There are many challenges in the context of data-driven projects. The processes for data provision and data loading are indispensable/essential for the implementation of data-driven projects. In pilot projects or proof-of-concept projects, this can usually still be done through manual uploads and one-off transformations. Data pipelines therefore come into play when an Data Science Project evolving from the exploratory phase to a finished data product. Because with the increasing demands on Speed, Regularity and Reliability of the data provision are Data pipelines necessary.
The requirements for data provision therefore become increasingly important in the course of the productive implementation of projects. But even during the exploration of new issues, it can make sense to deal with the data requirements at an early stage. technical Aspects of data projects in relation to data pipelines. This prevents problems and delays later on in the projects.
Definition of a data pipeline
A data pipeline is one of the five dimensions of data-driven projects. In other words, a data pipeline is a Integral part of a data-driven project. The data pipeline loads data from one or more sources (for example, the cloud) and makes it available in the required form at the required location. Many different data formats and technologies can be used.
Link tip: If you want to know everything about the five dimensions of data-driven projects, also read our article on the Data Maturity Assessment.
A data pipeline comprises several steps:
- The extraction of data from different source systems
- The data cleansing and quality check
- The data transformation
- The storage or saving of the data at the destination or in the target system
The added value of data pipelines
- Reliable, uniform and comprehensible database
- Reduction of the time required for new use cases
- Highly consistent quality of results for existing use cases
The most important added value that data pipelines deliver is a reliable, uniform and comprehensible database. This means that subsequent use cases, for example, can be developed much more quickly and efficiently. The high amount of time required for the exploration, preparation and summarisation of data can thus be significantly reduced.
Link tip: Read here about the 5 measures that can be taken to achieve an optimal Data quality can be achieved.
For existing use cases, data pipelines can enable the quality of the results to remain high and the models to be continuously adapted to new developments. In addition, the traceability of the data and the processes can ensure compliance with Data governance and data security guidelines are guaranteed.
Our expertise from 500 projects
Due to the more than 500 data projects that we have carried out for our customers in recent years, we can draw on many years of experience in the field of Data Science and data engineering. In particular, we are distinguished by the combination of experience in the field of Software Engineering, Data Science and Data Engineering out. In the course of our many data science projects, we have already built numerous data pipelines. We therefore know the requirements that use cases place on the data, but also the stumbling blocks in the implementation of data pipelines.
Our services cover all aspects of data pipelines:
- Technological concept
- Architecture concept
- Advice on technology decisions
- Definition of interfaces
- Integration into existing infrastructure