Big Data Analytics: Distributed Storage & Partioning of Relational Databases

Big Data Analytics: Distributed Storage & Partitioning of Relational Databases

Course Overview
This study material covers the key concepts of distributed storage and the partitioning strategies used in relational databases to manage and optimize large datasets. It explains how horizontal and vertical partitioning can improve query performance and scalability, along with real-world examples of query processing, JSON data handling, and parallel computations.

Key Topics Covered:

• Replication and Partitioning:

  • Understand the role of data replication and partitioning in distributed relational databases, which are essential for fault tolerance, scalability, and efficient data retrieval.
  • Learn about the different partitioning techniques and how they impact query performance in large-scale data environments.

• Horizontal Partitioning:

  • Ranges and Lists: Explore how datasets can be partitioned horizontally by ranges, lists, or column ranges, enabling data to be distributed across multiple database nodes.
  • Hash Values: Learn how hash-based partitioning distributes data based on hash values, ensuring even distribution across the system.
  • Queries and Limitations: Understand how partitioning affects the performance of SELECT, JOIN, and other query operations, as well as the limitations of each partitioning method in certain use cases.

• Parallel Query Processing:

  • SELECT Operations: Discover how parallel query processing improves the efficiency of SELECT queries in distributed databases.
  • Joins and Cartesian Products: Learn how parallel processing handles joins, multi-way joins, and Cartesian products in large datasets.
  • Examples: Real-world examples demonstrating the speedup achieved through parallel query processing.

• Vertical Partitioning:

  • Understand vertical partitioning, where table columns are divided across nodes to reduce query processing time and optimize the handling of large datasets.
  • Learn when and why vertical partitioning is beneficial, especially for OLAP (Online Analytical Processing) and data warehouse environments.

• Sparse Data in Relational Databases:

  • Sparse Data and Key-Value Tables: Introduction to sparse data, where certain data points are missing or irregular. Learn how to manage sparse data using key-value pairs and how relational databases handle such datasets.
  • JSON Format: Understand how relational databases manage JSON data—a common format for storing sparse and semi-structured data.
  • Example: Todo List in JSON Format: Practical example of using JSON datatypes in relational databases to store and manage sparse datasets efficiently.
  • JSON Operators: Learn about the operators available in modern relational databases for querying JSON data effectively.

Why Choose This Material?

• Comprehensive coverage of partitioning techniques (horizontal and vertical) and how they enhance performance in distributed relational databases.
• Practical examples of parallel query processing, joins, and handling sparse data with JSON in relational databases.
• Ideal for students and professionals looking to understand distributed database systems in Big Data Analytics environments.

This material is highly suitable for students, database administrators (DBAs), and data engineers working with large-scale relational databases in distributed environments.