Description

This comprehensive training program offers a deep dive into the realm of Big Data engineering, focusing on data modeling and architecture to equip participants with the skills and knowledge needed to tackle complex data challenges at Locus Academy .

Through a blend of theoretical concepts and practical hands-on exercises, participants will learn how to design robust data models, architect scalable Big Data systems, and optimize data pipelines for efficient processing and analysis. The training covers key topics such as conceptual, logical, and physical data modeling, data warehouse design principles, distributed computing frameworks, and best practices for managing and governing Big Data environments.

Participants will also explore advanced techniques for data integration, data governance, and data quality assurance to ensure the reliability and integrity of large-scale data systems. By the end of the training, participants will be equipped with the expertise to design, implement, and manage data-centric solutions that meet the evolving demands of modern enterprises in the era of Big Data.

TABLE OF CONTENT

1. Introduction to Big Data

Definition of Big Data
Characteristics and challenges of Big Data
Importance and applications of Big Data in various industries

2. Basics of Data Management

Data types and structures
Relational databases
NoSQL databases
Data normalization and denormalization

3. Distributed Systems

Fundamentals of distributed computing
Distributed storage and processing
Cluster computing
Fault tolerance and scalability

4. Big Data Technologies and Tools

Hadoop ecosystem (HDFS, MapReduce, YARN)
Apache Spark
Apache Flink
Data warehousing solutions

5. Data Processing and Analysis

Batch processing vs. real-time processing
Data cleaning and preprocessing
Data transformation and enrichment
Stream processing

6. Data Modeling and Architecture

Big Data architecture
Data modeling for Big Data
Data integration and orchestration
Lambda architecture and Kappa architecture

7. Cloud Computing for Big Data

Cloud-based storage solutions
Cloud-based data processing platforms
Serverless computing
Case studies of using cloud platforms for Big Data

8. Data Security and Privacy

Challenges in securing Big Data
Encryption and access control
Compliance with data protection regulations
Best practices for ensuring data privacy

9. Real-world Big Data Applications

Case studies and use cases
Industry-specific applications (e.g., healthcare, finance, retail)
Success stories and lessons learned

10. Capstone Project

Hands-on project involving the design and implementation of a Big Data solution
Integration of various tools and technologies studied in the course
Presentation and documentation of the project

11. Emerging Trends in Big Data

Machine learning and AI in Big Data
Edge computing
Graph databases and analytics
Continuous evolution and future directions

Please Visit AWS Official Site: || Locus Academy ha s more than a decade experience in delivering the training/staffing on Big Data Engineering with Data Modeling and Architecture for corporates across the globe. The participants for the training/staffing on Big Data Engineering with Data Modeling and Architecture are extremely satisfied and are able to implement the learnings in their on going projects.

Other useful references

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