Companies usually accumulate large amounts of data. The larger the volume of data, the slower and more inefficient it is to process. Data pipelines ensure that data processing is clearly structured and implemented effectively. Acosom helps to better exploit the potential of data by using data pipelines.

With data pipelines, a distinction is made between ETL and ELT. With the classic ETL (Extract, Transform, Load) method, the data is extracted, transformed and then loaded or transferred. However, data is lost during the transformation. That’s why ELT first loads the data, saves it and only then transforms it.

Data pipelines differ in their process steps and processing types. Extract, Transform, Load (ETL) is the classic method: data is first extracted, then prepared and then loaded into another system. “Transform” involves consolidating data and cleaning the data from low-quality data. “Load” means the provision of the data via container or API. However, these intermediate steps can be built on top of one another in different ways. In the ELT process (Extract, Load, Transfer), the data is first loaded and only then processed - i.e. exactly the other way around than is the case with ETL. Due to the reverse order with ELT, no data is lost in this way. This is useful, for example, to train machine learning models as precisely as possible. The ELT approach is also suitable for big data and data lakes.

In addition to data warehouses and data engineers, data pipelines are the main components of data engineering. Data engineering summarizes a series of measures that create interfaces and mechanisms for a continuous and reliable flow of information and access. Data engineers are responsible for setting up and operating the data infrastructure in companies. In data warehouses, companies collect, store and format extracted data from certain systems. This data is moved – for example from applications to a data warehouse or database – via data pipelines.