The challenges of implementing BI ETL and data warehousing
With the advent of big data and the demand for timely, accurate data across globally distributed enterprises, businesses are finding batch-oriented BI ETL and an inflexible approach to data warehousing can result in processing bottlenecks, costly project delays, and an inability to react swiftly to unexpected changes in the marketplace. Business users want real-time insight while IT teams—overwhelmed by ballooning data stores, shrinking batch windows, and long-running BI ETL tasks—are struggling to process and deliver the freshest data possible.
One problem is that BI ETL tools simply can't process the streams of incoming data fast enough. The volume and velocity of data enterprises are handling today seem to necessitate a real-time ETL solution. Another problem is that setting up, making adjustments to, and updating data warehouses in accordance with changing business requirements is time-consuming and risky. While ETL tools can help teams design and maintain BI ETL processes, they cannot automate and expedite other complex data warehousing processes.