Lately, companies, in their efforts to engage in real-time decision-making by exploiting big data, have been inclined to find a suitable architecture for this data as quickly as possible. With many companies, including SaaS users, choosing to deploy their own infrastructures entirely on their own, the combination of Apache Flink and Kafka offers low-latency data pipelines that are built for complete reliability.
Particularly due to the financial and technical constraints it brings, small and medium-sized enterprises often have a number of challenges to overcome when using cloud service providers. One major issue is the complexity of cloud pricing models, which can lead to unexpected costs and budget overruns. This article explores how to design, build, and deploy a predictive machine learning (ML) model using Flink and Kafka in an on-premises environment to power real-time analytics.