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The Data Streaming Landscape 2025

Data streaming is a new software category. It has grown from niche adoption to becoming a fundamental part of modern data architecture, leveraging open source technologies like Apache Kafka and Flink. With real-time data processing transforming industries, the ecosystem of tools, platforms, and cloud services has evolved significantly. This blog post explores the data streaming landscape of 2025, analyzing key players, trends, and market dynamics shaping this space.
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Data Streaming Trends for 2025 - Leading with Apache Kafka and Flink
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Top Trends for Data Streaming with Apache Kafka and Flink in 2025

Apache Kafka and Apache Flink are leading open-source frameworks for data streaming that serve as the foundation for cloud services, enabling organizations to unlock the potential of real-time data. Over recent years, trends have shifted from batch-based data processing to real-time analytics, scalable cloud-native architectures, and improved data governance powered by these technologies. Looking ahead to 2025, the data streaming ecosystem is set to undergo even greater changes. Here are the top trends shaping the future of data streaming for businesses.
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Data Streaming with Apache Kafka and Flink in the Cloud in Healthcare and Pharma at Cardinal Health
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Data Streaming in Healthcare and Pharma: Use Cases and Insights from Cardinal Health

This blog delves into Cardinal Health’s journey, exploring how its event-driven architecture and data streaming power use cases like supply chain optimization, and medical device and equipment management. By integrating Apache Kafka with platforms like Apigee, Dell Boomi and SAP, Cardinal Health sets a benchmark for IT modernization and innovation in the healthcare and pharma sectors.
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Dynamic Pricing with Data Streaming using Apache Kafka and Flink
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A New Era in Dynamic Pricing: Real-Time Data Streaming with Apache Kafka and Flink

In the age of digitization, the concept of pricing is no longer fixed or manual. Instead, companies increasingly use dynamic pricing — a flexible model that adjusts prices based on real-time market changes to enable real-time responsiveness, giving companies the tools they need to respond instantly to demand, competitor prices, and customer behaviors. This blog post explores the fundamentals of dynamic pricing, its link to data streaming, and real-world examples across different industries such as retail, logistics, gaming and the energy sector.
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Intelligent Traffic System for Tolling with Dynamic Pricing and Enforcement with Apache Kafka
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IoT and Data Streaming with Kafka for a Tolling Traffic System with Dynamic Pricing

In the rapidly evolving landscape of intelligent traffic systems, innovative software provides real-time processing capabilities, dynamic pricing and new customer experiences, particularly in the domains of tolling, payments and safety inspection. This blog post delves into success stories from Quarterhill and DKV Mobility providing traffic and payment systems for tolls. Data streaming powered by Apache Kafka has been pivotal in the journey towards building intelligent traffic systems in the cloud.
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Fraud Prevention with Apache Kafka in Real Time in Financial Services and Banking
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Fraud Prevention in Under 60 Seconds with Apache Kafka: How A Bank in Thailand is Leading the Charge

In the fast-paced world of finance, the ability to prevent fraud in real-time is not just a competitive advantage – it is a necessity. For one of the largest banks in Thailand Krungsri (Bank of Ayudhya), with its vast assets, loans, and deposits, the challenge of fraud prevention has taken center stage. This blog post explores how the bank is leveraging data streaming with Apache Kafka to detect and block fraudulent transactions in under 60 seconds to ensure the safety and trust of its customers.
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Serverless Data Streaming on Azure Cloud with Apache Kafka Event Hubs Confluent Cloud for OneLake and Microsoft Fabric
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When to Choose Apache Kafka vs. Azure Event Hubs vs. Confluent Cloud for a Microsoft Fabric Lakehouse

Choosing between Apache Kafka, Azure Event Hubs, and Confluent Cloud for data streaming is critical when building a Microsoft Fabric Lakehouse. Each option caters to different needs, and this blog post will guide you in selecting the right data streaming solution for your use case.
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Lakehouse and Data Streaming - Competitor or Complementary
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How Microsoft Fabric Lakehouse Complements Data Streaming (Apache Kafka, Flink, et al.)

In today’s data-driven world, understanding data at rest versus data in motion is crucial for businesses. Data streaming frameworks like Apache Kafka and Apache Flink enable real-time data processing. Meanwhile, lakehouses like Snowflake, Databricks, and Microsoft Fabric excel in long-term data storage and detailed analysis, perfect for reports and AI training. This blog post delves into how these technologies complement each other in enterprise architecture.
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Microsoft Fabric and OneLake Azure Lakehouse vs Databricks and Snowflake Cloud
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What is Microsoft Fabric for Azure Cloud (Beyond the Buzz) and how it Competes with Snowflake and Databricks

If you ask your favorite large language model, Microsoft Fabric appears to be the ultimate solution for any data challenge you can imagine. That’s also the impression many people get from Microsoft’s sales teams. But is it really the silver bullet it’s made out to be? This article takes a closer look exploring the glossy marketing and sales definition of the platform and then deconstructing it from a more practical perspective. Learn what Microsoft Fabric is truly built for, and how it fits into the wider data landscape, especially in comparison to other major players in the data analytics market like Databricks and Snowflake.
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Real-Time AI ML Model Inference Predictive AI and Generative AI with Data Streaming using Apache Kafka and Flink
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Real-Time Model Inference with Apache Kafka and Flink for Predictive AI and GenAI

Artificial Intelligence (AI) and Machine Learning (ML) are transforming business operations by enabling systems to learn from data and make intelligent decisions for predictive and generative AI use cases. Two essential components of AI/ML are model training and inference. This blog post explores how data streaming with Apache Kafka and Flink enhances the performance and reliability of model predictions. Whether for real-time fraud detection, smart customer service applications or predictive maintenance, understanding the value of data streaming for model inference is crucial for leveraging AI/ML effectively.
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