Redpanda Data

Big news!!! 🎊 Redpanda has acquired the popular stream processing and connectivity framework Benthos. That means with our new Redpanda Connect we're now the most comprehensive end-to-end streaming data platform! 🔗 Gone are the days of suffering through multiple projects that manually stitch data from your streams to your data warehouse or operational database. Read what our founder Alexander Gallego has to say about all that's new at Redpanda. 👇 https://lnkd.in/gpUfGYgs

📣 LAST CALL: Getting started with Redpanda Today is the day! Join our friendly pandas for a free online session all about Redpanda's architecture, deployment options, practical use cases, and best practices. If you're just entering the #streamingdata scene, then this is the best welcome pack to our...pack 🐾 🗓 Wednesday, July 10th 🌎 Americas: 9 am PT | 12 pm ET 🌍 EMEA: 8 am ET | 1 pm BST You're still in time to join! Sign up here👇 https://lnkd.in/gjd75ZgJ

Want to build a 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗠𝗟 𝘀𝘆𝘀𝘁𝗲𝗺, that 𝘀𝗰𝗮𝗹𝗲𝘀, without 𝗯𝘂𝗿𝗻𝗶𝗻𝗴 𝗰𝗮𝘀𝗵 ? Here is the way ↓↓↓ 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 🤔 Let’s say you work as an ML engineer at a fintech startup, whose flagship product is a mobile app for online payments. The company is still small, but nonetheless a critical problem you need to tackle from day 0 is the automatic detection of fraudulent transactions. The system needs to be > 𝗙𝗮𝘀𝘁, otherwise, you will detect fraud too late, and the predictions, no matter how precise they are, will be useless. > 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 based on the volume of transactions your users generate per second. You aim to become a serious contender to VISA or MasterCard, so you want a design that can scale to over 65,000 transactions per second. > 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁. You don’t want high upfront costs 💸 and complex infrastructure to mange (well, who does? 😛) So the question is > How can you build a 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 and 𝗰𝗼𝘀𝘁-𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 system to detect fraud❓ 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 🧠 To make your system capable of → ingesting transactions, and → producing fraud notifications to end-users at scale and fast you can use a streaming data platform (aka message bus), like Apache Kafka or, even better, Redpanda Data. 𝗪𝗵𝗮𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆? 🎛️ The scalability of the system depends on 2 things: > Number of brokers in your message bus and the resources you allocate to them (CPU, memory…). > Number of Docker instances you run of your Fraud Detector Service. Horizontal scaling 𝗪𝗵𝗮𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝘀𝘁𝘀? 💸 To run this system you need at least 2 things: > A 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺, where your Fraud Detector Service runs. For example, Amazon EKS, Google Cloud GKE or Quix Cloud, and > A 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺, like Apache Kafka or Redpanda. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 𝗼𝗿 𝗥𝗲𝗱𝗽𝗮𝗻𝗱𝗮 ❓ Apache Kafka is built in Java and requires a complex mesh of services to run, while Redpanda Data is designed from the ground up in C++ and shipped as a single binary. Which means it is easier and cheaper to run. 🤑 𝗠𝘆 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 💡 If you want to start building real-time ML systems, without investing a lot in upfront infrastructure costs, I recommend you try 𝗥𝗲𝗱𝗽𝗮𝗻𝗱𝗮 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀. → With a 𝗳𝗲𝘄 𝘀𝗲𝗰𝗼𝗻𝗱𝘀 you get your production cluster up and running 🚀 → You 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗮𝗻𝘆 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 🎉, and → You 𝗼𝗻𝗹𝘆 𝗽𝗮𝘆 𝗳𝗼𝗿 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝘂𝘀𝗲, so you go from 0 USD and scale based on your growth 🤑 Click here to try it for FREE ↓↓↓ https://lnkd.in/eSd-P4Es ---- Hi there! It's Pau Labarta Bajo 👋 Every day I share free, hands-on content, on production-grade ML, to help you build real-world ML products. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 and 𝗰𝗹𝗶𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 🔔 so you don't miss what's coming next #machinelearning #realtimeml #streaming

#PyTorch and #TensorFlow are two of the most popular frameworks in the #ML community because of their flexibility, extensive libraries, and active support. But which one is best suited for your #streamingdata use case? This post digs into how each framework supports building streaming data apps, their compatibility with #Kafka, and how to make your job easier by integrating with Redpanda 🐾 Read it here👇 https://lnkd.in/gwC8STnC

📣 VIRTUAL EVENT JULY 17: Emergence of the Live Data Stack Get the scoop on the next-gen technologies powering data-intensive applications in our live online event hosted by Joe Reis, co-author of The Fundamentals of Data Engineering, and our Panda-in-Chief Alexander Gallego Gallego! WHAT YOU'LL LEARN 👜 Business drivers for real-time systems 👨💻 Technology transitions required for a live #data stack 🚀 Common #deployment patterns 💪 Skills and expertise to support #realtime systems 🤩 Real-world examples of the live data stack in action WHEN AND WHERE 🗓 Wednesday, July 17th 🕘 9 am PDT | 12 pm EDT | 5 pm BST 📍 Wherever there's Wi-Fi (it's online) HOW TO JOIN Simple! Just sign up here to book your spot👇 https://lnkd.in/gEE-N6jK

With the rise of #SaaS and self-serve #data platforms, organizations want to equip their employees with the fresh insights they need to make data-driven decisions faster. However, building a user-facing #analytics solution that provides these swift insights is easier said than done. To give you a hand, Dunith Danushka cooked up a helpful blueprint for implementing a simple, scalable user-facing analytics solution 🚀 Read it here👇 https://lnkd.in/gTHEVGwR

This is the more detail version of the demo I did during the Redpanda Data launch stream, where I created a smart robot using a #RaspberryPi 4! This is how I turned this resource-limited device into a powerful AI-driven robot capable of answering questions in real-time using redpanda #connect 🔧Highlights: - Integrating Redpanda and Redpanda Connect for efficient data handling - Speech-to-Text conversion - Dynamic topic assignment for query processing - Text-to-Speech playback of responses - Utilizing OpenAI for natural language processing WDYT about the demo? Let me know! #kafka #kafkaconnect #ai #openai #rag #edge #iot

Modern apps require modern platforms—so sticking to traditional #tech will inevitably leave you behind. 
 If you're struggling to get with the times, come meet Redpanda for a fantastically simple (yet incredibly powerful) #streamingdata engine that'll end your battle with #batch for good 👉 https://lnkd.in/e_8fnf6C     #StreamingDataDoodles #kafka #realtime #data

🗣️ Don't miss this short interview with Michał Maślanka at #JOTB24. Michał is responsible for Redpanda Data's Raft implementation and cluster orchestration bits. #softwaredevelopment #distributedsystems #Raft 👉 https://lnkd.in/dTvbzKdw

🎵 Don't stop me now I'm having such a good time, I'm having a ball Don't stop me now 🎵 Once you get started with Redpanda, you won't want to stop at all! Join our free online session on "Getting started with Redpanda" to learn about Redpanda's architecture, deployment options, practical use cases, and best practices. 🗓 Wednesday, July 10th 🌎 Americas: 9 am PT | 12 pm ET 🌍 EMEA: 8 am ET | 1 pm BST Sign up to join👇 https://lnkd.in/gjd75ZgJ