Understanding Data Virtualization
Data-driven decision-making stands as a key strategy for numerous companies globally. For decision-making to be effective, data must be provided to users promptly. Companies utilize ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) approaches to develop analytics layers from various data sources, aiding users in making informed decisions. However, both these paradigms face challenges in producing datasets on time for consumer use due to the involvement of multiple processes and tools.
Many companies find it challenging to establish a unified view from diverse data sources. With the daily increase in data sources and consumers, technology vendors focus on zero ETL as a solution. Data virtualization can be employed to avoid unnecessary ETL processes and data replication.