According to IDC, the world had 33 zettabytes of data in 2018. By 2025, we’ll have 175 zettabytes, a 27% annual growth rate. Between now and 2025, leading organizations will use data engineering and data engineering services to replace old data warehouse architectures with big data architectures that embed analytics in everything. Simply put: data engineering turns traditional data and analytics from something that you stop and do to learn about your business to something that runs your business.

What’s a zettabyte, and how are zettabytes connected to innovation?

What’s a zettabyte (ZB)? Consider the following comparison: you can buy a terabyte hard drive at a store today. If each terabyte were a millimeter, then a zettabyte skyscraper would be 12 times higher than the Burj Khalifa, the world’s tallest building, which is 828 meters high.

Of course, a zettabyte is just a number. But this number represents an inevitable trend: we are collecting more data, and this data is being put to use, which creates even more data, thereby accelerating data collection and innovation. If your data is not growing, you are probably missing out on the opportunity to innovate.

Enter the data engineer.

The data engineer role grew out of the need to deal with this data growth. Data engineering emerged as a critical skill set as it became clear that data warehouses and data marts could not handle the requirements – in volume, velocity, and variety – of big data and it’s analytical cousin, AI.

Data warehousing organizes data, upfront, for anticipated use. Warehoused data is in hierarchical tables of rows and columns. Data is shaped based on business dimensions that don’t change much: for example, products SKUs and reporting periods. By contrast, with a big data store – a data lake – your data is flat and organized as you need it.

Data engineer or superhero?

Data engineering skills span disciplines and eras. A big data data engineer is a software engineer who specializes in data. They need to manipulate and move data with code and containerts, while still understanding ETL.

A data engineer also needs to be an expert in infrastructure and architectures. They operate in both the cloud and on-premise worlds. They master new technologies like no-SQL graph databases, near-real time streaming data sources, and machine learning models, while maintaining the old data warehouse.

If you don’t have these sort of superheros with all of these special powers, your may want to consider data engineering services from a consulting partner. Syntelli Solutions can help. Our data engineering services will help your company modernize your data strategy so you keep innovating well into the Yottabyte Age.