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Data Governance

5 posts
Real-Time FinOps with Data Streaming using Apache Kafka and Flink
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  • 6 minute read

FinOps in Real Time: How Data Streaming Transforms Cloud Cost Management

FinOps bridges the gap between finance and engineering to control cloud spend in real time. However, many organizations still rely on delayed, batch-driven data pipelines that limit visibility and slow down decisions. This blog explores how Apache Kafka and Apache Flink enable real-time, governed FinOps by streaming cloud usage data as it happens. It covers the challenges of data governance, compliance, and cross-functional accountability—and how streaming architecture addresses them. Real-world examples from Bitvavo and SumUp show how financial services companies scale securely, build cost-aware teams, and improve agility using event-driven platforms.
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Data Lineage for Data Streaming with OpenLineage Apache Kafka and Flink
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  • 11 minute read

Open Standards for Data Lineage: OpenLineage for Batch AND Streaming

One of the greatest wishes of companies is end-to-end visibility in their operational and analytical workflows. Where does data come from? Where does it go? To whom am I giving access to? How can I track data quality issues? The capability to follow the data flow to answer these questions is called data lineage. This blog post explores market trends, efforts to provide an open standard with OpenLineage, and how data governance solutions from vendors such as IBM, Google, Confluent and Collibra help fulfil the enterprise-wide data governance needs of most companies, including data streaming technologies such as Apache Kafka and Flink.
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Policy Enforcement and Data Quality for Apache Kafka with Schema Registry
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  • 7 minute read

Policy Enforcement and Data Quality for Apache Kafka with Schema Registry

Good data quality is one of the most critical requirements in decoupled architectures, like microservices or data mesh. Apache Kafka became the de facto standard for these architectures. But Kafka is a dumb broker that only stores byte arrays. The Schema Registry enforces message structures. This blog post looks at enhancements to leverage data contracts for policies and rules to enforce good data quality on field-level and advanced use cases like routing malicious messages to a dead letter queue.
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Apache Kafka for Data Consistency (and Real-Time Data Streaming)
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Apache Kafka for Data Consistency (and Real-Time Data Streaming)

Real-time data beats slow data in almost all use cases. But as essential is data consistency across all systems, including non-real-time legacy systems and modern request-response APIs. Apache Kafka’s most underestimated feature is the storage component based on the append-only commit log. It enables loose coupling for domain-driven design with microservices and independent data products in a data mesh. This blog post explores how Kafka enables data consistency with a real-world case study from financial services.
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Top Use Cases and Architectures for Data Streaming with Apache Kafka in 2023
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Top 5 Data Streaming Trends for 2023

Data Streaming is one of the most relevant buzzwords in tech to build scalable real-time applications in the cloud and innovative business models. Do you wonder about my predicted TOP 5 data streaming trends in 2023 to set data in motion? Check out the following presentation and learn what role Apache Kafka plays. Learn about decentralized Data Mesh, cloud-native lakehouse, data sharing, improved user experience, and advanced data governance.
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