The Strangler Fig Design Pattern - Migration and Replacement of Legacy IT Applications with Data Streaming using Apache Kafka
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Replacing Legacy Systems, One Step at a Time with Data Streaming: The Strangler Fig Approach

Modernizing legacy systems doesn’t have to mean a risky big-bang rewrite. This blog explores how the Strangler Fig Pattern, when combined with data streaming, enables gradual, low-risk transformation—unlocking real-time capabilities, reducing complexity, and supporting scalable, cloud-native architectures. Discover how leading organizations are using this approach to migrate at their own pace, stay compliant, and enable new business models. Plus, why Reverse ETL falls short and streaming is the future of IT modernization.
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Data Streaming with Apache Kafka and Flink as the Backbone for a B2B Data Marketplace
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Data Streaming as the Technical Foundation for a B2B Marketplace

A B2B data marketplace empowers businesses to exchange, monetize, and leverage real-time data through self-service platforms featuring subscription management, usage-based billing, and secure data sharing. Built on data streaming technologies like Apache Kafka and Flink, these marketplaces deliver scalable, event-driven architectures for seamless integration, real-time processing, and compliance. By exploring successful implementations like AppDirect, this post highlights how organizations can unlock new revenue streams and foster innovation with modern data marketplace solutions.
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SaaS vs PaaS Cloud Service for Data Streaming with Apache Kafka and Flink
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Fully Managed (SaaS) vs. Partially Managed (PaaS) Cloud Services for Data Streaming with Kafka and Flink

The cloud revolution has reshaped how businesses deploy and manage data streaming with solutions like Apache Kafka and Flink. Distinctions between SaaS and PaaS models significantly impact scalability, cost, and operational complexity. Bring Your Own Cloud (BYOC) expands the options, giving businesses greater flexibility in cloud deployment. Misconceptions around terms like “serverless” highlight the need for deeper analysis to avoid marketing pitfalls. This blog explores deployment options, enabling informed decisions tailored to your data streaming needs.
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Apache Flink - Overkill for Simple Stateless Stream Processing
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Apache Flink: Overkill for Simple, Stateless Stream Processing and ETL?

Discover when Apache Flink is the right tool for your stream processing needs. Explore its role in stateful and stateless processing, the advantages of serverless Flink SaaS solutions like Confluent Cloud, and how it supports advanced analytics and real-time data integration together with Apache Kafka. Dive into the trade-offs, deployment options, and strategies for leveraging Flink effectively across cloud, on-premise, and edge environments, and when to use Kafka Streams or Single Message Transforms (SMT) within Kafka Connect for ETL instead of Flink.
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Data Streaming with Apache Kafka and Flink in Healthcare and Manufacturing at Siemens Healthineers
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How Siemens Healthineers Leverages Data Streaming with Apache Kafka and Flink in Manufacturing and Healthcare

Siemens Healthineers, a global leader in medical technology, delivers solutions that improve patient outcomes and empower healthcare professionals. A significant aspect of their technological prowess lies in their use of data streaming to unlock real-time insights and optimize processes. This blog post explores how Siemens Healthineers uses data streaming with Apache Kafka and Flink, their cloud-focused technology stack, and the use cases that drive tangible business value, such as real-time logistics, robotics, SAP ERP integration, AI/ML, and more.
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The Data Streaming Landscape 2025 with Kafka Flink Confluent Amazon MSK Cloudera Event Hubs and Other Platforms
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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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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 financial services, 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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One Apache Kafka Cluster Type Does NOT Fit All Use Cases
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Apache Kafka Cluster Type Deployment Strategies

Organizations start their data streaming adoption with a single Apache Kafka cluster to deploy the first use cases. The need for group-wide data governance and security but different SLAs, latency, and infrastructure requirements introduce new Kafka clusters. Multiple Kafka clusters are the norm, not an exception. Use cases include hybrid integration, aggregation, migration, and disaster recovery. This blog post explores real-world success stories and cluster strategies for different Kafka deployments across industries.
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Apache Iceberg Open Table Format for Data Lake Lakehouse Streaming wtih Kafka Flink Databricks Snowflake AWS GCP Azure
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Apache Iceberg – The Open Table Format for Lakehouse AND Data Streaming

An open table format framework like Apache Iceberg is essential in the enterprise architecture to ensure reliable data management and sharing, seamless schema evolution, efficient handling of large-scale datasets and cost-efficient storage. This blog post explores market trends, adoption of table format frameworks like Iceberg, Hudi, Paimon, Delta Lake and XTable, and the product strategy of leading vendors of data platforms such as Snowflake, Databricks (Apache Spark), Confluent (Apache Kafka / Flink), Amazon Athena and Google BigQuery.
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