Big Data Masters Program

The Big Data Masters Program is tailored for professionals aiming to enhance their expertise in the rapidly evolving Big Data field. This program provides in-depth training on essential tools and systems used by Big Data experts, including Hadoop, Spark, Java essentials, and advanced SQL techniques.

Designed by industry leaders, the curriculum offers a blend of theoretical knowledge and practical skills, aligning with current industry standards to ensure that participants are equipped with the latest tools and methodologies used in the field.

Participants will engage in hands-on training that emphasizes real-world applications, allowing them to gain practical experience with key technologies and frameworks.

The program is structured to provide a comprehensive understanding of Big Data concepts while focusing on practical skills that accelerate the learning process.

With a focus on industry-relevant content and practical exercises, this program prepares professionals to tackle the complexities of Big Data projects and advance their careers in this dynamic field.

The Big Data Master Program offered by Yuva Sakthi Academy is a meticulously crafted course designed by experienced professionals in the field of Big Data.

This comprehensive curriculum encompasses all the essential aspects required to become a proficient Big Data specialist. Through a combination of theoretical instruction and hands-on practical sessions, participants dive into both fundamental and advanced concepts of Big Data technologies.

This includes an in-depth exploration of Hadoop, Spark, and NoSQL databases, as well as data processing, storage, and analysis. The course is structured to provide practical insights into real-world scenarios, ensuring that participants are well-prepared to tackle challenges in their future careers.

Industry experts guide participants throughout the Big Data Master Program, sharing invaluable insights and expertise from their own experiences. This mentorship ensures that learners not only understand theoretical concepts but also acquire practical skills essential for success in the field.

Additionally, the program extends beyond technical knowledge, offering career mentoring, resume assistance, and interview preparation support to help participants secure promising job opportunities upon completion.

This comprehensive approach aims to equip participants with the knowledge, skills, and confidence needed to excel as Big Data professionals in today's competitive job market.

Upon successfully completing the Big Data Master Program, participants receive an industry-recognized certification from Yuva Sakthi Academy, validating their proficiency in Big Data technologies. This certification serves as a testament to their expertise and enhances their credibility as they pursue opportunities in the data industry.

The curriculum is regularly updated to incorporate the latest industry trends and technologies, ensuring that participants stay ahead of the curve and are well-equipped to handle future challenges.

Whether you're an aspiring data analyst, a seasoned professional looking to upskill, or a student eager to kickstart your career, the Big Data Master Program provides a comprehensive learning journey tailored to meet the demands of the ever-evolving tech landscape.

Syllabus:

• Module 1: Getting Familiar with Spark
• Introduction to Apache Spark and its significance in the Big Data ecosystem
• Comparative analysis of Apache Spark vs. Apache MapReduce
• Overview of Spark components and architecture
• Installation and configuration of Spark
• Exploring Spark features and capabilities
• Module 2: Working with Resilient Distributed Dataset (RDD)
• Understanding transformations and actions in RDD
• Data loading and saving techniques with RDD
• Key-value pair RDDs and their operations
• Implementing MapReduce operations using RDD
• Optimizing performance with partitioners
• Module 3: Spark Application Programming
• Mastering SparkContext for application management
• Initializing Spark applications with Java
• Building and running real-time Spark applications
• Passing functions to Spark for advanced processing
• Submitting applications to Spark clusters
• Module 4: Spark Libraries
• Comprehensive overview of Spark libraries and their applications
• Module 5: Spark Configuration, Monitoring, and Tuning
• Understanding Spark cluster architecture and components
• Configuring Spark properties for optimal performance
• Logging and monitoring Spark applications
• Visualizing jobs using Directed Acyclic Graphs (DAGs)
• Performance tuning and optimization techniques
• Module 6: Spark Streaming
• Architecture of Spark Streaming: DStreams and RDD batches
• Utilizing receivers for data ingestion
• Common transformations and actions for DStreams
• Module 7: MLlib and GraphX
• Introduction to MLlib for machine learning applications
• Exploring GraphX for advanced graph processing tasks
• SQL for Big Data
• Comprehensive SQL training covering foundational knowledge in RDBMS, taught by industry experts.
• Oracle SQL Training Syllabus
• Introduction to Oracle Database and its components
• Using SQL SELECT statement to retrieve data
• Data filtering and sorting techniques
• Utilizing single-row functions for data customization
• Implementing conditional expressions and conversion functions
• Aggregating data using group functions
• Joining multiple tables for complex queries
• Implementing sub-queries for data retrieval
• Understanding and using SET operators
• Data manipulation techniques and DDL statements
• Managing schema objects and user access control
• Leveraging data dictionary views for object management
• Handling large datasets efficiently
• Data management across time zones
• Utilizing regular expressions for data manipulation
• Linux Fundamentals
• Essential Linux Skills
• Overview of fundamental Linux commands
• Working with the Vim text editor: modes and commands
• Understanding the Linux filesystem hierarchy
• Creating and managing files and directories
• Using grep and filter commands for data management
• Creating and managing users and groups effectively
• Understanding permissions and file management in Linux
• Software installation and management using yum
• Exploring runlevels, services, and daemons
• Big Data with Hadoop
• Comprehensive training on Hadoop, covering essential concepts and hands-on experience with industry-standard tools.
• Challenges and opportunities in Big Data analytics
• Setting up and installing a Hadoop cluster
• Mastering HDFS (Hadoop Distributed File System) for data storage
• Hands-on experience with MapReduce programming in Java
• Implementing Big Data analytics using Pig and Hive
• Integrating HBase with Hive for enhanced data processing
• Understanding ZooKeeper for distributed system management
• Exploring YARN architecture for resource management
• Overview of the Hadoop framework and ecosystem
• Essential Linux skills for Hadoop operations
• Deep dive into MapReduce, Java, Pig, and Hive
• Mastering HBase for NoSQL database management
• Data loading techniques using Sqoop and Flume
• Workflow scheduling with Oozie for automation
• Engaging in a hands-on real-time project
• Apache Spark & Scala
• This module focuses on mastering Spark with Scala for high-performance data processing and analytics.
• Module 1: Getting Acquainted with Spark
• Introduction to Apache Spark and its applications in Big Data
• Comparison of Spark with MapReduce: Pros and Cons
• Components of the Spark stack and their roles
• Downloading, installing, and launching Spark
• Module 2: Resilient Distributed Datasets (RDD)
• Transformations and actions in RDD for data processing
• Loading and saving data in RDDs
• Understanding key-value pair RDDs and their operations
• MapReduce techniques with RDD
• Improving performance with partitioning strategies
• Module 3: Programming with Spark
• Understanding SparkContext and its usage in applications
• Writing and executing Spark applications using Scala
• Creating data pipelines with Spark components
• Building and deploying Spark applications
• Module 4: Spark Streaming
• Architecture of Spark Streaming and processing streams
• Using DStreams for real-time data analysis
• Integration with data sources like Kafka
• Module 5: Machine Learning with MLlib
• Overview of MLlib and its machine learning algorithms
• Building predictive models using Spark MLlib
• Model evaluation techniques and metrics
• Module 6: Graph Processing with GraphX
• Introduction to graph processing with GraphX
• Building graph algorithms with Spark
• Practical applications of graph processing
• Capstone Project
• Final project to apply all learned concepts in a comprehensive data analytics project.
• Real-world case study and problem definition
• Data collection, cleaning, and preparation techniques
• Data analysis and visualization strategies
• Implementing machine learning models and algorithms
• Presenting findings and insights to stakeholders