Data warehouse automation involves automating the complete data warehousing cycle including planning, design, development, deployment, analysis, change management, and maintenance.
Mapping dataflows and creating design patterns are an integral part of the data warehousing process. This requires writing thousands of lines of code, which could essentially take months. The ability to quickly create, save, and reuse saved patterns can help reduce design time and costs significantly.
Extraction of data from multiple sources can take up a lot of resources as data is dispersed into several internal and external systems. The incoming data often requires transformation into the required format, which is then loaded into the data warehouse. It then undergoes query and analyses for reporting and business intelligence.
An effective data warehousing automation tool offers an agile and flexible approach to building a data warehouse quickly, facilitating business intelligence tasks and data analysis.
It must be able to automate the key processes, from data extraction and transformation to loading it into the data warehouse, thereby reducing manual effort and accelerating time-to-value.