![]() This can be challenging, particularly when dealing with complex data structures or integration requirements. Integration with other systems: ETL processes often involve integrating data with other systems, such as data warehouses, analytics platforms, or business intelligence tools. ![]() ![]() Organizations want to ensure that data is protected and accessed only by authorized users, with increased complexity when dealing with large volumes of data or data from multiple sources.Äata governance and compliance: data governance policies and regulations stipulate how data provenance, privacy, and security is to be maintained, with additional complexity arising from integration of complex data sets or data subject to multiple disparate regulations.Äata transformation and cleansing: ETL processes often require significant data transformation and cleansing in order to prepare data for analysis or integration with other systems. Security and privacy: ETL processes often involve sensitive or confidential data. what the original source was) can be difficult once data sources are integrated.Īvailability and scale: Is there enough storage and compute in your staging area to keep up with the data? (The more data that needs to be transformed, the more computationally and storage intensive it can become.)įiltering: Which data is important data and which can be ignored or discarded? Reasoning about the lineage of data (i.e. To run the initial ETL, you need to create a change. Make sure you include the applicable product name in your change request, instead of the placeholder.It can be difficult to identify and correct errors or inconsistencies in the data. The change request examples included below for Extract, Transform and Load (ETL) tasks can be used for Oracle Argus Mart, Oracle Argus Analytics, and Oracle Argus Insight. Permissions: Do your networks and systems have access and rights to the data?Äata freshness: Are you capturing real-time data, or stale data that's no longer of value? What is the ephemeral nature of the data? Are you able to capture it before the data passes its lifetime?Äata quality and integrity: Do you have validation in place to notice if the data that is extracted is in an expected form? Combining data from multiple sources can be challenging due to differences in data formats, structures, and definitions.
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