Christoph Pinkel

🚀 𝗝𝘂𝘀𝘁 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗼𝗻 𝗦𝘂𝗯𝘀𝘁𝗮𝗰𝗸 about leveraging 𝗞𝗮𝗳𝗸𝗮 Streams for 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗮𝗹𝗲𝗿𝘁𝘀! 🌦️ In this piece, I explore how 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 and 𝘀𝘁𝗿𝗲𝗮𝗺 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 can be used to efficiently handle weather data and trigger timely alerts. Perfect for those interested in 𝗲𝘃𝗲𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲, 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, and 𝗯𝗶𝗴 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 and see how we combined the power of 𝗞𝗮𝗳𝗸𝗮 𝗦𝘁𝗿𝗲𝗮𝗺𝘀 and 𝗗𝗼𝗰𝗸𝗲𝗿 to build a robust solution for weather monitoring. #KafkaStreams #RealTimeData #BigData #EventDriven #Java #Docker #WeatherAlerts #DataEngineering #CloudComputing

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I wrote a new post on the Sympathetic Ink blog to sum up The Deconstructed Database and what makes it composable. Learn more about the role of #Parquet, #Arrow, #DataFusion, Apache Iceberg, #Calcite and OpenLineage

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In this demonstration, Tom Scott tackled a real-world scenario where a retailer's transaction data was producing erroneous results due to an unusually large exchange rate for the Zimbabwean Dollar. This anomaly caused transactions with this currency to be recorded with a zero value, effectively making them invalid and impacting financial reports and operations. This anomaly caused the transaction amounts to reduce to zero, disrupting operations and creating confusion. By leveraging #indexing, we swiftly identified the root cause of the problem—the incorrect exchange rate—allowing us to rectify the issue promptly. This example highlights how indexing significantly accelerates problem-solving, enabling teams to quickly diagnose and address data anomalies without the need for unnecessary data movement and the complexity of #ETL pipelines. #Kafka #DataStreaming #Indexing #DataAnalytics #BigData #RealTimeData #ETL #DataManagement #StreamProcessing

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In a recent meeting, I was asked to justify ROI, and I'm thrilled to share that we've helped customers realize up to 350% ROI and $4.12M in net present value over 3 years through better performance, higher uptime, and lower costs. These impressive results stem from a commissioned Forrester Consulting TEI study conducted on behalf of Redis. The study delves into the costs and benefits shared by Redis Enterprise customers, highlighting their experiences and the tangible outcomes they achieved. #ROI #ForresterStudy #RedisEnterprise Watching the recording here: https://lnkd.in/dvRCnehS

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Meet Imply's Team at #AWSSummit Berlin on 15 + 16 May 2024 at STATION Berlin! 💻 🇩🇪 Learn more about the managed version of Apache Druid —a time-series data storage and analysis engine with subsecond queries across data at scale. 🐨 🧙🏼‍♂️ #ApacheKafka #ApacheDruid #eventdrivenarchitecture #streamingdata #realtimeanalytics #AWSIoTCore #cloudengineering Raj Prabagaran, MBAHellmar BeckerTom Mack

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Just a few weeks after the GA release of Arm64 runners, we’re already seeing our clients achieve noticeable reductions in build times and lower runtime costs. If you're looking to scale your CI/CD operations and accelerate feedback loops, now is the time to explore the benefits of Arm64! Key takeaways: - ⚡ Faster build times to streamline development. - 💰 Cost savings through optimized runtimes. Ready to take your CI/CD to the next level? #CI/CD #Arm64 #DevOps #BuildOptimization #CostEfficiency #ContinuousIntegration target: https://lnkd.in/gY-_n6JJ

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I just updated my first table format comparison blog post to fix some missing details of Apache Hudi. https://lnkd.in/dwSUS6qS

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When we initially built Conduktor, as Kafka engineers, our focus was to provide other developers with the most helpful tool—removing all the complexity of the CLI— and making it accessible in an elegant, simple, but powerful UI. The more Conduktor grew, the more we hit some limitations inherent to UIs: applications don't talk to a UI, they don't talk to an HTTP API, they talk to Kafka directly. Somehow related, in the most mature tech organizations, everything revolves around GitOps: it provides the core experience with all its constraints, but at least, you have control! It is often a beautiful dream, because lacking flexibility and causing much friction for the teams who need to use it. This leads to operational burdens and complexity evolving at a fast pace to answer the developers and ops needs. At Conduktor, we wanted something more flexible and easy to deploy no matter your tech maturity, because time is precious for everyone. Everyone loves simple stuff. I do. It's easy to build complex things and harder to build simple things. We enhanced Conduktor by connecting it directly to the Kafka applications, too, offering many features beyond standard Kafka capabilities (preventing misconfigurations, header mutations, easy encryption, multi-tenancy, data quality constraints, etc.) and paving the way to unify the user and application experience. While having a beautiful engine purring is great, the real value comes in making the technology accessible and collaborative for all teams, helping accelerate business objectives. Everyone, including organizations, needs guidance and a path to grow. Data management is complex to get more or less right (talk to your CDO), and there are so many paths ahead. Streaming data is complicating the landscape even more. This led us to transform Conduktor again into a streaming framework atop your data ecosystem, designed to standardize workflows and enhance team collaboration. This is the essence of Conduktor 2.0. TLDR: Get started and let me know https://www.conduktor.io TLDR2: Read more here about our new free tier and our Kafka features here https://v2.conduktor.io/

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Jack Vanlightly - wrote this - you should read it: "Kafka topics act as primitives for sharing data between systems and even between organizations. Iceberg et al have added another solid sharing primitive ideal for the analytics estate with its multitude of query engines and BI tools that can read and write to these tables. The first manifestation of this new sharing primitive is the commoditization of the data lake and the rise of the headless data architecture. With storage and compute disaggregated, each organization gets to choose the query engines/BI tools that suit their needs, putting more power into the hands of the organization to choose where they place their data and how they access it. " https://lnkd.in/dWkiFyV7

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pgvectorscale: A Game-Changer for Vector Similarity Search 🚀 Timescale's #pgvectorscale extension for PostgreSQL has achieved a remarkable milestone, matching the performance of Pinecone, a dedicated vector database, while being 75% more cost-effective. 💰 In their latest blog post, Timescale showcases the impressive speed and efficiency of pgvectorscale, which leverages the power of PostgreSQL to deliver lightning-fast vector similarity search. This breakthrough opens up new possibilities for applications like semantic search, recommendation systems, and more. 🔍 Key takeaways: 🌟 pgvectorscale now matches Pinecone's performance in vector similarity search 💸 pgvectorscale is 75% more cost-effective compared to Pinecone 🔧 Easy integration with existing PostgreSQL databases 🌐 Scalable solution for handling billions of vectors ⚡ Optimized for fast and efficient search results pgvectorscale's seamless integration with PostgreSQL allows developers to incorporate vector similarity search into their applications with ease, while benefiting from the robustness and scalability of a proven database system. 🗄️ If you're looking for a high-performance, cost-effective solution for vector similarity search, pgvectorscale is definitely worth exploring. 🔍💡 #pgvectorscale #postgresql #vectorsimilaritysearch #semanticsearch #recommendationsystems #timescale https://lnkd.in/gsfgCK32

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