Bachelor Thesis: Patterns for Data Intensive Applications

With the ever-increasing volume, velocity, and variety of data in today’s digital landscape, understanding how to effectively structure and manage data-intensive applications is crucial. These applications often face unique challenges related to data storage, processing, and retrieval, necessitating robust design solutions. Similar to the ‘Gang of Four’ (GoF) book, which catalogues design patterns for object-oriented programming, this thesis aims to uncover and categorize patterns specific to data-intensive contexts.

The purpose of this thesis thus is to explore and analyze established patterns in the realm of data intensive applications (DiA). The student will conduct a comprehensive review of existing literature, including scholarly papers and insightful blog posts, to identify and extract common design patterns and best practices that can be applied to the development of scalable and efficient data-intensive systems.

Tasks

1. Literature Review:
   The student will begin by conducting a thorough literature review utilizing the provided starting literature, which may include academic papers, case studies, technological documentation, and relevant blog posts from industry experts.
2. Pattern Identification:
   The student will systematically analyze the reviewed materials to identify recurring themes and techniques employed in successful data intensive applications. This may include strategies related to data modeling, data processing frameworks, caching mechanisms, data integration techniques, and scalability considerations.
3. Pattern Extraction and Documentation:
   Each identified pattern will be documented in detail, including its purpose, context of use, advantages, and potential limitations. The student should aim to create a concise and accessible collection of patterns that can be utilized by practitioners and researchers alike.
4. Comparison and Classification:
   The identified patterns will be compared against each other and classified into categories similar to those found in the GoF design patterns, thereby creating a cohesive framework for understanding data intensive application design.

Expected Outcomes A well-documented thesis that outlines key patterns for data intensive applications, contributing to the field of software engineering. A pattern catalog that can serve as a practical reference for developers and architects working on data heavy projects. An enhanced understanding of the methodologies and strategies that enhance the performance and scalability of data intensive applications. Conclusion: This bachelor thesis presents an exciting opportunity to contribute to the growing body of knowledge surrounding data intensive applications. By synthesizing existing literature into practical design patterns, the student will not only deepen their own expertise but also provide valuable resources for others in the field.

Student: 1

Supervisor: Philipp.Zech@uibk.ac.at