Browsing Tag

Cloud

184 posts
Confluent and Databricks for Data Integration and Stream Processing
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  • 4.8K 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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  • 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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Data Sharing for MVNO Growth and Beyond with Data Streaming in the Telco Industry
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  • 8 minute read

Real-Time Data Sharing in the Telco Industry for MVNO Growth and Beyond with Data Streaming

The telecommunications industry is transforming rapidly as Telcos expand partnerships with MVNOs, IoT platforms, and enterprise customers. Traditional batch-driven architectures can no longer meet the demands for real-time, secure, and flexible data access. This blog explores how real-time data streaming technologies like Apache Kafka and Flink, combined with hybrid cloud architectures, enable Telcos to build trusted, scalable data ecosystems. It covers the key components of a modern data sharing platform, critical use cases across the Telco value chain, and how policy-driven governance and tailored data products drive new business opportunities, operational excellence, and regulatory compliance. Mastering real-time data sharing positions Telcos to turn raw events into strategic advantage faster and more securely than ever before.
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Apache Kafka 4.0 - The Business Case for Data Streaming at Enterprise Scale
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  • 7 minute read

Apache Kafka 4.0: The Business Case for Scaling Data Streaming Enterprise-Wide

Apache Kafka 4.0 represents a major milestone in the evolution of real-time data infrastructure. Used by over 150,000 organizations worldwide, Kafka has become the de facto standard for data streaming across industries. This article focuses on the business value of Kafka 4.0, highlighting how it enables operational efficiency, faster time-to-market, and architectural flexibility across cloud, on-premise, and edge environments. Rather than detailing technical improvements, it explores Kafka’s strategic role in modern data platforms, the growing data streaming ecosystem, and how enterprises can turn event-driven architecture into competitive advantage. Kafka is no longer just infrastructure—it’s a foundation for digital business
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Shift Left Architecture at Siemens with Stream Processing using Apache Kafka and Flink
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  • 6 minute read

Shift Left Architecture at Siemens: Real-Time Innovation in Manufacturing and Logistics with Data Streaming

Industrial enterprises face increasing pressure to move faster, automate more, and adapt to constant change—without compromising reliability. Siemens Digital Industries addresses this challenge by combining real-time data streaming, modular design, and Shift Left principles to modernize manufacturing and logistics. This blog outlines how technologies like Apache Kafka, Apache Flink, and Confluent Cloud support scalable, event-driven architectures. A real-world example from Siemens’ Modular Intralogistics Platform illustrates how this approach improves data quality, system responsiveness, and operational agility.
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The Importance of Focus for Software and Cloud Vendors - Data Streaming with Apache Kafka and Flink
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  • 1.8K views
  • 15 minute read

The Importance of Focus: Why Software Vendors Should Specialize Instead of Doing Everything (Example: Data Streaming)

As real-time technologies reshape IT architectures, software vendors face a critical decision: specialize deeply in one domain or build a broad, general-purpose stack. This blog examines why a focused approach—particularly in the world of data streaming—delivers greater innovation, scalability, and reliability. It compares leading platforms and strategies, from specialized providers like Confluent to generalist cloud ecosystems, and highlights the operational risks of fragmented tools. With data streaming emerging as its own software category, enterprises need clarity, consistency, and deep expertise. In this post, we argue that specialization—not breadth—is what powers mission-critical, real-time applications at global scale.
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The Strangler Fig Design Pattern - Migration and Replacement of Legacy IT Applications with Data Streaming using Apache Kafka
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  • 8 minute read

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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Retail Media with Data Streaming using Apache Kafka and Flink
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  • 4.1K views
  • 8 minute read

Retail Media with Data Streaming: The Future of Personalized Advertising in Commerce

Retail media is reshaping digital advertising by using first-party data to deliver personalized, timely ads across online and in-store channels. As retailers build retail media networks, they unlock new revenue opportunities while improving ad effectiveness and customer engagement. The key to success lies in real-time data streaming, which enables instant targeting, automated bidding, and precise attribution. Technologies like Apache Kafka and Apache Flink make this possible, helping retailers like Albertsons enhance ad performance and maximize returns. This post explores how real-time streaming is driving the evolution of retail media
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Replacing OT Middleware with Data Streaming using Kafka and Flink for Cloud-Native Industrial IoT with MQTT and OPC-UA
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  • 3.7K views
  • 11 minute read

Modernizing OT Middleware: The Shift to Open Industrial IoT Architectures with Data Streaming

Legacy OT middleware is struggling to keep up with real-time, scalable, and cloud-native demands. As industries shift toward event-driven architectures, companies are replacing vendor-locked, polling-based systems with Apache Kafka, MQTT, and OPC-UA for seamless OT-IT integration. Kafka serves as the central event backbone, MQTT enables lightweight device communication, and OPC-UA ensures secure industrial data exchange. This approach enhances real-time processing, predictive analytics, and AI-driven automation, reducing costs and unlocking scalable, future-proof architectures.
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Learnings from the CIO Summit: AI + Data Streaming = Key for Success
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  • 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.
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