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Confluent

47 posts
Data Streaming with Confluent Meets SAP and Databricks for Agentic AI at Sapphire in Madrid
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  • 6 minute read

Data Streaming Meets the SAP Ecosystem and Databricks – Insights from SAP Sapphire Madrid

SAP Sapphire 2025 in Madrid brought together global SAP users, partners, and technology leaders to showcase the future of enterprise data strategy. Key themes included SAP’s Business Data Cloud (BDC) vision, Joule for Agentic AI, and the deepening SAP-Databricks partnership. A major topic throughout the event was the increasing need for real-time integration across SAP and non-SAP systems—highlighting the critical role of event-driven architectures and data streaming platforms like Confluent. This blog shares insights on how data streaming enhances SAP ecosystems, supports AI initiatives, and enables industry-specific use cases across transactional and analytical domains.
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Enterprise Application Integration with Confliuent and Databricks for Oracle SAP Salesforce Servicenow et al
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  • 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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Shift Left Architecture with Confluent Data Streaming and Databricks Lakehouse Medallion
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  • 9 minute read

Shift Left Architecture for AI and Analytics with Confluent and Databricks

Confluent and Databricks enable a modern data architecture that unifies real-time streaming and lakehouse analytics. By combining shift-left principles with the structured layers of the Medallion Architecture, teams can improve data quality, reduce pipeline complexity, and accelerate insights for both operational and analytical workloads. Technologies like Apache Kafka, Flink, and Delta Lake form the backbone of scalable, AI-ready pipelines across cloud and hybrid environments.
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Confluent and Databricks for Data Integration and Stream Processing
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  • 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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Shift Left Architecture at Siemens with Stream Processing using Apache Kafka and Flink
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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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  • 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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Data Streaming with Apache Kafka and Flink as the Backbone for a B2B Data Marketplace
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  • 7 minute read

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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Data Streaming with Apache Kafka and Flink in Healthcare and Manufacturing at Siemens Healthineers
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  • 6 minute read

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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Data Streaming Trends for 2025 - Leading with Apache Kafka and Flink
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  • 18 minute read

Top Trends for Data Streaming with Apache Kafka and Flink in 2025

Apache Kafka and Apache Flink are leading open-source frameworks for data streaming that serve as the foundation for cloud services, enabling organizations to unlock the potential of real-time data. Over recent years, trends have shifted from batch-based data processing to real-time analytics, scalable cloud-native architectures, and improved data governance powered by these technologies. Looking ahead to 2025, the data streaming ecosystem is set to undergo even greater changes. Here are the top trends shaping the future of data streaming for businesses.
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