Browsing Tag

AI

24 posts
Data Streaming Trends for 2026 with Apache Kafka Flink Diskless Cloud Agentic AI
Read More
  • 882 views
  • 9 minute read

Top Trends for Data Streaming with Apache Kafka and Flink in 2026

Each year brings new momentum to the data streaming space. In 2026, six key trends stand out. Platforms and vendors are consolidating. Diskless Kafka and Apache Iceberg are reshaping storage. Real-time analytics is moving into the stream. Enterprises demand zero data loss and regional compliance. Streaming is now powering operational AI with real-time context. Data streaming has evolved. It is now strategic infrastructure at the heart of modern enterprise systems.
Read More
0
0
0
Online Feature Store for AI ML with Data Streaming using Apache Kafka Flink FlinkSQL Confluent Cloud at Wix
Read More
  • 4.5K views
  • 6 minute read

Online Feature Store for AI and Machine Learning with Apache Kafka and Flink

Real-time personalization requires more than just smart models. It demands fresh data, fast processing, and scalable infrastructure. This blog post explores how Wix.com rebuilt its online feature store using Apache Kafka and Flink, turning their AI architecture into a real-time powerhouse that supports personalized experiences for millions of users.
Read More
0
0
0
The Future of Data Streaming with Apache Flink for Agentic AI Supporting A2A and MCP
Read More
  • 9.8K views
  • 8 minute read

The Future of Data Streaming with Apache Flink for Agentic AI

Agentic AI is moving into production. Autonomous, tool-using, goal-driven systems that need real-time data and context. Apache Kafka and Flink provide the event-driven foundation to run these agents at scale. With the new Flink Agents project (FLIP-531), Flink will natively support long-running, system-triggered AI agents integrated with LLMs, tools, and emerging protocols like MCP and A2A. This marks a major step toward reliable, enterprise-grade Agentic AI.
Read More
0
0
0
The Rise of Kappa Architecture in the Age of Agentic AI with Data Streaming using Apache Kafka and Flink
Read More
  • 7.4K views
  • 9 minute read

The Rise of Kappa Architecture in the Era of Agentic AI and Data Streaming

The shift from Lambda to Kappa architecture reflects the growing demand for unified, real-time data pipelines that serve both analytical and operational needs. With the rise of Agentic AI and streaming-first systems, Kappa—powered by Apache Kafka and Apache Flink—delivers low-latency, event-driven infrastructure that supports modern applications, from scalable data products to autonomous AI agents. Open table formats and Shift Left principles further establish Kappa as the foundation for consistent, governed, and future-ready data platforms.
Read More
0
0
0
Agentic AI with Apache Kafka as Event Broker Combined with MCP and A2A Protocol
Read More
  • 26.8K 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.
Read More
0
0
1
Data Streaming Lake Warehouse and Lakehouse with Confluent Databricks Snowflake using Iceberg and Tableflow Delta Lake
Read More
  • 11.9K views
  • 12 minute read

Databricks and Confluent Leading Data and AI Architectures – What About Snowflake, BigQuery, and Friends?

Confluent, Databricks, and Snowflake are trusted by thousands of enterprises to power critical workloads—each with a distinct focus: real-time streaming, large-scale analytics, and governed data sharing. Many customers use them in combination to build flexible, intelligent data architectures. This blog highlights how Erste Bank uses Confluent and Databricks to enable generative AI in customer service, while Siemens combines Confluent and Snowflake to optimize manufacturing and healthcare with a shift-left approach. Together, these examples show how a streaming-first foundation drives speed, scalability, and innovation across industries.
Read More
0
0
0
Data Streaming and Lakehouse - Comparison of Confluent with Apache Kafka and Flink and Databricks with Spark
Read More
  • 9.3K 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.
Read More
0
0
0
Fraud Prevention in Mobility Services with Data Streaming using Apache Kafka and Flink with AI Machine Learning
Read More
  • 10.5K views
  • 7 minute read

Fraud Detection in Mobility Services (Ride-Hailing, Food Delivery) with Data Streaming using Apache Kafka and Flink

Mobility services like Uber, Grab, and FREE NOW (Lyft) rely on real-time data to power seamless trips, deliveries, and payments. But this real-time nature also opens the door to sophisticated fraud schemes—ranging from GPS spoofing to payment abuse and fake accounts. Traditional fraud detection methods fall short in speed and adaptability. By using Apache Kafka and Apache Flink, leading mobility platforms now detect and block fraud as it happens, protecting their revenue, users, and trust. This blog explores how real-time data streaming is transforming fraud prevention across the mobility industry.
Read More
0
0
0
Event-Driven Agentic AI with Data Streaming using Apache Kafka and Flink
Read More
  • 26.5K views
  • 12 minute read

How Apache Kafka and Flink Power Event-Driven Agentic AI in Real Time

Agentic AI marks a major evolution in artificial intelligence—shifting from passive analytics to autonomous, goal-driven systems capable of planning and executing complex tasks in real time. To function effectively, these intelligent agents require immediate access to consistent, trustworthy data. Traditional batch processing architectures fall short of this need, introducing delays, data staleness, and rigid workflows. This blog post explores why event-driven architecture (EDA)—powered by Apache Kafka and Apache Flink—is essential for building scalable, reliable, and adaptive AI systems. It introduces key concepts such as Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) protocol, which are redefining interoperability and context management in multi-agent environments. Real-world use cases from finance, healthcare, manufacturing, and more illustrate how Kafka and Flink provide the real-time backbone needed for production-grade Agentic AI. The post also highlights why popular frameworks like LangChain and LlamaIndex must be complemented by robust streaming infrastructure to support stateful, event-driven AI at scale.
Read More
0
0
0
Learnings from the CIO Summit: AI + Data Streaming = Key for Success
Read More
  • 2.2K views
  • 5 minute read

CIO Summit: The State of AI and Why Data Streaming is Key for Success

The CIO Summit in Amsterdam provided a valuable perspective on the state of AI adoption across industries. While enthusiasm for AI remains high, organizations are grappling with the challenge of turning potential into tangible business outcomes. Key discussions centered on distinguishing hype from real value, the importance of high-quality and real-time data, and the role of automation in preparing businesses for AI integration. A recurring theme was that AI is not a standalone solution—it must be supported by a strong data foundation, clear ROI objectives, and a strategic approach. As AI continues to evolve toward more autonomous, agentic systems, data streaming will play a critical role in ensuring AI models remain relevant, context-aware, and actionable in real time.
Read More
0
0
0