Real-time data beats slow data. It is that easy! But what is real-time? The term always needs to…
Data streaming emerged as a new software category. It complements traditional middleware, data warehouse, and data lakes. Apache Kafka became the de facto standard. New players enter the market because of Kafka’s success. One of those is Redpanda, a lightweight Kafka-compatible C++ implementation. This blog post explores the differences between Apache Kafka and Redpanda, when to choose which framework, and how the Kafka ecosystem, licensing, and community adoption impact a proper evaluation.
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.
Fraud detection becomes increasingly challenging in a digital world across all industries. Real-time data processing with Apache Kafka became the de facto standard to correlate and prevent fraud continuously before it happens. This blog post explores case studies for fraud prevention from companies such as Paypal, Capital One, ING Bank, Grab, and Kakao Games that leverage stream processing technologies like Kafka Streams, KSQL, and Apache Flink.
Industrial IoT and Industry 4.0 enable digitalization and innovation. SCADA control systems are a vital component of IT/OT modernization. The SCADA evolution started with monolithic applications and moved to networked and web-based platforms. This blog post explores building the 5th generation: A cloud-native SCADA infrastructure with Apache Kafka. A real-world case study explores the journey of a German system operator for electricity to show how such a journey to open and scalable real-time workloads and edge-to-cloud integration progressed.
Logistics, shipping, and transportation require real-time information to build efficient applications and innovative business models. Data streaming enables correlated decisions, recommendations, and alerts. Kafka is everywhere across the industry. This blog post explores several real-world case studies from companies such as USPS, Swiss Post, Austrian Post, DHL, and Hermes. Use cases include cloud-native middleware modernization, track and trace, and predictive routing and ETA planning.
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.
The sports world is changing. Digitalization is everywhere. Cameras and sensors analyze matches. Stadiums get connected and incorporate mobile apps and location-based services. Players use social networks to influence and market themselves and consumer products. Real-time data processing is crucial for most innovative sports use cases. This blog post explores how data streaming with Apache Kafka helps reimagine the sports industry, showing a concrete example from the worldwide table tennis organization.
Apache Kafka became the de facto standard for data streaming. Various cloud offerings emerged and improved in the last years. Amazon MSK Serverless is the latest Kafka product from AWS. This blog post looks at its capabilities to explore how it relates to “the normal” partially managed Amazon MSK, when the serverless version is a good choice, and when other fully-managed cloud services like Confluent Cloud are the better option.
Even digital natives – that started their business in the cloud without legacy applications in their own data centers – need to modernize their cloud-native enterprise architecture to improve business processes, reduce costs, and provide real-time information to their downstream applications. This blog post explores the benefits of an open and flexible data streaming platform compared to a proprietary message queue and data ingestion cloud services. A concrete example shows how DoorDash replaced cloud-native AWS SQS and Kinesis with Apache Kafka and Flink.