Re-examining the ETL vs. ELT Conversation in the Age of Cloud Analytics
AIAnalyticsBases de donnéesBig DataCloudData LakesData integrationData intelligenceData scienceGestion des donnéesGreen ITIALarge Language Models (LLM)Machine Learning (ML)Maintenance prédictivePlateforme dataSaasThis eBook with RTInsights explores how many organizations now employ advanced techniques such as predictive analytics, machine learning, and artificial intelligence to delve deeper into their data and gain more precise insights. These workloads not only demand the use of a columnar store database, which reorganizes the data in preparation, but they also demand reorganizing the data in multiple ways into “data marts” that address different types of analysis. The demands of varying data sources and varying workloads require organizations to reassess their approach to ETL (Extract, Transform, Load) versus ELT (Extract, Load, Transform).
