ELT
There’s a way to deal with the challenges that come with ETL. By implementing ELT, an organisation is able to extract and load its raw data into a warehouse and then transform that data into the required outcome or result. This gives the organisation visibility on its raw data, thus making it easier to change or create new transformations. While this approach is an improvement on ELT, it too lacks flexibility when it comes to transformations that need to be altered. Additionally, there is no real-time visibility of those transformations when dealing with the question of ‘How was this calculated?’, and no true lineage to the source systems.
Furthermore, implementing this method calls for the expertise and understanding of how to connect to a database, data warehouse or data lake. It requires the skill of analysts who can write SQL, Python, Scala etc.
ET
A new way of integrating data is to ‘bring it home’ by simply extracting and transforming it. The removal of the requirement to load data before it can be consumed makes this method ideal. It reduces complexity and places analysts and consumers at the forefront of their data warehouse. This allows them to connect to live data and configure transformations that can then be saved as metadata. The ET approach makes data considerably easier to manipulate and assures real-time visibility.
fraXses empowers organisations to move away from traditional approaches, and systematically integrate their data with real-time visibility and minimal effort. The platform democratises data, allowing for anyone in an organisation to engage with it and deliver new transformations.
