Real Time Gaming with Apache Kafka Powers Dream11 Fantasy Sports
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Powering Fantasy Sports at Scale: How Dream11 Uses Apache Kafka for Real-Time Gaming

Fantasy sports has evolved into a data-driven, real-time digital industry with high stakes and massive user engagement. At the heart of this transformation is Dream11, India’s leading fantasy sports platform, which relies on Apache Kafka to deliver instant updates, seamless gameplay, and trustworthy user experiences for over 230 million fans. This blog post explores how Dream11 leverages Kafka to meet extreme traffic demands, scale infrastructure efficiently, and maintain real-time responsiveness—even during the busiest moments of live sports.
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Data Streaming Lake Warehouse and Lakehouse with Confluent Databricks Snowflake using Iceberg and Tableflow Delta Lake
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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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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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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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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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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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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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Fraud Prevention in Mobility Services with Data Streaming using Apache Kafka and Flink with AI Machine Learning
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Fraud Detection in Mobility Services (Ride-Hailing, Food Delivery) with Data Streaming using Apache Kafka and Flink

Mobility services like Uber, Grab, and FREE NOW (Lyft) rely on real-time data to power seamless trips, deliveries, and payments. But this real-time nature also opens the door to sophisticated fraud schemes—ranging from GPS spoofing to payment abuse and fake accounts. Traditional fraud detection methods fall short in speed and adaptability. By using Apache Kafka and Apache Flink, leading mobility platforms now detect and block fraud as it happens, protecting their revenue, users, and trust. This blog explores how real-time data streaming is transforming fraud prevention across the mobility industry.
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Electric Vehicle (EV) Charging - Automotive and ESG with Data Streaming at Virta
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Virta’s Electric Vehicle (EV) Charging Platform with Real-Time Data Streaming: Scalability for Large Charging Businesses

The rise of Electric Vehicles (EVs) demands a scalable, efficient charging network—but challenges like fluctuating demand, complex billing, and real-time availability updates must be addressed. Virta, a global leader in smart EV charging, is tackling these issues with real-time data streaming. By leveraging Apache Kafka and Confluent Cloud, Virta enhances energy distribution, enables predictive maintenance, and supports dynamic pricing. This approach optimizes operations, improves user experience, and drives sustainability. Discover how real-time data streaming is shaping the future of EV charging and enabling intelligent, scalable infrastructure.
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Apache Kafka 4.0 - The Business Case for Data Streaming at Enterprise Scale
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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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