Partner Repost: Using Streaming Analytics to Identify and Visualise Fraudulent ATM Transactions in Real-Time

Storing and analyzing large amounts of data is no longer the primary focus for successful companies, as speed in providing relevant information to decision-makers has become more crucial. Streaming analytics help identify perishable insights, which require immediate attention to avoid missing business opportunities. However, many companies view implementing a streaming analytics platform as a complex and costly project. In reality, using the right technologies and tools can set up such a platform quickly and effectively. A solution that identifies fraudulent ATM transactions in real-time has been built using a simple architecture, leveraging Confluent Kafka for data buffering and KSQL for SQL-like querying capabilities. The high-level architecture is comprised of three steps: building and analyzing streams of data, ingesting streams into a data store in real-time, and visualizing the data in real-time. This solution utilizes SingleStore for data ingestion and storage, which integrates seamlessly with Confluent Kafka through SingleStore Pipelines. Zoomdata is used to visualize the data in real-time, leveraging its smart query engine and Data DVR technology to connect to the source data stream immediately, reflecting changes as they occur.

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