Once you’ve begun to use Hadoop for ETL offload, the next logical steps on your journey toward the data warehouse of the future are to create an active archive and a data repository.
Most organizations have structured data they rarely access, but do not want to delete. Moving this cold data into Hadoop for active archive keeps it available for analytics and search while lowering costs. In fact, according to one study, storing data in Hadoop instead of a legacy data warehouse saved organizations $15 million on hardware costs.
Moving siloed and unstructured data to Hadoop makes more data available for advanced analytics, which helps companies increase profits, reduce fraud, decrease reporting costs and boost productivity. It also sets them up for the final step in the journey: Lambda Architecture with In-Memory Technology.