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Data Streaming

58 posts
Snowflake and Apache Kafka Data Integration Anti Patterns Zero Reverse ETL
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  • 3.8K views
  • 9 minute read

Snowflake Integration Patterns: Zero ETL and Reverse ETL vs. Apache Kafka

Snowflake is a leading cloud-native data warehouse. Integration patterns include batch data integration, Zero ETL and near real-time data ingestion with Apache Kafka. This blog post explores the different approaches and discovers its trade-offs. Following industry recommendations, it is suggested to avoid anti-patterns like Reverse ETL and instead use data streaming to enhance the flexibility, scalability, and maintainability of enterprise architecture.
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Google Apache Kafka for BigQuery GCP Cloud Service
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  • 9.1K views
  • 8 minute read

When (Not) to Choose Google Managed Service for Apache Kafka?

Google announced its Apache Kafka for BigQuery cloud service at its conference Google Cloud Next 2024 in Las Vegas. Welcome to the data streaming club joining Amazon, Microsoft, IBM, Oracle, Confluent, and others. This blog post explores this new managed Kafka offering for GCP, reviews the current status of the data streaming landscape, and shares some criteria to evaluate when Kafka in general and Google Apache Kafka in particular should (not) be used.
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When NOT to use Apache Kafka
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  • 3.4K views
  • 3 minute read

When NOT to Use Apache Kafka? (Lightboard Video)

Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This blog post contains a lightboard video that gives you a twenty-minute explanation of the DOs and DONTs.
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ESG and Sustainability powered by Data Streaming with Apache Kafka and Flink
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  • 5.9K views
  • 13 minute read

Green Data, Clean Insights: How Kafka and Flink Power ESG Transformations

This blog post explores the synergy between Environmental, Social, and Governance (ESG) principles and Kafka and Flink’s real-time data processing capabilities, unveiling a powerful alliance that transforms intentions into impactful change. Beyond just buzzwords, real-world deployments architectures across industries show the value of data streaming for better ESG ratings.
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SAP Datasphere and Apache Kafka as Data Fabric for ERP Integration
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  • 5.7K views
  • 12 minute read

SAP Datasphere and Apache Kafka as Data Fabric for S/4HANA ERP Integration

SAP is the leading ERP solution across industries around the world. Data integration with other data platforms, applications, databases, and APIs is one of the hardest challenges in the IT and software landscape. This blog post explores how SAP Datasphere in conjunction with the data streaming platform Apache Kafka enables a reliable, scalable and open data fabric for connecting SAP business objects of ECC and S/4HANA ERP with other real-time, batch, or request-response interfaces.
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Data Streaming Landscape 2024 around Kafka Flink and Cloud
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  • 10.4K views
  • 21 minute read

The Data Streaming Landscape 2024

The research company Forrester defines data streaming platforms as a new software category in a new Forrester Wave. Apache Kafka is the de facto standard used by over 100,000 organizations. Plenty of vendors offer Kafka platforms and cloud services. Many complementary open source stream processing frameworks like Apache Flink and related cloud offerings emerged. And competitive technologies like Pulsar, Redpanda, or WarpStream try to get market share leveraging the Kafka protocol. This blog post explores the data streaming landscape of 2024 to summarize existing solutions and market trends. The end of the article gives an outlook to potential new entrants in 2025.
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Tiered Storage for Apache Kafka - Use Cases Architecture Benefits.png
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  • 8.6K views
  • 11 minute read

Why Tiered Storage for Apache Kafka is a BIG THING…

Apache Kafka added Tiered Storage to separate compute and storage. The capability enables more scalable, reliable and cost-efficient enterprise architectures. This blog post explores the architecture, use cases, benefits, and a case study for storing Petabytes of data in the Kafka commit log. The end discusses why Tiered Storage does NOT replace other databases and how Apache Iceberg might change future Kafka architectures even more.
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  • 7.5K views
  • 10 minute read

Top 5 Trends for Data Streaming with Kafka and Flink in 2024

Do you wonder about my predicted TOP 5 data streaming trends with Apache Kafka and Flink in 2024 to set data in motion? Discover new technology trends and best practices for event-driven architectures, including data sharing, data contracts, serverless stream processing, multi-cloud architectures, and GenAI.
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Dish Wireless Cloud-native 5G Telco Network powered by Data Streaming with Apache Kafka
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  • 2.7K views
  • 8 minute read

How Apache Kafka helps Dish Wireless building cloud-native 5G Telco Infrastructure

5G telco infrastructure provides the basic foundations of data movement and increasingly unlocks new capabilities for low latency and critical SLAs. Real-time data processing with data streaming using Apache Kafka enables innovation across industries. This blog post explores the success story of Dish Wireless and its cloud-native standalone 5G infrastructure leveraging data streaming.
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  • 3.6K views
  • 6 minute read

How Michelin improves Aftermarket Sales and Customer Service with Data Streaming

Aftermarket sales and customer service require the right information at the right time to make context-specific decisions. This post explains the modernization of supply chain business process choreography based on the real-life use case of Michelin, a tire manufacturer in France. Data Streaming with Apache Kafka enables true decoupling, domain-driven design, and data consistency across real-time and batch systems. Common business goals drove them: Increase customer retention, increase revenue, reduce costs, and improve time to market for innovation.
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