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ETL (Extract, Transform and Load) is a data preparation technique for moving data from standard applications into a data warehouse or analytics system that specializes in translating and interpreting trends and meanings from the data. The E-T-L of the name refer to each of the three steps of the process: first the data is Extracted from the application that is the data source, next the data is transformed into a format that is usable by the data warehouse/analytics system, and finally the data is loaded into the target system.
The ETL process has been the bread and butter of data warehousing and analytics for decades. But a study by IDC found that ETL is actually now becoming a bottleneck for modern analytics. Two-thirds of organizations now need to wait at least five days from the time data was captured before it can be transferred via ETL into the analytics system. But ETL is failing to keep pace with the demand for real-time analytics. Three-quarters of executives say that the lag in getting access to data quickly is hurting their business.
A newer technique called CDC, Changed Data Capture, uses new software design patterns for capturing the characteristics of the data. IDC found that, on average, within ten minutes, 65 percent of incoming CDC data can be moved into an analytics database.
Paul Grabscheid, vice president, of InterSystems, said that “as organizations look to compete and accelerate innovation, this study highlights the importance of concurrent transaction processing and real-time data analytics for improving customer experience, business productivity, operations and more.”