Module 1: Introduction to Big Data
- Definition and Characteristics of Big Data
- Challenges and Opportunities
- Evolution of Big Data Technologies
Module 2: Basics of Hadoop
- Introduction to Hadoop Ecosystem
- Hadoop Distributed File System (HDFS)
- MapReduce Programming Model
Module 3: Hadoop Ecosystem Components
- Apache Hive for Data Warehousing
- Apache Pig for Data Flow
- Apache HBase for NoSQL Databases
- Apache Spark for In-Memory Processing
Module 4: Data Ingestion and Processing
- Apache Flume for Log Collection
- Apache Sqoop for Data Import/Export
- Real-time Data Processing with Apache Kafka
Module 5: Hadoop Cluster Management
- Setting Up and Configuring a Hadoop Cluster
- Cluster Monitoring and Maintenance
- High Availability and Fault Tolerance
Module 6: Advanced Hadoop Concepts
- YARN (Yet Another Resource Negotiator)
- Hadoop Security and Authorization
- Hadoop Performance Tuning
Module 7: Introduction to Big Data Analytics
- Overview of Big Data Analytics
- Batch and Real-time Processing
- Use Cases and Applications
Module 8: Machine Learning with Big Data
- Introduction to MLlib (Machine Learning Library)
- Building and Evaluating Machine Learning Models
- Integrating Machine Learning with Hadoop
Module 9: Big Data in the Cloud
- Cloud Computing Basics
- Big Data Solutions on Cloud Platforms (AWS, Azure, GCP)
Module 10: NoSQL Databases
- Introduction to NoSQL Databases
- Types of NoSQL Databases (e.g., MongoDB, Cassandra)
- Integrating NoSQL with Hadoop
Module 11: Big Data Security and Governance
- Security Challenges in Big Data
- Implementing Security Measures
- Data Governance and Compliance
Module 12: Real-world Big Data Projects
- Case Studies and Project Examples
- Hands-on Project Work
Module 13: Future Trends in Big Data
- Emerging Technologies (e.g., Apache Flink, Apache Beam)
- Industry Trends and Innovations
Module 14: Tools and Development Environment
- Setting Up a Hadoop Development Environment
- Using Development Tools (e.g., Apache Zeppelin, Jupyter)
Module 15: Career Development in Big Data
- Job Roles in Big Data
- Building a Career Path
- Networking and Professional Development
Bigdata and Hadoop Training Certification
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Bigdata and Hadoop Course Fee and Duration in Jalandhar
| Track | Regular Track | Weekend Track | Fast Track |
|---|---|---|---|
| Course Duration | 45 – 60 Days | 8 Weekends | 5 Days |
| Hours | 2 hours a day | 3 hours a day | 6+ hours a day |
| Training Mode | Live Classroom | Live Classroom | Live Classroom |
- Big Data Engineer
- Hadoop Developer
- Data Engineer
- Big Data Architect
- Data Analyst (Big Data)
- Machine Learning Engineer (Big Data)
- Big Data Consultant
- Data Scientist (Big Data)
- Cloud Data Engineer
- Big Data Administrator
- Big Data Quality Analyst
- IoT Data Engineer
- Business Intelligence Analyst (Big Data)
- Hadoop Operations Analyst
- Cybersecurity Analyst (Big Data)
- Amazon (AWS)
- Microsoft
- IBM
- Cloudera
- Hortonworks (now part of Cloudera)
- LinkedIn (Microsoft)
- Uber
- eBay
- Netflix
- Yahoo (Verizon Media)
- Airbnb
- Cisco
A qualification for a Big Data and Hadoop course typically requires a background in computer science or related fields, along with knowledge of programming languages like Java or Python. Familiarity with data processing concepts is beneficial. Some courses cater to beginners, while others may target professionals seeking to enhance their Big Data skills.
