What are Data Pipelines?
Data pipelines are at the beating heart of your data strategy and are often used by the data engineers to transfer your data from source to target in a uniform, reproducible, resilient, scalable and qualitative way.
Data comes in many forms (structured, unstructured, …) and modes (streaming, batch). We make sure that we understand your data sources both functional and non-functional to set up a uniform way of working to ingest your data in the best suitable way.
Reproducibility is one of our core principles. For every code we write, we make sure it is clearly documented and runs in an environment that can be industrialised. Taking reproducibility into account from the very beginning enables us to smoothly go from development mode to day-to-day operations mode.
On one hand, resiliency for us means that we write data pipelines that can handle anomalies in data, detect them but at the same time keep the pipeline up. On the other hand, we handle a resilient release strategy that easily allows us to deploy new versions of pipelines.
From the very beginning, we do not only ask ourselves the question whether the data pipeline will run with today’s data, but we also foresee the data growing in the future and take this into account in the design, development and deployment.
We make sure that the data quality is handled in our pipelines by understanding, cleaning, augmenting and transforming the data. To make sure the quality of the data stays clean, we perform continuous monitoring to create alerts if necessary.