Robin Seitz
San Francisco, California, United States
5K followers
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I'm a software engineer who's been working in the industry for about 8 years now. I love…
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Clemens Vasters
I like the consistency and simplicity and extensibility of the Apache #Avro schema model. I also think that the JSON encoding defined in the Apache Avro specification is a comical mistake. It's so unintuitive, that none of you would be able to guess how to write a conformant JSON document reading an Avro schema. The JSON document you would quite likely write intuitively instead would rather conform to this alternate JSON encoding for Apache Avro schemas: "Plain JSON" https://lnkd.in/eEHPbJrj
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Asanká Botheju
👉 Amazon OpenSearch Ingestion adds support for ingesting data from self-managed sources Amazon OpenSearch Ingestion now allows you to ingest data from self-managed OpenSearch, Elasticsearch and Apache Kafka clusters, eliminating the need to run and manage 3rd party tools like Logstash to migrate your data from self-managed sources into Amazon OpenSearch Service. Now you can seamlessly migrate or continuously replicate your data from all OpenSearch versions and Elasticsearch 7.x versions either on Amazon EC2 or on-premises environments into Amazon OpenSearch Service managed clusters or Serverless collections. #aws #amazonopensearch #seamlessingestion
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Shiv Iyer
Materialized views in #PostgreSQL store pre-computed results of frequent, complex queries to boost performance. They are periodically refreshed for up-to-date data but require careful management of refresh frequency, disk space, and resources. #PostgreSQLDBA @MinervaDB https://zurl.co/7ixU
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Anthony Giza
Increase Model Flexibility and ROI for GenAI App Delivery With Kubernetes https://buff.ly/4e7WlZW As with past technology adoption journeys, initial experimentation costs eventually shift to a focus on ROI. In a recent post on X, Andrew Ng extensively discussed GenAI model pricing reductions. This is great news, since GenAI models are crucial for powering the latest generation of AI applications. However, model swapping is also emerging as both an innovation enabler, and a cost saving strategy, for deploying these applications. Even if you've already standardized on a specific model for your applications with reasonable costs, you might want to explore the added benefits of a multiple model approach facilitated by Kubernetes. A Multiple Model Approach to GenAI A multiple model operating approach enables developers to use the most up-to-date GenAI models throughout the lifecycle of an application. By operating in a continuous upgrade approach for GenAI models, developers can harness the specific strengths of each model as they shift over time. In addition, the introduction of specialized, or purpose-built models, enables applications to be tested and refined for optimal accuracy, performance and cost.
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Shiv Iyer
Configuring #InnoDB I/O threads in #MySQL 8 is key to optimizing performance, especially on multi-core servers with high I/O capacity. These threads manage tasks like flushing dirty pages and merging insert buffers, improving database efficiency.#MySQLPerformance @MinervaDB #dba https://zurl.co/wDoN
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Rubi H.
💡 Reducing I/O in PostgreSQL with WAL Compression 💡 ⁉️ Did you know you can optimize disk I/O in PostgreSQL by enabling WAL compression? Write-Ahead Logging (WAL) is critical for PostgreSQL durability, but frequent writes can cause high disk I/O, especially in write-heavy workloads. By compressing WAL files (wal_compression=on), you: ✅ Reduce Disk I/O: Smaller WAL files mean fewer writes to storage. ✅ Save Storage: Minimize space usage for archived WALs. ✅ Improve Write Throughput: Less I/O pressure allows smoother operations. 📌 Even in read-heavy workloads, enabling WAL compression can reduce I/O during infrequent writes without impacting read performance. Just ensure you have adequate CPU availlable for the minimal compression overhead. Compression requires extra CPU, which may not suit all workloads. 💡 Pro Tip: Combine WAL compression with checkpoint tuning (max_wal_size, checkpoint_timeout) for even better performance! 💡 When should you enable it? If you have a write-heavy workload or need to optimize storage and I/O, enable WAL compression!!! #PostgreSQL #CloudSQL #DatabaseOptimization #WriteAheadLogging
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Yaar Reuveni
Excellent talk by our Daniel Balosh on give a well thought recepie to thoroughly go from a tech debt situation with an overwhelmed MySQL DB and reuduce it’s CPU usage by 70% in just three weeks!! Also we at Eleos Health are #hiring - #dataplatform and #fullstack #teamlead #qa #dsci #dataengineering - DM me Alon Alon Dror Merav Liat Tal Elad Sapir Samuel Amit Lidor Yacov Andrew Almog Michelle Raz Naomi Lior Or Lior Yair
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Mark Teehan
The Kafka Connect file-chunk connectors (now available on Confluent Hub) can be used to stream files (of any size) through a Kafka topic. Stream audio, video, images, documents - content of any type. This article explains why (and when) file-streaming may be needed; and the original purpose of the connectors.
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Madhur Prashant
Interested in integrating SageMaker Processing jobs with PrestoDB for efficient batch transforms and frequent model retraining 🚀 ? Releasing a new AWS blog with Amit Arora, Johnny Chivers and Antara Raisa containing an end to end an open source solution that uses Amazon SageMaker Pipelines and PrestoDB. This blog dives into an end-to-end workflow that seamlessly connects SageMaker Processing jobs to your PrestoDB data sources, enabling optimized batch inference and scheduled model retraining. From data fetching and model training to batch transforms and real-time deployment, you can now use this approach for a variety of use cases all through minimal/no-code changes! Do check it out and let me know if you have any questions/feedback. Blog link: https://lnkd.in/evsgxt3P Open-Source GitHub Repository: https://lnkd.in/e8nsWvGN #AWS #Machinelearning #AmazonSageMaker #MLOps
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Ketan U.
Recently Haytham Abuelfutuh presented about Batch Workloads and how Union and Flyte are used for Large scale batch and near realtime workloads. Check out this post to 1. Learn about the challenges in using Kubernetes for running Batch workloads 2. The problems in the programming model with Batch workloads. Often times its not a single job - but it may start with one 3. The simplification that can be introduced by using Union. Union runs them across multiple clouds, multiple kubernetes clusters, securely accessing large amounts of data, while maintaining efficiency through - caching, smart techniques in which container state is saved, how pods are used, how back pressure is maintained etc. Check out the blog.
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Gaurav Kakad
🚀 Unleash the Power of LlamaIndex on Google Cloud ! 🚀 New article on deploying LlamaIndex agentic RAG workflows to Cloud Run using Llama Deploy. Learn how to: - Build complex RAG applications with ease - Leverage the flexibility of LlamaIndex workflows - Streamline deployment with Llama Deploy - Scale your LLM apps on Google Cloud Cloud Run Check it out ! 🔗 [https://lnkd.in/dvXbEMnz] #LLM #LlamaIndex #CloudRun #GoogleCloud #RAG #Deployment #Workflows #LlamaDeploy
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Pradeep Sharma
#OpenSourceDiscovery 95 Today I discovered: ScyllaDB Real-time NoSQL data store for big data apps ScyllaDB is a distributed NoSQL database that leverages C++ implementation (using Seastar framework) and a shard-per-core architecture to deliver max. hardware utilization with higher throughput and lower latencies 🚀Demo: https://www.scylladb.com/ 👨💻 Source: https://lnkd.in/gTdb3w6m 🛠 Stack: C++ 👑 Author: ScyllaDB team - Avi Kivity, Tomasz Grabiec, Botond Dénes, Pavel Emelyanov, Benny Halevy, Nadav Har'El, Asias He, Kefu Chai, Raphael Raph Carvalho, Paweł Dziepak, Piotr Sarna, Pekka Enberg #opensource #database #nosql #performance #cassandra #alternative
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Shiv Iyer
Configuring #InnoDB I/O threads in #MySQL 8 is key to optimizing performance, especially on multi-core servers with high I/O capacity. These threads manage tasks like flushing dirty pages and merging insert buffers, improving database efficiency.#MySQLPerformance @MinervaDB #dba https://zurl.co/wDoN
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Ziaul Kamal
With WarpStream, Confluent Got a New Type of Kafka Platform https://lnkd.in/gfTxqiYr The Mountain View California-based company Confluent offers the Kafka streaming software on an enterprise platform. It also offers a version for the cloud, Confluent Cloud. And now it has a third way to deliver Kafka, thanks to the recently acquired WarpStream earlier last month. Considering the buzz at the company’s annual Current user conference, held in Austin, this buy was anything but the usual routine acquisition of a potential competitor. WarpStream developed its own real-time data streaming, “Kafka-compatible” distribution, based loosely on — Buzzword Alert! — the Bring-Your-Own-Cloud (BYOC) model, though the WarpStream’s founders insist its not standard BYOC. Confluent, as always, is bullish on streaming data, and at this year’s conference, they reinforced the idea that most IT systems will eventually move from a batch-processing mindset to that of analyzing and processing stream data. The company’s latest data streaming report shows that 91% of IT leaders are planning on moving their organizations more towards data streaming. WarpStream fits a particular niche; it is perfect for logging, observability, and feeding data lakes, all cases where a little bit of latency is an acceptable tradeoff for large cloud cost savings, Confluent execs noted. “Our goal is to make data streaming the central nervous system of every company, and to do that we need to make it something that is a great fit for a vast array of use cases and companies,” noted Jay Kreps, CEO and co-founder of Confluent, in a company statement of the acquisition. WarpStream Ahead WarpStream’s approach provides a set of containers to the customers with a fully scalable Kafka deployment, along with supporting tools. It is managed under what is called a “shared responsibility” model of cloud computing, where both the customer and vendor share in the responsibility of maintaining the application. Unlike other BYOC approaches, however, it is up to the customer, not the service provider, to scale to the needed level of service. But that can be done automatically. In a press conference at the conference, WarpStream co-creator Ryan Worl said he did not feel comfortable with a business model where the vendor could scale up its operations on the customer’s behalf. So that part is left to the customer. “The thing we realized is that we could avoid a lot of the problems with the traditional BYOC model by re-architecting the underlying system in a different way,” explained WarpStream confounder Richard Artoul during the Current keynote. In this setup, the control plane and data plane are split, with WarpStream managing the control plane from its own cloud account, which manage the transactions, consumer groups, metadata and consensus. The customer manages the data plane, which is a set of containerized agents, each basically the equivalent of a stateless Kafka broker, handling...
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