In essence, by adopting automation, an organization is basically “future-proofing” its analytical architecture – no small accomplishment! Agile MethodologyĮTL automation supports the technical staff as they move to adopt a more iterative and agile methodology. Minimal additional recoding efforts will be needed. Much of the ETL code generated from an automation technology can be easily retrofitted to the new environment through simple pull-down menu options. Project lead time is greatly reduced with automation when adopting a new technological target (e.g., moving to Snowflake or Synapse) or migrating from an on-premises environment to a cloud-based one. Being able to understand how upstream ETL changes can affect downstream analytical assets eliminates so many problems for users and implementers alike. Think how useful that information becomes to business users, data scientists, others using and creating analytical assets. Data lineage consists of the metadata that shows all the manipulations occurring to data from its source(s) to its ultimate target database as well as the individual operations to produce analytical assets (algorithms, calculations, etc.). Data LineageĪ significant automation boon to any analytical environment is its automatic creation of the data’s lineage. No matter where the data resides (on-premises, in the cloud, in a relational database or not), these sets of data remain the same, making their utilization so much easier by all (business community or technical staff). The implementers can spin up new data and analytical assets or perform maintenance on existing assets without introducing “creative” (non-standard) data into these critical components. Document Process Automationīy setting up routine programs to handle common tasks like date and time processing, reference and look-up tables, and serial key creation, the analytical teams establish much-needed standards. In other words, they trust the data and asset. Business users increase their adoption of analytical assets if they can determine that the asset was created from the same data they would have used, that it was properly integrated with other sets of data, and that the ultimate analytical asset is exactly what they need. It is as useful to the business community as it is to the technical implementation staff. This metadata is not an after-thought it is integral to the automation software itself and is always current. Among its many benefits are the following: Automated DocumentationĪutomation ensures that the ETL processes are not just tracked but documented in terms of up-to-date metadata on every extraction, every transformation, and every movement of the data, and every manipulation performed on it as it makes its way to the ultimate analytical asset (a report, an analytic result, a visualization, a dashboard widget, and so on). We must improve and speed up all data integration by introducing automation into ETL processes.Īutomating is more than just relieving the implementers of creating over and over the many mundane and repetitive tasks. Just the length of time it takes to create a new report – a relatively simple process – demonstrates that just having ETL skills is not enough. The increasingly complex infrastructures of most analytical environments, the addition of massive amounts of data from unusual sources, and the complexity of the analytical workflows all contribute to the difficulties that implementation teams have in meeting the needs of the business community. Sadly though, ETL alone is not good enough to keep up with the speed at which modern analytical needs change and grow. It has been a programming skill set mandatory for those responsible for the creation of analytical environments and their maintenance. Extraction, transformation, and loading (ETL) processes have been in existence for almost 30 years.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |