restore in SQL
14. Projects in SQL
- SQL Library Management System Project
- SQL Sales Data Analysis Project
- SQL Restaurant Billing System Project
- SQL Patient Record System Project
- SQL Vehicle Service Booking System Project
- SQL Simple Inventory ans Sales System Project
- SQL Movie Rating System Project
- SQL E-commerce Product Refund and Return Analytics System Project
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.
1. Data Engineering Lifecycle
- Development Environment Setup: Working with the command line
- Production Environment Setup & Best Practices
- Stages of the Data Pipeline (Generation, Ingestion, Storage, Transformation, and Serving)
2. Create ETL Pipelines
- ETL Concepts: Extract, Transform, Load
- Real-time Data ingestion and transformation
- Data Engineering Pipelines with ELT Best Practices
- Batch Processing vs Streaming Ingestion
- Real-time Streaming vs Micro Batch vs Batch Flow
3. Advanced ETL Flow
- Collect Data from Various Sources and Load it into Target Systems
- Transformation Techniques: Cleaning and Preprocessing Data after Extraction
- Advanced Data Cleaning: Strategies for Real-World Data Issues
- Error Handling & Logging in ETL
4. Real-Time Data Ingestion and Streaming Pipelines
- Real-time Data Ingestion
- Designing a Streaming Pipeline
- Data Queuing for Improved Reliability
- Fan-out – Create Multiple Flows for Real-Time and Batch Processing
- Design and Implement Data Pipeline using Hadoop, Spark, Kafka, Flume, BI
- Enterprise Data Flow Best Practices
5. Data Lake and Storage Strategies
- Design and Architecture of a Complete Data Lake
- Best Practices of Data Mart
- Storage and Archival Policy
- Streaming and Batch Flow with Storage
- Advanced Data Engineering PracticesOLTP vs OLAP
6. Advanced Data Engineering Practices
- Configuring High-Availability and Fault-Tolerant Designs
- Scalability Strategies
- Future-Proof the Deployment for Reliability
7. Data Engineering Project Architecture (Industry Use Cases)
- Live Architecture of E-commerce (Flipkart)
- Live Architecture of Telecom (Jio)
- Live Real-Time Architecture of Media, BFSI, and Other Domains
8. Enterprise Project Flow (Architectural Patterns)
- Layered Architecture Practices for Scalability
- Data Collection and Acquisition
- Data Queuing for improved reliability
- Fan-out – create multiple flows for Real-time and batch processing
9. Apache Spark Frameworks
- Introduction to Spark Components and Architecture
- Spark Deployment Modes
- Spark Web UI
10. PySpark
- Introduction to PySpark Shell
- Submitting PySpark Jobs
- Writing Your First PySpark Job Using Jupyter Notebook
11. Spark RDDs
- Introduction to Spark RDDs (Resilient Distributed Datasets)
- Challenges in Traditional Computing
- Creating RDDs
- RDD Persistence and Caching
12. Dive into RDDs
- General Operations on RDDs
- Key-Value Pairs in RDDs
- RDD Lineage
- Partitioning in RDDs
- Passing Functions to Spark
13. 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
14. PySpark Streaming
- Introduction to Spark Streaming
- Spark Streaming Workflow
- StreamingContext Initialization
- Working with DStreams
- Windowed Operators
- Stateful Operators
15. PySpark Machine Learning (MLlib)
- Introduction to Machine Learning
- Introduction to MLlib
- Features and Tools of MLlib
16. 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
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
1. Introduction to Power BI
- Understanding Business Intelligence & Power BI
- Installing and Setting Up Power BI
- Power BI Workflow: From Data to Insights
- Navigating the Power BI Desktop Interface
2. Mastering Power Query in Power BI
- Overview of Power Query
- Exploring the Power Query Editor
- Understanding Data Types in Power Query
- Text Operations: Splitting, Merging, and Extracting Data
- Column Management: Removing & Previewing Data
- Data Grouping Techniques in Power Query
3. Data Transformation in Power BI
- Creating Conditional Columns for Smart Data Processing
- Handling Missing Data with Fill Up & Fill Down Techniques
- Reshaping Data: Pivoting & Unpivoting Columns
- Combining Data: Merging & Appending Datasets
- Automating Data Imports from Folder Sources
4. Data Modeling & DAX Essentials
- Fundamentals of Data Modeling
- Advanced Data Modeling Techniques
- Introduction to DAX (Data Analysis Expressions)
- Intermediate DAX Functions
- Advanced DAX Formulas & Optimization
5. Creating Interactive Reports in Power BI
- Report Building Essentials
- Enhancing Reports with Advanced Features
- Designing Engaging & Insightful Reports
- Using AI-Powered Visuals for Smarter Reports
- Structuring Reports with Hierarchies & Drilldowns
6. Data Visualization & Analytics – Part 1
- Applying Conditional Formatting for Better Insights
- Using Field Parameters & Interaction Controls
- Enhancing Reports with Dynamic Tooltips & Drillthrough
- Exploring the Analytics Pane for In-Depth Data Analysis
7. Data Visualization & Analytics – Part 2
- Adding Interactive Buttons in Power BI
- Mastering Bookmarks for Navigation
- Advanced Bookmarking Techniques
- Organizing Data with Groups & Bins
- Visualizing Trends with Scatter Charts
8. Power BI Service & Dashboard Management
- Using Sparklines for Quick Data Trends
- Integrating Custom Visuals in Power BI
- Removing & Managing Custom Visuals
- Customizing Reports with Themes & Hex Codes
- Introduction to Power BI Service & Workspace Creation
- Building Dynamic Dashboards in Power BI
- Creating & Managing Apps with Usage Metrics
9. Advanced Power BI Concepts
- Understanding Data Refresh & Scheduled Refresh
- Implementing Incremental Refresh for Large Datasets
- Setting Up Row-Level Security (RLS)
- Semantic Model Endorsement & Certification
- Performance Optimization Strategies in Power BI
- Comparing Import Mode, Direct Query, & Live Connections
- Improving Model Performance for Faster Reports
10. Power BI Case Study
- Real-World Case Study: Connecting & Analyzing Web Data
1. Introduction to Cloud & AWS
- Understanding Traditional IT Infrastructure
- Data Centers: Then vs. Now
- Introduction to Cloud Computing
- Cloud Service Models
- Cloud Deployment Models
2. AWS Fundamentals & Getting Started
- AWS Cloud Overview
- AWS Global Infrastructure: Regions, Availability Zones, Edge Locations
- AWS Free Tier Explained
- Hands-on: Setting Up Your AWS Account
- Exploring AWS Service Categories
3. Identity & Access Management (IAM) – Security First!
- Introduction to IAM: Users, Groups & Roles
- Hands-on: IAM User & Role Creation
- IAM Policies & Permissions
- IAM Security Tools & Best Practices
- AWS CLI for IAM Management
4. Compute Services – AWS EC2 Mastery
- Introduction to Amazon EC2
- Launching Your First EC2 Instance
- EC2 Instance Types & Use Cases
- Security Groups & Network Access
- SSH & Instance Connect
- EC2 Lifecycle: Start, Stop, Terminate
- EC2 Pricing Models: On-Demand, Reserved, Spot Instances
5. Storage Services – EBS, EFS & S3
- Amazon EBS: Persistent Block Storage
- Creating & Managing EBS Volumes
- Snapshots & Backup Strategies
- Amazon EFS: Scalable File Storage
- Deploying Shared File Systems with EFS
- Introduction to Amazon S3
- Creating & Configuring S3 Buckets
- S3 Storage Classes & Lifecycle Policies
- Hosting Static Websites on S3
6. High Availability & Auto Scaling
- Ensuring High Availability in AWS
- Elastic Load Balancers (Application & Network Load Balancers)
- Auto Scaling Groups (ASG) – Scaling for Performance & Cost
- Hands-on: Configuring an Auto Scaling Group
7. AWS Database Services – RDS & NoSQL
- AWS Database Offerings Overview
- Amazon RDS & Aurora
- Hands-on: Deploying an RDS Database
- Amazon DynamoDB: NoSQL Databases at Scale
- Amazon Redshift: Data Warehousing for Big Data
8. Networking & Security – Mastering AWS VPC
- IP Addressing in AWS
- Virtual Private Cloud (VPC) & Its Components
- VPC Hands-on: Creating Subnets & Security Groups
- NACL vs. Security Groups
- VPC Peering & Transit Gateway
9. Infrastructure as Code – AWS CloudFormation
- Introduction to AWS CloudFormation
- Hands-on: Deploying Infrastructure as Code
- AWS Elastic Beanstalk for Application Deployment
- AWS CodePipeline: CI/CD in AWS
10. AWS Security & Compliance – Keeping the Cloud Secure
- Shared Responsibility Model Explained
- AWS Security & Compliance Services
- WAF & Shield for Web Application Protection
- AWS Key Management Service (KMS) for Encryption
- Secrets Manager: Managing Sensitive Credentials
11. Monitoring, Logging & Auditing AWS Resources
- AWS CloudWatch: Monitoring Metrics & Logs
- AWS EventBridge: Automating Event-Driven Actions
- AWS CloudTrail: Tracking API Calls & Security Logs
- AWS Trusted Advisor: Optimizing Security & Costs
12. AWS Messaging & Notification Services
- Introduction to Asynchronous Messaging in AWS
- Amazon SQS: Setting Up Message Queues
- Amazon SNS: Scalable Notifications & Alerts
13. Global Application Delivery & Performance Optimization
- Route 53: DNS Management & Routing Policies
- Amazon CloudFront: Content Delivery Network (CDN)
- AWS Global Accelerator: Optimizing Application Performance
14. AWS Big Data & Analytics
- Amazon Athena: Querying Data Directly from S3
- Amazon Kinesis: Real-time Data Streaming
- AWS Glue & QuickSight: Data Integration & Visualization
15. Serverless Computing & AWS Lambda
- Introduction to Serverless Architecture
- AWS Lambda: Running Functions Without Servers
- Hands-on: Deploying AWS Lambda Functions
- API Gateway & AWS Fargate for Serverless Applications
16. Cost Management & Optimization Strategies
- AWS Cost Explorer: Understanding AWS Billing
- Cost Optimization Best Practices
- Managing AWS Budgets & Savings Plans
17. Cloud Migration & Enterprise Adoption
- AWS Well-Architected Framework for Best Practices
- AWS Cloud Adoption Framework (CAF)
- Cloud Migration Strategies
- AWS Database Migration Service (DMS)
18. AWS Certification Exam Preparation
- Introduction to AI & ML Services in AWS
- AWS CLF-C02 Certification Overview
- How to Prepare for AWS Exams
- Sample Questions & Practical Tips
💻 Self-paced 🔵 Instructor-led Live
Big Data Projects
Build Real-time Projects & Get Job-Ready!
-
Retalix – Retail Data Analysis
Analyze retail data to find customer preferences, shopping patterns, and seasonal changes to help improve store performance.Web Log Analytics
Study server log files to understand website visitors, fix errors, and make the website run better.Log Data Analysis
Examine log data to find issues, improve system performance, and keep the system secure. -
Sentiment Analysis with Real-Time Data
Use live social media data to identify whether people feel positive, negative, or neutral about a topic.Weather Data Analysis
Look at past weather records to see trends in temperature and rainfall over time.Building a Data Lake
Design and build a scalable data lake to store structured and unstructured data, enabling advanced analytics for business intelligence. -
Satellite Image Processing
Analyze satellite images to extract valuable insights for agriculture monitoring, urban planning, or disaster management.Twitter Data Analysis
Extract and analyze Twitter data to track trending topics, user behavior, and public sentiment in real-time.Telecom Customer Analysis
Process telecom customer data to identify churn risks, usage patterns, and opportunities for personalized services. -
IVR Data Analysis
Analyze IVR call data to enhance customer experience by identifying frequent issues and optimizing call flows.End-to-End Big Data Ecosystem
Design and build a complete data pipeline to ingest, process, store, and visualize industry-specific datasets for actionable insights.Enterprise Data Warehouse
Develop a robust data warehouse solution for retail or healthcare organizations to streamline data storage and analytics.
Tools and Technologies You’ll Learn in Data Engineering with Big Data Bootcamp
In our Data Engineering with Big Data Bootcamp, you will learn
This Bootcamp is designed to make you job-ready and confident in the exciting world of Data Engineering!
Master Data Engineering Fundamentals
Data Lake Architecture
Big Data Tools & Technologies
Real-Time Data Processing
Cloud Integration
End-to-End Big Data Projects
Database Expertise
Job Ready Skills
Join 37,950+ learners enrolled in TechVidvan’s Big Data and Data Engineering Course – Try before you Buy
From Learning to Placement – Enroll in our Live Data Engineering with Big Data Bootcamp
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- ✓ Live Classes with Expert Mentors
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Benefits:
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- ✓ IBM-aligned labs for job-ready output
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Job Guarantee
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Enroll in Self-paced Data Engineering Course
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Learn From Industry’s Best Instructors
Big Data Case Studies
-
Big Data at Google
Google processes billions of queries daily using Bigtable and MapReduce, ensuring lightning-fast search results and relevant ad placements to maintain its dominance in the search industry.Big Data at Spotify
Spotify employs Hadoop and Spark MLlib to analyze user preferences and listening habits, delivering personalized playlists that keep users engaged and reduce churn.Big Data at Tesla
Tesla uses Big Data and AI-powered frameworks to process vast amounts of sensor and video data from its fleet, improving the performance and safety of its self-driving algorithms. -
Big Data at NASA
NASA employs Big Data frameworks to analyze data from space missions, enabling real-time insights that improve mission success rates and resource allocation.Big Data at PayPal
PayPal employs Big Data analytics with Hadoop and Spark to monitor and detect fraudulent transactions in real time, ensuring safe and secure payment processing.Big Data at Twitter
Twitter leverages Hadoop and Storm to process massive volumes of tweets in real time, identifying trending topics and enabling timely content moderation and targeted advertising. -
Big Data at Alibaba
Alibaba uses Big Data technologies like Hadoop and machine learning to analyze transaction patterns and detect fraudulent activities, ensuring a secure online shopping experience for its customersBig Data at Cisco
Cisco processes terabytes of network data daily using Big Data technologies to predict and resolve network bottlenecks, improving connectivity and customer satisfaction.Big Data at Ford
Ford employs Big Data frameworks to analyze vehicle sensor data, enabling predictive maintenance that reduces breakdowns and enhances customer trust. -
Big Data at eBay
eBay uses Hadoop and machine learning to optimize search algorithms, ensuring customers find relevant products quickly, improving user satisfaction and sales.Big Data at Samsung
Samsung leverages Big Data tools to analyze customer feedback and device usage patterns, enabling targeted product development and marketing strategies.
Full Stack Big Data and Data Engineering Job Roles
Full Stack Data Engineering with Big Data Roadmap
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Full Stack 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.
