We have taken a proactive approach to modernizing our data pipelines at Onclusive, emphasizing cost optimization, performance, and scalability. Initially, we relied on AWS managed services such as Lambdas, Polars, and Kinesis, which proved to be quite expensive. As a result, we transitioned to a self-managed Kafka cluster, which is both scalable and cost-effective. Kafka can handle large volumes of data and allows us to adjust our capacity as needed.

To further optimize costs, we replaced many Lambdas with Kafka and Apache Beam. Since Lambdas operate on a pay-per-execution basis, they can become costly when running multiple instances simultaneously. With Kafka and Apache Beam, we can implement auto-scaling policies that enable us to increase the number of executors when demand rises and decrease them when it falls. This flexibility significantly reduces our expenses.

For instance, if we receive 10 GB of data in the morning, we can scale up the executors to process this data quickly. As the data flow decreases, we scale down accordingly. This strategy is more economical than maintaining numerous Lambdas.

In summary, by leveraging Kafka and Apache Beam, we have enhanced performance, reduced costs, and ensured scalability. Our modernized pipelines are now efficient and cost-effective.