Data Lake vs. Data Warehouse: Choosing the Right Architecture
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P107Keywords:
Data Lake, Data Warehouse, Big Data, ETL, ELT, Cloud Storage, Schema-on-read, Schema-on-write, Structured Data, Unstructured Data, Business Intelligence, Analytics, Data Architecture, Data Strategy, Hybrid Architecture, Data Governance, Scalability, Cost-efficiency, Real-time Analytics, Machine Learning, Data Integration, Data Modeling, Query Performance, Metadata Management, Data Quality, Data Processing, Storage Optimization, Data Ingestion, Data Transformation, Data Lakes vs. WarehousesAbstract
Driven by data, companies have several options for choosing the suitable data architecture to meet their evolving needs in the current world. Originally the standard for organized analytics, the data lake today challenges the conventional data warehouse since it offers flexibility for unstructured, raw data as data counts rise and sources multiply. Designed for effective querying and ordered reporting, data warehouses fit corporate intelligence tools, regulatory reporting, and financial analytics. Data lakes are more fit for machine learning, real-time analytics, and extensive data analysis since they span a wide spectrum of data typestext, images, logs, and video. Data lakes have changed large data ecosystems by bringing about a move from strict schemas to schema-on-read approaches. But this flexibility affects governance and query performance, so the choice between the two is one of concessions. Which architecture most meets the "3 Vs" of datavolume (the amount of data being handled), variance (the range of forms and sources), and speed (the pace at which data is generated and requires analysis)? While some companies may choose one strategy, many others are thinking about hybrid models combining the scalability and agility of lakes, sometimes known as a "data lakehouse," with the structured querying powers of warehouses. These hybrid solutions provide ideal benefits: controlled, effective analytics combined with many approaches of data collecting. Corporate goals, analytical complexity, present infrastructure, particular uses including compliance reporting, broad consumer insights, predictive modeling, or real-time customizing will all affect the suitable solution. Companies that link their data strategy with expected and real needs will be best equipped to get insightful analysis and stimulate creativity
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