Data engineering is all about building data pipelines for ingesting, processing, and governing all types of data (structured, unstructured, semi-structured) at all latencies (batch and streaming) in cloud or hybrid environments.
Data engineering is a set of operations aimed at creating interfaces and mechanisms for the flow and access of information. It takes dedicated specialists – data engineers – to maintain data so that it remains available and usable by others. In short, data engineers set up and operate the organization’s data infrastructure preparing it for further analysis by data analysts and scientists.
Sharing top billing on the list of data science capabilities, machine learning and artificial intelligence are not just buzzwords – many organizations are eager to adopt them. However, the often forgotten fundamental work necessary to make it happen – data literacy, collection, and infrastructure – must be accomplished prior to building intelligent data products.
If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.