Frequently Asked Questions
How do data pipelines help the business?
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.
What types of data pipelines are there?
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 processes: ETL vs. ELT
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.
What role does data engineering play in data pipelines?
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