Real-Time Data Engineering: Building High-Performance Systems for Actionable Insights

In today’s fast-paced digital world, businesses rely heavily on real-time data to make critical decisions that can make or break their success. Real-time data engineering — the process of collecting, processing, and analyzing data in real-time — is a critical component of this process. However, it is also a complex and challenging task that requires specialized skills, tools, and technologies that differ from those used in traditional data engineering practises.

Challenges

Real-time data engineering is difficult for several reasons. Firstly, the volume and velocity of data generated by modern organisations that operate at such a big scale like FanDuel, can be overwhelming. In such cases, data is generated at such volume and high rate that traditional data processing and storage techniques become insufficient. To handle this data more efficiently, specialized tools and technologies are required.

The above also has an impact on the skills and expertise the team members working on these usecases will need to have. Processing data every hour, versus as and when the event happens are completely different requirements, thus the technologies, programming languages and distributed compute required is also different. Furthermore, real-time data engineers need to have a deeper understanding of the business domain and the types of data that are critical to business operations, as in most cases, they are acting on behalf of the business and reduce the time human analytical actions are taken by analysts.

Finally, real-time data engineering requires a robust and highly-scalable infrastructure that can handle the demands of real-time data processing. The nature of real-time engineering requires more flexibility to scale up compute and processing resources in a quicker fashion, as the data might come in big spikes. At FanDuel, we see this very regularly during big events such as SuperBowl, where data spikes drastically at significant periods of the game. Building and maintaining such an infrastructure requires significant investment and expertise.

Rewards

Despite these challenges, real-time data engineering is crucial to the success of modern businesses. Firstly, real-time data enables businesses to make critical decisions in real-time, which is essential in today’s fast-paced business environment. For example, real-time data can be used to detect fraud in financial transactions, monitor the performance of critical systems, and track the behavior of customers in real-time.

Secondly, real-time data engineering can help businesses optimize their operations and improve their bottom line. By collecting and analyzing real-time data, businesses can identify inefficiencies and bottlenecks in their operations and take corrective action in real-time. This can help businesses reduce costs, increase efficiency, and improve customer satisfaction.

Thirdly, real-time data engineering can help businesses gain a competitive edge in the market. By analyzing real-time data, businesses can identify trends and patterns that their competitors may not be aware of. This can help businesses develop new products and services, improve existing ones, and make better-informed decisions about their business strategy.

In conclusion, real-time data engineering is a critical component of modern business operations. While it is a challenging and complex task, the benefits of real-time data processing are clear. As such, businesses that invest in real-time data engineering are likely to be more successful in the long term.

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