Browsing Tag

Cloud

178 posts
Data Streaming Lake Warehouse and Lakehouse with Confluent Databricks Snowflake using Iceberg and Tableflow Delta Lake
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  • 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.
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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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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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  • 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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