Unlock Self-Serve Streaming SQL with Amazon Managed Service for Apache Flink – AWS: A Beginner’s Guide

Businesses are using cutting-edge technologies like Apache Flink, Apache Spark, and Apache Kafka Streams to analyze real-time transaction data, detect anomalies, and improve customer experiences. Riskified, a fraud prevention platform, relies on these technologies to distinguish legitimate customers from fraudulent ones and safeguard businesses from losses due to fraudulent transactions.

Riskified’s core focus is on real-time fraud prevention, making streaming technologies essential for their operations. They needed a user-friendly interface to create streaming pipelines that could accurately detect fraudulent transactions. To address this need, Riskified embarked on a journey to enable self-service streaming SQL pipelines.

By using SQL to create streaming pipelines, businesses can process real-time data with minimal complexity. SQL offers a powerful and intuitive way to perform real-time transformations, filtering, aggregations, and joins in continuous data streams. For example, Riskified implemented a real-time velocity check using streaming SQL to monitor purchasing behavior based on user identifiers. This approach allowed them to detect potentially fraudulent activities in real-time and take appropriate actions to prevent losses.

While Riskified initially used Confluent ksqlDB for its streaming pipelines, they eventually faced limitations related to schema evolution, resource management, and pipeline abstraction. This led them to explore alternative technologies that could better support their expanding streaming use cases. Ultimately, Riskified turned to Amazon Managed Service for Apache Flink to build a scalable, accessible, and production-ready streaming architecture.

By transitioning to Apache Flink, Riskified was able to address the complexities of developing and maintaining robust streaming applications. With Amazon Managed Service for Apache Flink, Riskified could overcome the challenges they encountered with Confluent ksqlDB and create a more efficient and comprehensive streaming solution. This shift allowed Riskified to improve the precision of their fraud detection mechanisms and streamline their streaming operations.

In conclusion, by leveraging streaming SQL and transitioning to Apache Flink, Riskified was able to enhance their fraud prevention capabilities, reduce risk, and provide a better experience for their customers across various industries. This journey highlights the importance of utilizing cutting-edge technologies to stay ahead of emerging trends, detect anomalies, and optimize business operations in real-time.