Thank Your Data Engineers With A Streaming Data Warehouse


I recently watched the movie Ford v Ferrari, based on the true story of automotive designer Caroll Shelby and race car driver Ken Miles, and their quest to build a revolutionary vehicle that can win the famed 24 Hours of Le Mans. Ford v Ferrari has the hallmarks of a racing movie — loud engines, daring turns, and the like. But what struck me about the film was the unusual level of focus on the long, iterative process of engineering the right vehicle for Le Mans’ grueling course. It’s easy to forget how much effort goes into a high-functioning finished product before it sees the light of day. 

In the data and analytics community, we too may be overlooking what’s going on behind the scenes. Much of the talk in our industry centers around new applications for data science and machine learning, like self-driving cars or language generators that type like humans (seriously though, GPT-3 is freaky). It’s natural human inclination to focus on the flashy and the exciting. Our attention goes to the winning driver of a race, not to the team behind the car, without which the driver would be hoofing it around the track. With fascinating new use cases popping up by the day, we can often lose track of how our innovations are made possible. 

Today, the data engineer is to cutting-edge applications what Carroll Shelby’s design was to Ken Miles’ driving. Data engineers make sure that whoever is working with data can get the data they need, by building pipelines for their applications. Data engineers get the data, transform it into a format useful for analysis, and provide it in real time, if needed, or in batch once a day or week or month, if not. Their work to ensure that an organization’s data architecture functions as a well-oiled machine is a critical prerequisite to building any products or services on top of it.  

When behind-the-scenes data engineering isn’t incorporated into our conversation, we can miss out on important trends, including what the profession’s biggest challenges are, and how leading data engineers are addressing them. One of today’s most pervasive data engineering challenges is the shift from working with data in periodic batches, to building real-time data pipelines and processing systems. Real-time analysis delivers far better results, but some organizations are put off by cost and complexity concerns, because batch systems are typically thought to be easier to set up. In response, data engineers are increasingly working with a streaming data warehouse, a platform combining historical, streaming, and graph analysis with location intelligence and AI. 

The streaming data warehouse simplifies the data engineering experience by fusing all the necessary capabilities for modern analytical applications into a single solution. With the traditional model, if a data engineer has historical data in a data warehouse, but needs real-time and location data, they might need to include Spark, and then send data to another system for location information, and so on. Instead of managing complex pipelines for a sprawling infrastructure of different components, with the streaming data warehouse, data engineers can perform all of their preparation and processing on a single copy of the data, decreasing their time-to-value for any pipeline they’re trying to build. 

Avoiding the hassle and time sink of setting up, diagnosing, and troubleshooting numerous components is of serious value to operational efficiency. The cherry on top is that by reducing an organization’s infrastructure footprint, streaming data warehouses like Kinetica can also be cost-effective to deploy. The platform flips the notion that real-time data pipelines are costly and complex completely on its head, equipping data engineers to keep building the engines behind our best innovations.


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