ING is a data-driven experimental enterprise that is heavily investing in big data, analytics, and streaming processing. As do many other enterprises, ING deals with a large variety of data sources. The amount of data which must be handled goes beyond the computing performance of single machines, and vertical scalability is hardly an option. For this specific reason, ING is moving toward a scenario where machines are grouped into clusters and data is handled by distributed processing systems.
ING’s state-of-the-art data lake and streaming data platform are important building blocks for this transformation. The data lake, built around Hadoop and IBM PDA, replaces several enterprise data warehouses and is the central repository for all types of data, supporting various types of queries for stakeholders’ demands. Data is also being handled more often than not as streams—ING works with Flink and Kafka to provide faster, more reactive up-to-date user experiences and journeys. Finally, machine learning aids traditional SQL to provide better insight when it comes to operational excellence, business processes, marketing, and security applications.
Bas Geerdink explains why and how ING is becoming more and more data-driven, sharing use cases, architecture, and technology choices along the way.
Bas Geerdink is a programmer, scientist, and IT manager at ING, where he’s responsible for the fast data systems that process and analyze streaming data. Bas has a background in software development, design, and architecture with broad technical experience from C++ to Prolog to Scala. His academic background is in artificial intelligence and informatics. Bas’s research on reference architectures for big data solutions was published at the IEEE conference ICITST 2013. He occasionally teaches programming courses and is a regular speaker at conferences and informal meetings.
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