Scalability and Performance Optimization Techniques in Azure Data Lake Analytics for Researcher Recommendation Systems

EasyChair Preprint 14069

Abstract

Scalability and performance optimization are crucial aspects of building efficient researcher recommendation systems in Azure Data Lake Analytics. This paper explores various techniques and best practices to enhance scalability and optimize performance in Azure Data Lake Analytics for such systems.

The paper begins by providing an overview of Azure Data Lake Analytics and highlighting the significance of scalability and performance optimization in researcher recommendation systems. It then delves into scalability techniques, including partitioning data through horizontal and vertical partitioning, distributing data across multiple nodes, and scaling compute resources dynamically. The concept of parallel processing and optimizing query execution plans are also discussed.

Next, the paper explores performance optimization techniques in Azure Data Lake Analytics. It covers data format optimization by choosing efficient file formats and compressing data to reduce storage and I/O costs. Query optimization techniques such as indexing and query hints are explored, along with memory management strategies and monitoring/tuning approaches to identify and resolve performance bottlenecks.

Furthermore, the integration of Azure Data Lake Analytics with researcher recommendation systems is examined. This includes data ingestion and preprocessing, recommendation model training using distributed computing, and designing efficient serving infrastructure for real-time recommendation serving. Real-world case studies and best practices are presented to illustrate successful implementation strategies.

Keyphrases: Azure Data Lake Analytics, efficient deployment, efficient design, researcher recommendation systems

@booklet{EasyChair:14069,
  author    = {Kayode Sheriffdeen and Toheeb Olaoye},
  title     = {Scalability and Performance Optimization Techniques in Azure Data Lake Analytics for Researcher Recommendation Systems},
  howpublished = {EasyChair Preprint 14069},
  year      = {EasyChair, 2024}}