Amid unprecedented volumes of data being generated, organizations need to harness the value of their data from heterogeneous systems in real time. However, on-prem databases are slow, rigid, and expensive to maintain, limiting the speed at which businesses can scale and drive innovation. Today’s organizations need scalable, cloud-native databases with real-time data. This demo walks you through building streaming data pipelines with Confluent Cloud. You’ll learn about:
- Confluent’s fully managed source connectors to stream customer data and credit card transactions in real time into Confluent Cloud
- Process and enrich data streams in real time. You'll use aggregates and windowing to create a customer list of potentially stolen credit cards
- A fully managed sink connector to load enriched data into MongoDB Atlas for real-time fraud analysis
Break down data silos and stream on-premises, hybrid, and multicloud data to cloud databases such as MongoDB Atlas, Azure Cosmos DB and more, so that every system and application has a consistent, up-to-date, and enhanced view of the data at all times. With Confluent streaming data pipelines, you can connect, process, and govern real-time data for all of your databases. Unlock real-time insights, focus on building innovative apps instead of managing databases, and confidently pave a path to cloud migration and transformation.
To learn more about Confluent’s solution, visit the Database streaming pipelines page
There are two versions of this demo
- Using ksqlDB as the stream processing engine.
- In this version there are two source connectors (Oracle CDC and RabbitMQ)
- Oracle database contains customers information
- RabbitMQ contains each customer's credit card transactions
- Using Flink SQL as the stream processing engine
- In this version there is one source connector (Oracle CDC) and one Python producer
- Oracle database contains customers information
- Python producer generates sample credit card transactions
