2020/09/25 by Ruoran Liu, Liu, Ruoran, Haruna Isah +3 · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Cloud Computing and Resource Management #Data Quality and Management #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #and Cluster Computing (cs.DC) #cs.AI #cs.DB #cs.DC #cs.LG
paper · pdf · doi:10.48550/arxiv.2009.12415
6 pages
arxiv created 2020/09/25 · openalex publication_date 2020/09/25 · arxiv updated 2020/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Large organizations are seeking to create new architectures and scalable platforms to effectively handle data management challenges due to the explosive nature of data rarely seen in the past. These data management challenges are largely posed by the availability of streaming data at high velocity from various sources in multiple formats. The changes in data paradigm have led to the emergence of new data analytics and management architecture. This paper focuses on storing high volume, velocity and variety data in the raw formats in a data storage architecture called a data lake. First, we present our study on the limitations of traditional data warehouses in handling recent changes in data paradigms. We discuss and compare different open source and commercial platforms that can be used to develop a data lake. We then describe our end-to-end data lake design and implementation approach using the Hadoop Distributed File System (HDFS) on the Hadoop Data Platform (HDP). Finally, we present a real-world data lake development use case for data stream ingestion, staging, and multilevel streaming analytics which combines structured and unstructured data. This study can serve as a guide for individuals or organizations planning to implement a data lake solution for their use cases.