Data Streaming is not a Race it is a Journey
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Data Streaming is not a Race, it is a Journey!

Data Streaming is not a race, it is a journey! Event-driven architectures and technologies like Apache Kafka or Apache Flink require a mind shift in architecting, developing, deploying, and monitoring applications. This blog post explores success stories from data streaming journeys across industries, including banking, retail, insurance, manufacturing, healthcare, energy & utilities, and software companies.
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The Real-Time Food and Retail Supply Chain powered by Apache Kafka
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Real-Time Supply Chain with Apache Kafka in the Food and Retail Industry

This blog post explores real-world deployments across the end-to-end supply chain powered by data streaming with Apache Kafka to improve business processes with real-time services. The examples include manufacturing, logistics, stores, delivery, restaurants, and other parts of the business. Case studies include Walmart, Albertsons, Instacart, Domino’s Pizza, Migros, and more.
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When not to use Apache Kafka
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When NOT to use Apache Kafka?

Apache Kafka is the de facto standard for event streaming to process data in motion. This blog post explores when NOT to use Apache Kafka. What use cases are not a good fit for Kafka? What limitations does Kafka have? How to qualify Kafka out as it is not the right tool for the job?
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Apache Kafka Smart Building Real Estate Smart City Energy Consumption IoT Analytics
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IoT Analytics with Kafka for Real Estate and Smart Building

This blog post explores how event streaming with Apache Kafka enables IoT analytics for cost savings, better consumer experience, and reduced risk in real estate and smart buildings. Examples include improved real estate maintenance and operations, smarter energy consumptions, optimized space usage, better employee experience, and better defense against cyber attacks.
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Apache Kafka in the Public Sector for Smart City Infrastructure
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Apache Kafka in the Public Sector – Part 2: Smart City

The public sector includes many different areas. Some groups leverage cutting-edge technology, like military leverage. Others like the public administration are years or even decades behind. This blog series explores both edges to show how data in motion powered by Apache Kafka adds value for innovative new applications and modernizing legacy IT infrastructures. This is part 2: Use cases and architectures for a Smart City.
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Apache Kafka in the Public Sector – Blog Series about Use Cases and Architectures

The public sector includes many different areas. Some groups leverage cutting-edge technology, like military leverage. Others like the public administration are years or even decades behind. This blog series explores both edges to show how data in motion powered by Apache Kafka adds value for innovative new applications and modernizing legacy IT infrastructures. Examples include a broad spectrum of use cases across smart cities, citizen services, energy and utilities, and national security.
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Reverse ETL Anti Pattern vs Event Streaming with Apache Kafka
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When to Use Reverse ETL and when it is an Anti-Pattern

This blog post explores why software vendors (try to) introduce new solutions for Reverse ETL, when Reverse ETL is really needed, and how it fits into the enterprise architecture. The involvement of event streaming to process data in motion is a key piece of Reverse ETL for real-time use cases.
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Cyber Situational Awareness with Apache Kafka
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Kafka for Cybersecurity (Part 2 of 6) – Cyber Situational Awareness

This blog series explores use cases and architectures for Apache Kafka in the cybersecurity space, including situational awareness, threat intelligence, forensics, air-gapped and zero trust environments, and SIEM / SOAR modernization. This post is part two: Situational awareness with continuous real-time data integration and data processing at scale.
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Serverless Kafka for Data in Motion as Rescue for Data at Rest in the Data Lake
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Serverless Kafka in a Cloud-native Data Lake Architecture

Apache Kafka became the de facto standard for processing data in motion. Kafka is open, flexible, and scalable. Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use a serverless Kafka SaaS offering to focus on business logic. However, hybrid scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden. This blog post explores how to leverage cloud-native and serverless Kafka offerings in a hybrid cloud architecture. We start from the perspective of data at rest with a data lake and explore its relation to data in motion with Kafka.
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