#Kafka and #Snowflake are almost essential to every modern #data pipeline. What should you use for streaming large amounts of data and what is the best choice for processing? Darko Kojović shares experience with Kafka Connect in a new #Atlantbh blog 👉 https://lnkd.in/dexDXJTC
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🚀 Are you interested to know how to ingest data realtime from Kafka to Snowflake using Snowpipe Streaming. 🚀 Then read my latest medium article that describes how to do this practically step by step! 📚 I’ll walk you through following things 🔥 : — Setup local Kafka cluster(on your machine) 💻 — Configure Kafka Connector for Snowflake ⚙ — Create some basic Snowflake configurations 🛠 — Ingest sample data in realtime from kafka to Snowflake 🔄 At the end of this, you will have the ability to build your own pipelines! #RealTimeData #DataIngestion #Kafka #Snowflake #SnowpipeStreaming 👇 👇 https://lnkd.in/d_Az7xu5
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If you're asking yourself questions like, "How can we improve our company's efficiency?" or "How can we manage our large volumes of data more flexibly?" the answer lies in the Trask Event Streaming Suite. This comprehensive package of zero-cost-license accelerators is designed to address your needs, whether or not you've implemented Apache Kafka and Flink. It optimizes real-time data flows, enhances decision-making, and enables seamless system integration – all in one compact solution packed with value. 📊 Ready to elevate your business? Discover more on our website👉 https://lnkd.in/esT49sED #integration #tess #apachekafka #flink #data #eventstreaming
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https://lnkd.in/dwuasbys : Navigating the Now: Real-time Data Processing Unveiled: Strategies for Efficient Data Streaming In a world where time is of the essence, businesses are increasingly turning to real-time data processing to gain instantaneous insights and make informed decisions. This blog explores the realm of real-time data processing, shedding light on the importance of data streaming, introducing key technologies like Apache Kafka, and unveiling strategies for constructing efficient real-time data pipelines...... #RealTimeData #DataStreaming #ApacheKafka #DataInsights #EventDriven #Microservices #DataProcessing #Innovation #TechStrategies #BigData #Analytics #DigitalTransformation #BusinessIntelligence #DataInnovation #DataPipelines
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I will be attending the webinar "Empowering Nigerian Businesses with Real-Time Data: Kafka and Confluent Explained." by COLLABORATIVE TECHNOLOGY INNOVATION LIMITED and Confluent This session will focus on the challenges Nigerian businesses face with legacy systems, siloed data, and slow decision-making processes. I’m looking forward to learning how Confluent’s data streaming platform can help transform these challenges into real-time insights and a competitive advantage. Join me if you're interested in understanding how real-time data can revolutionize your business! Let’s learn together. #Kafka #Confluent #RealTimeData #DataStreaming #NigerianBusinesses #Webinar #DataDrivenDecisions
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There are a few ways Arrow can be utilized in a streaming architecture. 1. Data Transport: Using Arrow to transport data between services that can talk in columnar format. 2. Intermediate Results Sharing: Leveraging Arrow to share computed results across streaming and batch operators within a unified engine. 3. Compute State Management: Utilizing Arrow to manage compute states off local storage. It's incredible to see Zander Matheson at Bytewax pushed in a couple of these directions using Arrow! I'd love to chat more about #3; I personally believe this has the biggest long-term implication for simplifying operation and stream/batch engine unification, especially as the hardware industry advances quickly on network/disk IO and GPU acceleration!
📢 Announcing Our Latest Blog Post 📢 'Real-time Data Platform with Redpanda Data, Bytewax, Arrow, and ClickHouse.' Discover how we utilize Arrow to simplify and improve streaming workloads through efficient micro-batch compression. Inspired by Chris Comeau's innovative work, we've outlined a complete streaming architecture leveraging the power of Kafka and Bytewax. 🔍 Key Highlights: - Understanding Arrow and IPC serialization format - Bridging batch and stream data seamlessly - Exploring event-driven vs. analytical streaming patterns - Building an efficient, real-time data pipeline Check out the full blog here 🌟 https://lnkd.in/eU99WxDt
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Small, tidy, powerful iot stack
📢 Announcing Our Latest Blog Post 📢 'Real-time Data Platform with Redpanda Data, Bytewax, Arrow, and ClickHouse.' Discover how we utilize Arrow to simplify and improve streaming workloads through efficient micro-batch compression. Inspired by Chris Comeau's innovative work, we've outlined a complete streaming architecture leveraging the power of Kafka and Bytewax. 🔍 Key Highlights: - Understanding Arrow and IPC serialization format - Bridging batch and stream data seamlessly - Exploring event-driven vs. analytical streaming patterns - Building an efficient, real-time data pipeline Check out the full blog here 🌟 https://lnkd.in/eU99WxDt
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When doing data processing work with Python, the most important factor for high performance is offloading work to components written in lower level languages like C, Go and Rust. With this pattern, Python is just used as a lightweight layer connecting the dots, operating once per microbatch (each containing hundreds or thousands of rows with Arrow columnar serialization), and the heavy lifting is done by Bytewax (Rust), Arrow (C), Redpanda (C) and ClickHouse (C). All of these are able to take advantage of SIMD CPU instructions and benefit from high cache hit rates due to the high-locality vectorized sequential memory layout with Arrow. These add up to achieving extremely high throughput (in the millions of rows per second) with minimal resource consumption, while keeping things simple with Python's readability and ease of use.
📢 Announcing Our Latest Blog Post 📢 'Real-time Data Platform with Redpanda Data, Bytewax, Arrow, and ClickHouse.' Discover how we utilize Arrow to simplify and improve streaming workloads through efficient micro-batch compression. Inspired by Chris Comeau's innovative work, we've outlined a complete streaming architecture leveraging the power of Kafka and Bytewax. 🔍 Key Highlights: - Understanding Arrow and IPC serialization format - Bridging batch and stream data seamlessly - Exploring event-driven vs. analytical streaming patterns - Building an efficient, real-time data pipeline Check out the full blog here 🌟 https://lnkd.in/eU99WxDt
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📢 Announcing Our Latest Blog Post 📢 'Real-time Data Platform with Redpanda Data, Bytewax, Arrow, and ClickHouse.' Discover how we utilize Arrow to simplify and improve streaming workloads through efficient micro-batch compression. Inspired by Chris Comeau's innovative work, we've outlined a complete streaming architecture leveraging the power of Kafka and Bytewax. 🔍 Key Highlights: - Understanding Arrow and IPC serialization format - Bridging batch and stream data seamlessly - Exploring event-driven vs. analytical streaming patterns - Building an efficient, real-time data pipeline Check out the full blog here 🌟 https://lnkd.in/eU99WxDt
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𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐩𝐚𝐜𝐡𝐞 𝐊𝐚𝐟𝐤𝐚 𝐰𝐢𝐭𝐡 𝐚 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐞𝐱𝐚𝐦𝐩𝐥𝐞! 🚀 Dive into my latest post where I've tried to simplify Apache Kafka's data streaming usage, through a shopping mall analogy. From tracking customer traffic to managing inventory, see how Kafka revolutionizes data flow. Perfect for beginners looking to grasp Kafka's core concepts with ease. 🛒💻 #ApacheKafka #DataStreaming #RealWorldExample #DataEngineering
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"Apache Kafka is NOT real real-time!" => Real-time data beats slow data. It is that easy! But what is real-time? The term always needs to be defined when discussing a use case. #apachekafka is the de facto standard for real-time #datastreaming. Kafka is good enough for almost all real-time scenarios. However, dedicated proprietary software is required for some use cases. Kafka is NOT the right choice if you need microsecond latency! This blog post explores the architecture of #NASDAQ that combines critical stock exchange trading with low-latency streaming analytics. https://lnkd.in/eMGH-eYM
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