Big Data Analytics: Distributed Storage & NoSQL Databases

Big Data Analytics: Distributed Storage & NoSQL Databases

Course Overview
This study material explores NoSQL databases and their role in Big Data Analytics. It compares relational databases with NoSQL technologies, covering different types of NoSQL databases, including key-value stores, document databases, graph databases, and column databases. Learn about the fundamental differences between BASE and ACID properties, the architecture of sharded document databases, and the practical applications of these databases in handling large-scale data.

Key Topics Covered:

• Introduction:

  • Relational vs Big-Data Technologies: Understand the key differences between traditional relational databases and modern Big Data technologies.
  • NoSQL Databases: Overview of NoSQL databases, their evolution, and why they are suited for large-scale and distributed data environments.
  • Types and Implementations: Explore various NoSQL database types and their implementations, including key-value stores, document databases, graph databases, and column stores.
  • BASE vs ACID: Learn about BASE (Basically Available, Soft state, Eventually consistent) and ACID (Atomicity, Consistency, Isolation, Durability) properties, and how they influence database design and performance.

• Key-Value Stores:

  • Overview of Key-Value Stores: Introduction to key-value stores, their use cases, and how they provide a simple yet efficient way to handle large volumes of data.

• Document Databases:

  • Schema-less Examples: Learn how schema-less document databases handle data without predefined schemas.
  • Foreign Keys: Explore how document databases manage relationships between documents using foreign keys.
  • Embedded Documents: Understand how embedded documents can be used to store nested data within a single document.
  • Organization and Terminology: Compare RDBMS terminology with document database terminology and understand how documents are organized and managed.
  • Inserting and Querying Documents: Practical examples of inserting and querying documents in a document database.
  • Advantages and Disadvantages: Discuss the benefits and limitations of document databases, including their scalability and flexibility.
  • Big Data Document Databases: Explore the use of document databases in big data environments and their role in managing large-scale, unstructured data.

• Document Databases: Partitioning:

  • Sharded Document Database Architecture: Learn about sharding as a technique for distributing data across multiple servers.
  • System Setup with Multiple Hosts: Step-by-step guide on setting up a sharded document database across multiple hosts.
  • System Setup for Testing: Instructions for setting up a document database on a single host for testing purposes.
  • Database and Collection Setup: Practical advice on configuring databases and collections within a document database.

• Other NoSQL Databases:

  • Graph Databases: Introduction to graph databases, their structure, and graph queries for managing and querying graph data.
  • Column Databases: Explore columnar databases and how they store data in columns rather than rows, making them ideal for analytical queries.
  • Object Databases: Learn about object databases that store data as objects, similar to object-oriented programming.

Why Choose This Material?

• Comprehensive coverage of NoSQL databases and their applications in Big Data Analytics.
• Practical examples and setup instructions for various NoSQL database types, including key-value stores, document databases, and graph databases.
• Ideal for students and professionals looking to understand and implement distributed storage solutions in modern data environments.

This material is perfect for students, data engineers, and Big Data professionals who want to learn about NoSQL databases and their role in handling large-scale data.