Traditional Kafka consumers for analytics = expensive, complex, and fragile. Replay, deduplication, schema drift… it’s a lot. On Oct 9, we’ll show how Estuary + MotherDuck simplify all of this with a fresh approach to streaming ingestion. 🔗 https://lnkd.in/dncWFDGm
How Estuary + MotherDuck simplify streaming ingestion
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We just released a MAJOR new feature for Atlas Stream Processing that will enable a lot of very impactful stream processing use cases that rely on lookup tables and other contextual data to perform in flight processing of data. Now with $cachedLookup you can reserve memory to keep a persistent in-memory copy of contextual data without having to query the source system on each lookup!
Tired of remote queries slowing down your streaming pipelines? With $𝐜𝐚𝐜𝐡𝐞𝐝𝐋𝐨𝐨𝐤𝐮𝐩 operator in 𝐀𝐭𝐥𝐚𝐬 𝐒𝐭𝐫𝐞𝐚𝐦 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠, you can boost performance when enriching event streams with static or slow-changing data! It uses an in-memory TTL cache, great for things like product catalogs or user metadata. https://lnkd.in/gZzPK6VF
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𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗲𝘀𝘁𝗲𝗿𝗱𝗮𝘆’𝘀 𝗱𝗮𝘁𝗮 𝗶𝘀 𝘆𝗲𝘀𝘁𝗲𝗿𝗱𝗮𝘆’𝘀 𝗻𝗲𝘄𝘀. In 2025, insights must arrive as the data arrives. Streaming, edge, in-memory analytics are turning latency into legacy.
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Streaming transforms how we interact with Foundation Models, shifting from static responses to dynamic, real-time experiences. By leveraging the streamResponse API, we can progressively display model output as it’s generated. https://lnkd.in/edpNudX9
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Check out the first case study for Aiven Diskless Kafka - OpsHelm, Inc. saved 78% on their annual Kafka bill by moving from MSK to Aiven Diskless for their streaming workloads. Check out the full case study on the Aiven blog via the link in the comments
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JSON-RPC is simple, lightweight, and easy to use. It has great library support across many languages. However, it lacks features like streaming, protobufs, and code generation. gRPC provides better performance through HTTP/2, protobuf serialization, bi-directional streaming, and code generation.
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Modern databases excel at analysis when data changes infrequently. For rapidly changing data, many custom "streaming" or "event processing" systems have been built. To offer near-real time answers, streaming systems compromise on the expressiveness of the computations they perform. 🎬 https://buff.ly/msAvQLe
Mihai Budiu - Unifying databases and streaming systems - EventCentric 2025
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🔍🤖 Enterprise Deep Research A multi-agent system leveraging LangGraph to power enterprise research automation. Features real-time streaming, human-guided steering, and flexible deployment through web and Slack interfaces. Explore EDR on GitHub 📚 https://lnkd.in/ejXppGvf
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Machine Learning algorithms constantly adapt to your evolving preferences, powering the personalized recommendations you encounter daily on streaming services and social media.
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You don’t have to be in tech to feel the pain of batch data. When decisions depend on yesterday’s report, everyone pays the price. Let's break down the early signs that your company may be ready for real-time data streaming. #DigitalTransformation #RealTimeData #DataStreaming
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💡 Data moves fast — your insights should too. From insight to action — instantly. Discover how streaming data enables real-time analysis, smarter actions, and instant decisions. 👉🏻 Swipe to learn about streaming data. Check our blog to find out more on streaming data: https://lnkd.in/dmWWkf8W #Data #Streamingdata #Bigdata #Dataintelligence #Riskmanagement #Antifraud #Frauddetection #Fraudprevention
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