It seems that we’re reaching the point where the Internet of Things (IoT) is moving from the domain of enthusiastic early-adopters to the more challenging, more profitable territory of mainstream enterprise technology.
In particular, companies in industries that depend on the attention and loyalty of large numbers of consumers – retail and air travel, for example –have already woken up to the possibilities. If you can recognize an individual customer’s mobile device as they move through an airport or walk past your store, you can potentially take action to improve that customer’s experience – whether that means making a personalized offer to attract their attention, or simply offering a warmer welcome in the departure lounge when you know they’ve spent a long time waiting in line at security.
Event-driven architectures are playing a key role in these types of applications. To learn more about how the event-driven approach works, and why it has special benefits for these use-cases, I spoke to Anson Kokkat, one of IBM’s leading experts on data modeling and data architecture design.
Anson Kokkat is a Product Manager at IBM, who works on the development of database modeling and tools. He started his career at IBM as a software engineer, leading a number of major database integration projects and supporting the development of the DB2 database platform. Today, he focuses on project management, sales enablement and customer advocacy, and presents exciting new IBM solutions to hundreds of clients worldwide.
Cindy Russell: Anson, in your experience, where are clients in their adoption cycle for these types of applications?
Anson Kokkat: We are already seeing companies putting these kinds of solutions into production – they’ve moved from neat data science projects and experimental prototypes into full-scale production.
That’s impressive because delivering these kinds of solutions is still a major technical achievement. Surprisingly, building an extensive network of IoT-connected sensors isn’t the main problem – the real challenge is in building a centralized architecture that is capable of ingesting and analyzing the vast quantities of data that these sensors produce. This is huge because if you can’t process the data fast enough, the insights may come too late to be useful.
IoT solutions aren’t the only type of system that faces this problem – it applies to any use-case where you have a huge amount of data streaming in, and you need to react to it in real time.
Another example might be a railroad that has acoustic sensors to detect the state of rails and bearings and help to prevent derailments: success depends on capturing and analyzing potentially millions of transactions per second to detect problems before they occur.
Or an online retailer might want its website to deliver a responsive customer experience by comparing an individual user’s previous shopping history with their current behavior as they browse the site – requiring near-instant analysis of every mouse-click.
Event-driven architectures have proven to be one of the best ways to solve the challenges of simultaneous high-volume data ingestion and high-speed analytics.
Cindy Russell: Can you describe what differentiates an event-driven architecture from a more traditional monolithic application platform?
Anson Kokkat: With a traditional approach, you would generally have an application platform connected to a database. The application would request create, read, update and delete (CRUD) operations, and the database would handle those requests and be responsible for maintaining the state of the application.
When you have a very large amount of data coming in, the problem is that there’s no separation of concerns. The same database engine is handling both reads and writes, which puts a brake on both types of operations.
