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Andrew Wooler

What Is Data Engineering and How Can It Be Streamlined?

Data engineering is the practice of getting data where it’s needed, when it’s needed, and how it’s needed. This is usually accomplished by taking data, transforming it into a format useful for analysis, and then building pipelines that provide it to an application either in real time or in periodic batches, such as once a day or once a week. 

You can imagine that most applications are not very useful if they don’t have the right information to process, so data engineering has long been important. Yet as applications become more sophisticated and organizations come up with new ways to utilize their data, the demand for data engineering is increasing fast. Google searches for the term have tripled since 2012, while data engineering has become one of the most rapidly growing occupations in tech. 

This is undoubtedly good for the job prospects of those in the field, but it doesn’t mean the role has gotten any easier — it’s the other way around. Data engineers are now asked to work with more components in a data architecture than ever, and to integrate diverse types of data with complex functions into new analytics domains, such as machine learning. All of this often adds up to manual processes and pipeline deployments that make it difficult to get data where it’s needed as quickly as possible. 

At Kinetica, we’ve found that by bringing together streaming and historical data with location, graph, and ML analytics in one platform, we’re able to decrease the time to value for data engineers by allowing their architecture to perform all those different types of analytics on a single copy of the data. They’re able to combine fast-moving datasets with static data stored on other sources, building pipelines without the manual time sink of integrating and troubleshooting many different components. By consolidating their analytics, data engineers are able to more easily meet their SLAs and gain greater visibility into the flow of their data. 

For more details on how Kinetica simplifies data engineering, see our Data Engineering Solutions Page.

Andrew Wooler is global marketing manager at Kinetica.

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