Browsing Tag

Enterprise Architecture

6 posts
MCP vs REST HTTP vs Apache Kafka -The Enterprise Architect Guide and Decision Tree to Agentic AI Integration
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  • 12 minute read

MCP vs. REST/HTTP API vs. Kafka: The Architect’s Guide to Agentic AI Integration

MCP, REST/HTTP APIs, and Apache Kafka are not alternatives. They solve different problems at different layers of the architecture. This article maps the decision: what each technology is built for, where the boundaries are, and where the real gray areas lie. Includes a comparison table and decision tree for architects.
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Shift Left Architecture 2.0 for the Era of Agentic AI with Kafka Flink Iceberg and MCP
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  • 10 minute read

The Shift Left Architecture 2.0: Operational, Analytical and AI Interfaces for Real-Time Data Products

The Shift Left Architecture moves data integration logic into an event-driven architecture where governed data products are built once and served to multiple consumers. The original pattern covered two interfaces: operational via Apache Kafka and analytical via Apache Iceberg. This post introduces the third: AI applications via MCP, powered by a real-time context engine that gives AI agents access to current operational data. Governance spans the full data stack through enterprise catalog tools. Together, the three interfaces turn a single data streaming investment into the foundation for operational, analytical, and AI-powered enterprise software.
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IT OT Convergence with Unified Namespace UNS and Data Product in Industrial IoT using Data Streaming Apache Kafka MQTT OPC UA
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  • 12.9K views
  • 13 minute read

Unified Namespace vs. Data Product in IT/OT for Industrial IoT

Industrial companies are connecting machines, sensors, and enterprise systems like never before. Real-time data, cloud-native platforms, and AI are driving this transformation—but only if silos between OT and IT can be broken down. This blog introduces two key architecture patterns that support IT/OT convergence. The Unified Namespace structures live OT data, while Data Products govern and deliver that data across IT systems. Technologies like MQTT, OPC UA and Apache Kafka play a central role in building scalable, secure, and real-time data pipelines. Combined, these patterns enable clean integration, better data quality, and faster time to value—laying the foundation for success in manufacturing, energy, logistics, and beyond.
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The Shift Left Architecture
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  • 8 minute read

The Shift Left Architecture – From Batch and Lakehouse to Real-Time Data Products with Data Streaming

Data integration is a hard challenge in every enterprise. Batch processing and Reverse ETL are common practices in a data warehouse, data lake or lakehouse. Data inconsistency, high compute cost, and stale information are the consequences. This blog post introduces a new design pattern to solve these problems: The Shift Left Architecture enables a data mesh with real-time data products to unify transactional and analytical workloads with Apache Kafka, Flink and Iceberg. Consistent information is handled with streaming processing or ingested into Snowflake, Databricks, Google BigQuery, or any other analytics / AI platform to increase flexibility, reduce cost and enable a data-driven company culture with faster time-to-market building innovative software applications.
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Apache Kafka and Snowflake Cost Efficiency and Data Governance
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  • 10 minute read

Apache Kafka + Flink + Snowflake: Cost Efficient Analytics and Data Governance

Snowflake is a leading cloud data warehouse and transitions into a data cloud that enables various use cases. The major drawback of this evolution is the significantly growing cost of the data processing. This blog post explores how data streaming with Apache Kafka and Apache Flink enables a “shift left architecture” where business teams can reduce cost, provide better data quality, and process data more efficiently. The real-time capabilities and unification of transactional and analytical workloads using Apache Iceberg’s open table format enable new use cases and a best of breed approach without a vendor lock-in and the choice of various analytical query engines like Dremio, Starburst, Databricks, Amazon Athena, Google BigQuery, or Apache Flink.
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The State of Data Streaming for Financial Services in 2023
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  • 9 minute read

The State of Data Streaming for Financial Services

This blog post explores the state of data streaming for financial services. The evolution of capital markets, retail banking and payments requires easy information sharing and open architecture. Data streaming allows integrating and correlating data in real-time at any scale. The foci are trending enterprise architectures for data streaming and customer stories. A complete slide deck and on-demand video recording are included.
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