Post by ummefatihaayat12 on Feb 28, 2024 10:31:16 GMT
ETL processes have always been an important, but unfortunately under-recognized, part of the data warehousing world. It seems impossible to conceive a data warehouse without thinking about ETL processes. But there are several reasons why its role is more relevant and more difficult these days, reasons that exceed the challenges that data migrations have posed until now. Heterogeneity of data sources. 2. Multiplicity of data types. 3. Integration requirements more demanding than ever. Very complex and new forms of data transformation are required to achieve the intended utility, although the change at the volume level is also notable.
The transformation is such that traditional ETL processes may not be useful since, in many cases, in-depth processing work is required beforehand; For example, text-oriented sources require language processing upfront in order to create structured meaning that can be used later in analysis. ETL processes: the future is here Older forms of ETL processes still work with the structured India Part Time Job Seekers Phone Number List data they were designed to work with and still power data warehouses that are used by hundreds, if not thousands, of users every day. They also continue to add value to the organization's data so why do without it despite the difficulties? After all, ETL processes allow, among other things: Have historical data where it is needed and when it is needed. Ensure data consistency across all sources, supporting complex data quality operations.
It may be that departments such as marketing or sales prioritize agility, however, in other areas of the company, such as finance or accounting, it is necessary to be absolutely sure of the veracity of data before using it for reporting and data collection. of decisions. That's why, instead of getting rid of ETL processes, organizations are choosing to enhance their capabilities with other technologies, such as data virtualization, which help them overcome their limitations related to new data formats, types and variety. from their sources of origin. Data virtualization is the ideal complement to ETL processes by allowing you to connect to any data source, internal or external, structured or unstructured; integrating your data to provide different perspectives of your variables, and exposing the different points of view as data services.