Policy Enforcement and Data Quality for Apache Kafka with Schema Registry
Read More

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.
Read More
Apache Kafka for Data Consistency (and Real-Time Data Streaming)
Read More

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.
Read More
Decentralized Data Mesh with Data Streaming in Financial Services and Banking
Read More

Decentralized Data Mesh with Data Streaming in Financial Services

Digital transformation requires agility and fast time to market as critical factors for success in any enterprise. The decentralization with a data mesh separates applications and business units into independent domains. Data sharing in real-time with data streaming helps to provide information in the proper context to the correct application at the right time. This blog post explores a case study from the financial services sector where a data mesh was built across countries for loosely coupled data sharing but standardized enterprise-wide data governance.
Read More
Real-Time Supply Chain Control Tower with Apache Kafka
Read More

A Real-Time Supply Chain Control Tower powered by Kafka

A modern supply chain requires just-in-time production, global logistics, and complex manufacturing processes. This blog post explores a solution that ingests all information flows into a unified central nervous system. The idea of the Supply Chain Control Tower becomes a reality: An integrated data cockpit with real-time access to all levels and systems of the supply chain.
Read More
The Heart of the Data Mesh Beats Real Time with Apache Kafka
Read More

The Heart of the Data Mesh Beats Real-Time with Apache Kafka

If there were a buzzword of the hour, it would undoubtedly be “data mesh”! This new architectural paradigm unlocks analytic and transactional data at scale and enables rapid access to an ever-growing number of distributed domain datasets for various usage scenarios. The data mesh addresses the most common weaknesses of the traditional centralized data lake or data platform architecture. And the heart of a decentralized data mesh infrastructure must be real-time, reliable, and scalable. Learn how the de facto standard for data streaming, Apache Kafka, plays a crucial role in building a data mesh.
Read More
Best Practices for Data Analytics with AWS Azure Googel BigQuery Spark Kafka Confluent Databricks
Read More

Best Practices for Building a Cloud-Native Data Warehouse or Data Lake

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 5: Best Practices for Building a Cloud-Native Data Warehouse or Data Lake.
Read More
Data Warehouse vs Data Lake vs Data Streaming Comparison
Read More

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 1: Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?
Read More
Stream Exchange for Data Sharing with Apache Kafka in a Data Mesh
Read More

Streaming Data Exchange with Kafka and a Data Mesh in Motion

Data Mesh is a new architecture paradigm that gets a lot of buzzes these days. This blog post looks into this principle deeper to explore why no single technology is the perfect fit to build a  Data Mesh. Examples show why an open and scalable decentralized real-time platform like Apache Kafka is often the heart of the Data Mesh infrastructure, complemented by many other data platforms to solve business problems.
Read More