Managing data lakes in big data era: What's a data lake and why has it became popular in data management ecosystem

TL;DR: In this paper, a new data storage technology approach, and a new Artificial Intelligence architecture, with three layers to extract relevant information from data of both recruiters and job seekers by exploiting machine learning, in particular clustering algorithms to group data points, natural language processing to convert text to numerical representations, and recurrent neural networks to produce matching keywords, and equations to generate a similarity score.

TL;DR: A generic and scalable metadata management model for data lakes is presented to address the challenges associated with the "data swamp". The model promotes generality and scalability by dynamically provisioning and resizing resources based on demand.

Abstract: Abstract In the era of big data, organizations face critical decisions when selecting between data lakes and data warehouses to meet their analytics requirements. This article presents a comprehensive comparative analysis of these two predominant data management architectures, emphasizing their structural differences, functional capabilities, and suitability for diverse analytics workloads. Data lakes offer scalable, cost-effective storage for raw, unstructured, and semi-structured data, supporting advanced analytics and machine learning applications. In contrast, data warehouses provide optimized, schema-on-write frameworks for fast querying and reliable reporting on structured data. Through detailed examination of architectural designs, integration with big data tools including Hadoop, Spark, and Kafka, and evaluations based on performance, scalability, cost, and governance, this paper provides organizations with evidence-based guidance to align their data strategies with business objectives. Case studies from healthcare and retail sectors illustrate practical implications of each approach, while emerging trends such as lakehouse architectures, AI integration, blockchain security, edge computing, and quantum computing highlight future directions. The findings support for a hybrid data management solution that leverages the strengths of both data lakes and warehouses to enable robust, scalable, and innovative big data analytics.

TL;DR: One of the contentious ideas to emerge in the big data era, data lakes have the ability to alter the data environment and it is worthwhile to conduct research on them.