Google Apache Kafka for BigQuery GCP Cloud Service
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When (Not) to Choose Google Apache Kafka for BigQuery?

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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Streaming Analytics SQL API with Apache Kafka Confluent ClickHouse Tinybird
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Apache Kafka and Tinybird (ClickHouse) for Streaming Analytics HTTP APIs

Apache Kafka became the de facto standard for data streaming. However, the combination of an event-driven architecture with request-response APIs is crucial for most enterprise architectures. This blog post explores how Tinybird innovates with a REST/HTTP layer on top of the open source analytics database ClickHouse in the cloud. Integrating Kafka with Tinybird, the benefits of fully managed services like Confluent Cloud, and customer stories from Factorial and FanDuel show why Kafka and analytics databases complement each other for more innovation and faster time-to-market.
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When NOT to use Apache Kafka
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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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The Past Present and Future of Stream Processing
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The Past, Present and Future of Stream Processing

Stream processing has existed for decades. The adoption grows with open source frameworks like Apache Kafka and Flink in combination with fully managed cloud services. This blog post explores the past, present and future of stream processing, including the relation of machine learning and GenAI, streaming databases, and the integration between data streaming and data lakes with Apache Iceberg.
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JavaScript Node JS Apache Kafka for Full Stack Data Streaming in Event Driven Architecture
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JavaScript, Node.js and Apache Kafka for Full-Stack Data Streaming

JavaScript is a pivotal technology for web applications. With the emergence of Node.js, JavaScript became relevant for both client-side and server-side development, enabling a full-stack development approach with a single programming language. Both Node.js and Apache Kafka are built around event-driven architectures, making them naturally compatible for real-time data streaming. This blog post explores open-source JavaScript Clients for Apache Kafka and discusses the trade-offs and limitations of JavaScript Kafka producers and consumers compared to stream processing technologies such as Kafka Streams or Apache Flink.
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Data Streaming with Apache Kafka and ARM CPU at the Edge and in the Cloud
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ARM CPU for Cost-Effective Apache Kafka at the Edge and Cloud

ARM CPUs often outperform x86 CPUs in scenarios requiring high energy efficiency and lower power consumption. These characteristics make ARM preferred for edge and cloud environments. This blog post discusses the benefits of using Apache Kafka alongside ARM CPUs for real-time data processing in edge and hybrid cloud setups, highlighting energy-efficiency, cost-effectiveness, and versatility. A wide range of use cases are explored across industries, including manufacturing, retail, smart cities and telco.
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ESG and Sustainability powered by Data Streaming with Apache Kafka and Flink
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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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GenAI Demo with Kafka, Flink, LangChain and OpenAI
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GenAI Demo with Kafka, Flink, LangChain and OpenAI

Generative AI (GenAI) enables automation and innovation across industries. This blog post explores a simple but powerful architecture and demo for the combination of Python, and LangChain with OpenAI LLM, Apache Kafka for event streaming and data integration, and Apache Flink for stream processing. The use case shows how data streaming and GenAI help to correlate data from Salesforce CRM, searching for lead information in public datasets like Google and LinkedIn, and recommending ice-breaker conversations for sales reps.
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How Intersport uses Apache Kafka as Database with Compacted Topic in Retail
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How the Retailer Intersport uses Apache Kafka as Database with Compacted Topic

Compacted Topic is a feature of Apache Kafka to persist and query the latest up-to-date event of a Kafka Topic. The log compaction and key/value search is simple, cost-efficient and scalable. This blog post shows in a success story of Intersport how some use cases store data long term in Kafka with no other database. The retailer requires accurate stock info across the supply chain, including the point of sale (POS) in all international stores.
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Real Time Customer Loyalty and Reward Platform with Apache Kafka
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Customer Loyalty and Rewards Platform with Apache Kafka

Loyalty and rewards platforms are crucial for customer retention and revenue growth for many enterprises across industries. Apache Kafka provides context-specific real-time data and consistency across all applications and databases for a modern and flexible enterprise architecture. This blog post looks at case studies from Albertsons (retail), Globe Telecom (telco), Virgin Australia (aviation), Disney+ Hotstar (sports and gaming), and Porsche (automotive) to explain the value of data streaming for improving the customer loyalty.
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