Discover job guarantee programs at Atharv Upgrade, ensuring career success with hands-on training and placement support in various industries.
Big Data Fundamentals: Master the core concepts of big data, including data storage, processing, and analysis.
Hadoop Ecosystem: Dive into the Hadoop framework, learning tools like HDFS, MapReduce, and YARN for efficient data management.
Apache Spark: Explore in-memory data processing with Apache Spark, a powerful framework for real-time analytics and large-scale data processing.
Distributed Databases: Gain expertise in NoSQL databases like MongoDB and Cassandra, essential for managing unstructured and semi-structured data.
Data Streaming: Learn about real-time data streaming with technologies such as Apache Kafka, enabling you to work with live data feeds.
Data Warehousing: Understand the principles of data warehousing and data lakes, and use technologies like Amazon Redshift and Google BigQuery.
Machine Learning Integration: Discover how to integrate machine learning models into big data platforms for predictive analytics.
Cloud-Based Big Data: Explore big data solutions on cloud platforms like AWS, Azure, and Google Cloud, gaining scalability and flexibility.
Real-World Projects: Apply your skills to real-world big data projects, showcasing your abilities to prospective employers.
Job Guarantee Assurance: Upon program completion, access interviews with our network of partner companies actively seeking big data professionals.
Module 1: Introduction to Big Data
- Understanding Big Data concepts
- Characteristics of Big Data
- Challenges and opportunities in Big Data
- History and evolution of Big Data
Module 2: Big Data Technologies and Ecosystem
- Overview of Hadoop, Spark, and other Big Data frameworks
- Components of the Hadoop ecosystem (HDFS, MapReduce, YARN)
- Introduction to Apache Spark
- Comparing Big Data storage and processing technologies
Module 3: Hadoop Distributed File System (HDFS)
- HDFS architecture and data storage
- Data replication and fault tolerance
- File operations in HDFS
- HDFS commands and administration
Module 4: MapReduce and Hadoop Programming
- MapReduce concepts and programming model
- Writing MapReduce jobs in Java
- Developing and debugging MapReduce applications
- MapReduce optimization techniques
Module 5: Data Ingestion and Extraction
- Data ingestion methods (batch and real-time)
- Working with structured and unstructured data
- Extracting data from various sources (databases, logs, APIs)
- Data cleansing and transformation
Module 6: Apache Spark Fundamentals
- Introduction to Apache Spark
- Spark architecture and components
- Resilient Distributed Datasets (RDDs)
- Spark transformations and actions
Module 7: Spark DataFrames and Structured Streaming
- Working with Spark DataFrames and Datasets
- Structured streaming for real-time data processing
- Spark SQL and data manipulation
- Writing Spark applications in Scala or Python
Module 8: Data Storage and NoSQL Databases
- NoSQL databases (e.g., MongoDB, Cassandra)
- Data modeling in NoSQL databases
- Storing and retrieving data in NoSQL databases
- Choosing the right NoSQL database for your use case
Module 9: Big Data Batch Processing
- Designing batch processing workflows
- Building ETL pipelines with Big Data tools
- Data warehousing and data lakes
- Batch processing optimization and performance tuning
Module 10: Real-time Data Processing with Kafka
- Introduction to Apache Kafka
- Publish-subscribe messaging model
- Kafka producers and consumers
- Building real-time data pipelines with Kafka
Module 11: Big Data Streaming and Processing
- Streaming data sources and processing
- Stream processing frameworks (e.g., Apache Flink)
- Event time vs. processing time
- Windowing and stateful processing
Module 12: Big Data Analytics and Machine Learning
- Leveraging Big Data for analytics
- Machine learning with Big Data (MLlib)
- Building recommendation systems
- Anomaly detection and predictive modeling
Module 13: Big Data Security and Privacy
- Security challenges in Big Data
- Authentication and authorization
- Data encryption and privacy regulations
- Implementing security measures in Big Data systems
Module 14: Big Data Governance and Compliance
- Data governance principles
- Compliance frameworks (e.g., GDPR, HIPAA)
- Metadata management and cataloging
- Data lineage and audit trails
Module 15: Cloud-Based Big Data Solutions
- Big Data on the cloud (e.g., AWS, Azure, GCP)
- Cloud-based Big Data storage and processing services
- Scalability and cost optimization in the cloud
- Hybrid and multi-cloud strategies
Module 16: Job Readiness and Interview Preparation
- Resume building and job application strategies
- Technical interview preparation
- Behavioral interview coaching
- Mock interviews and feedback
Module 17: Capstone Project
- Real-world Big Data project development
- Solving complex Big Data challenges
- Project documentation and presentation
Module 18: Job Placement Assistance and Networking
- Job search support and guidance
- Connecting with potential employers
- Job offer negotiation strategies
- Building a professional network in Big Data
Module 19: Career Development and Advancement
- Continuing education and certifications in Big Data
- Staying updated with industry trends
- Mentorship and professional growth opportunities
- Advancing your career in Big Data
Module 20: Job Guarantee and Post-Placement Support
- Job guarantee and placement support
- Post-placement mentorship and guidance
- Alumni network and ongoing support
- Continuous career development resources
Conclusion
In conclusion, our Big Data job guarantee program offers a deep dive into the world of data at scale, from foundational concepts to advanced analytics. With a strong emphasis on practical skills, job placement support, and continuous career development, graduates are well-prepared to tackle the complexities of Big Data and excel in data-driven roles.