The total amount of data created, captured, copied, and consumed in the world is forecast to increase rapidly in coming years and as the technology is growing. The rapid development of digitalization contributes to the ever-growing global data sphere. It is even increasing and leads to a separate science and engineering domain.

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.

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.

To understand data engineering in simple terms, let’s turn to databases – collections of consistent and accessible information. Within a large organization, there are usually many different types of operations management software: ERP, CRM, production systems, and more. And so there are many different databases as well. As the number of data sources multiplies, having data scattered all over in various formats prevents the organization from seeing the full and clear picture of their business state. It’s necessary to figure out how to get sales data from its dedicated database talk with inventory records kept in a SQL Server, for instance. This creates the necessity for integrating data in a unified storage system where data is collected, reformatted, and ready for use – a data warehouse. Now, data scientists and business intelligence (BI) engineers can connect to the warehouse, access the needed data in the needed format, and start yielding valuable insights from it.

The process of moving data from one system to another, be it a SaaS application, a data warehouse (DW), or just another database, is maintained by data engineers. A data architect, however, is responsible for building a DW – designing its structure, defining data sources, and choosing a unified data format.

Speaking about data engineering, we can’t ignore the big data concept. Grounded in the three Vs – volume, velocity, and variety – big data usually floods large technology companies like YouTube, Amazon, or Instagram. Big data engineering is about building massive reservoirs and highly scalable and fault-tolerant distributed systems able to inherently store and process data.

Big data architecture differs from the conventional data handling, as here we’re talking about such massive volumes of rapidly changing information streams that a data warehouse isn’t able to accommodate. The architecture that can handle such an amount of data is a data lake.