Browsing Tag

flink

61 posts
Data Streaming and AI in the Automotive Industry at OEMs like Porsche Tesla BMW
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  • 331 views
  • 13 minute read

Driving the Future: How Real-Time Data Streaming Is Powering Automotive Innovation

The automotive industry is rapidly shifting toward a software-defined, data-driven future. Real-time technologies like Apache Kafka and Apache Flink are now critical to powering connected vehicles, smart factories, autonomous platforms, and personalized mobility services. This blog explores how leading OEMs and suppliers use data streaming to drive digital transformation – from edge processing and AI to predictive maintenance and customer experience. As the industry moves toward intelligent, adaptive systems, event-driven architecture becomes a strategic foundation.
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Agentic AI with AWS Amazon Bedrock AgentCore and Data Streaming using Apache Kafka Flink and Confluent Cloud
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  • 1.8K views
  • 6 minute read

Building Agentic AI with Amazon Bedrock AgentCore and Data Streaming Using Apache Kafka and Flink

Agentic AI goes beyond chatbots. These are autonomous systems that observe, reason, and act—continuously and in real time. At AWS Summit New York 2025, Amazon launched Bedrock AgentCore to build and operate secure, scalable AI agents. But to run in production, agents also need real-time data, continuous context, and flexible integration. That’s where data streaming with Apache Kafka and Apache Flink comes in. Combined with open standards like MCP and A2A, they provide the event-driven foundation for always-on, enterprise-grade AI agents.
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FourKites Supply Chain Logistics Control Tower Powered by AI and Data Streaming with Confluent and Kafka
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  • 1.2K views
  • 8 minute read

Inside FourKites Logistics Platform: Data Streaming for AI and End-to-End Visibility in the Supply Chain

Global supply chains face constant disruption. Trade conflicts, wars, inflation, and shifting regulations are making logistics more unpredictable than ever. Traditional systems can’t keep up with the speed and complexity of today’s challenges. This blog post shows how FourKites, a leader in supply chain visibility, uses data streaming with Apache Kafka in the cloud, and AI to power real-time logistics. With over 3 million shipments tracked daily, FourKites delivers more than visibility — it enables fast, autonomous decisions at global scale.
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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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FinTech Alpian using Data Streaming and Agentic AI with Apache Kafka in Switzerland Regulated Market
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  • 6 minute read

Agentic AI and RAG in Regulated FinTech with Apache Kafka at Alpian Bank

Regulated FinTech is transforming financial services by combining compliance with innovation. This post explores how real-time data streaming with Apache Kafka and Flink enables modern architecture, personalization, and AI integration—while maintaining strict governance. Alpian, a fully licensed Swiss digital bank, showcases how Agentic AI, RAG, and domain-driven design work together in a compliant, cloud-only environment.
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Durable Execution Engine with Restate Temporal DBOS vs Stream Processing with Kafka Streams Apache Flink Spark Structured Streaming
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  • 8 minute read

­­The Rise of the Durable Execution Engine (Temporal, Restate) in an Event-driven Architecture (Apache Kafka)

Durable execution engines like Temporal and Restate are redefining how developers orchestrate long-running, stateful workflows in distributed systems. Unlike traditional BPM tools focused on human-centric tasks, these engines automate machine-to-machine processes with built-in durability, retries, and fault-tolerant coordination. When integrated with event-driven platforms like Apache Kafka, they enable scalable, resilient architectures—handling complex business logic such as order processing, fraud detection, and multi-step transactions. This blog explores their capabilities, differences from stream processing tools like Apache Flink, Kafka Streams or Spark Structured Streaming, and the emerging role they play in modern enterprise infrastructure.
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Agentic AI with Apache Kafka as Event Broker Combined with MCP and A2A Protocol
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  • 11.4K views
  • 7 minute read

Agentic AI with the Agent2Agent Protocol (A2A) and MCP using Apache Kafka as Event Broker

Agentic AI is emerging as a powerful pattern for building autonomous, intelligent, and collaborative systems. To move beyond isolated models and task-based automation, enterprises need a scalable integration architecture that supports real-time interaction, coordination, and decision-making across agents and services. This blog explores how the combination of Apache Kafka, Model Context Protocol (MCP), and Google’s Agent2Agent (A2A) protocol forms the foundation for Agentic AI in production. By replacing point-to-point APIs with event-driven communication as the integration layer, enterprises can achieve decoupling, flexibility, and observability—unlocking the full potential of AI agents in modern enterprise environments.
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Enterprise Application Integration with Confliuent and Databricks for Oracle SAP Salesforce Servicenow et al
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  • 3.1K views
  • 12 minute read

Databricks and Confluent in the World of Enterprise Software (with SAP as Example)

Enterprise data lives in complex ecosystems—SAP, Oracle, Salesforce, ServiceNow, IBM Mainframes, and more. This article explores how Confluent and Databricks integrate with SAP to bridge operational and analytical workloads in real time. It outlines architectural patterns, trade-offs, and use cases like supply chain optimization, predictive maintenance, and financial reporting, showing how modern data streaming unlocks agility, reuse, and AI-readiness across even the most SAP-centric environments.
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Confluent and Databricks for Data Integration and Stream Processing
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  • 3.4K views
  • 10 minute read

Confluent Data Streaming Platform vs. Databricks Data Intelligence Platform for Data Integration and Processing

This blog explores how Confluent and Databricks address data integration and processing in modern architectures. Confluent provides real-time, event-driven pipelines connecting operational systems, APIs, and batch sources with consistent, governed data flows. Databricks specializes in large-scale batch processing, data enrichment, and AI model development. Together, they offer a unified approach that bridges operational and analytical workloads. Key topics include ingestion patterns, the role of Tableflow, the shift-left architecture for earlier data validation, and real-world examples like Uniper’s energy trading platform powered by Confluent and Databricks.
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Data Streaming and Lakehouse - Comparison of Confluent with Apache Kafka and Flink and Databricks with Spark
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  • 3.4K views
  • 9 minute read

The Past, Present, and Future of Confluent (The Kafka Company) and Databricks (The Spark Company)

Confluent and Databricks have redefined modern data architectures, growing beyond their Kafka and Spark roots. Confluent drives real-time operational workloads; Databricks powers analytical and AI-driven applications. As operational and analytical boundaries blur, native integrations like Tableflow and Delta Lake unify streaming and batch processing across hybrid and multi-cloud environments. This blog explores the platforms’ evolution and how, together, they enable enterprises to build scalable, data-driven architectures. The Michelin success story shows how combining real-time data and AI unlocks innovation and resilience.
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