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Webinar: Redis Alternatives Compared 😎 🔗 https://lnkd.in/ebNTKA3z Peter Zaitsev will provide analysis of: 🔸 Emerging Redis forks like Valley and Redix 🔸 Established contenders like DragonflyDB and KeyDB 🔸 Unique options like Microsoft Garnet and Redka Also, this deep dive covers: 🔸 Licensing models and implications 🔸 Feature sets and functionality comparisons 🔸 Governance and community support structures 🔸 Performance considerations for various use cases

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Want to attend the conference and enhance your PostgreSQL knowledge at the same time? In addition to conference talks, you can attend full- or half-day world-class PostgreSQL training sessions carefully selected by our Programme Committee! These sessions are held on Tuesday, ahead of the conference, and can be purchased as part of your registration. The number of places is limited. - Mastering PostgreSQL in Kubernetes with CloudNativePG - Hands-on Introduction to PostgreSQL Operation - Logical replication - getting in and out of the cloud - A beginner's guide to performance - Are you collecting the right metrics? - Empowering AI Applications: Integrating Vector Databases with PostgreSQL Register: https://lnkd.in/dQKcT5eF #postgresql #postgres #training #workshop

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New blog post on understanding Apache Hudi's consistency model using TLA+, with special focus on multi-writer scenarios. https://lnkd.in/dnMHWRv9

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Wondering how Kafka compares to message brokers like ActiveMQ and RabbitMQ? Watch this video featuring some of our open source experts explaining the key differences and use cases >> https://ter.li/egv410 #apachekafka #activemq #rabbitmq #opensource

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Wondering how Kafka compares to message brokers like ActiveMQ and RabbitMQ? Watch this video featuring some of our open source experts explaining the key differences and use cases >> https://ter.li/egv410 #apachekafka #activemq #rabbitmq #opensource

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We were excited and intrigued by the TableFlow announcement by Confluent at the recent Kafka Summit London. It's great to see the Streaming Datalake concept getting the traction it deserves! In this latest blog post we highlight some of the considerations that come with the convergence of operational and analytical data systems and how the Streambased and TableFlow approaches differ, including: ⚙ Should you move/transform data for analytical purposes? ⏳ What (if any) latency is acceptable between operational and analytical systems? 📊 How should we surface operational data for analytical purposes? Please enjoy and, as always, join the discussion! https://lnkd.in/eMbgKn-f

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New video - database Interaction in #Scala with Skunk: A comprehensive guide to setting up and integrating a PostgreSQL database into your purely functional Scala backend, including - library integration - configuration - using single and pooled sessions - integrating domain with codecs - Skunk commands and queries - running CRUD operations - repositories and best practice https://lnkd.in/d_2T38hs

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PostgreSQL and pgvector: Now Faster than Pinecone, 75% cheaper, 100% open-source. Introducing pgvectorscale, an open-source PostgreSQL extension that builds on pgvector, enabling greater performance and scalability. Here’s how pgvectorscale helps pgvector outperform specialized vector database like Pinecone: 1️⃣ StreamingDiskANN:  A new vector search index that overcomes limitations of in-memory indexes like HNSW the index on disk, making it more cost-efficient to run and scale as vector workloads grow. Inspired by the DiskANN paper from Microsoft. 2️⃣ Statistical Binary Quantization (SBQ): Developed by researchers at Timescale, this technique improves on standard binary quantization techniques by improving accuracy when using quantization to reduce the space needed for vector storage 3️⃣ Written in Rust, giving the PostgreSQL community to contribute to vector support. 📈The result? On our benchmark of 50 million Cohere embeddings (768 dimensions each), PostgreSQL with pgvector and pgvectorscale achieves 28x lower p95 latency and 16x higher query throughput compared to Pinecone for approximate nearest neighbor queries at 99 % recall, all at 75 % less cost when self-hosted on AWS EC2. We also tested it against Pinecone’s p2 high performance index, see the blog post at the end of this post for full results (spoiler: It’s just as impressive). Pgvectorscale is open-source under the PostgreSQL license and free for you to use on any PostgreSQL database for your AI projects. To get started, see the pgvectorscale github repo: https://lnkd.in/ghXj2e-U Or try it on Timescale Cloud on any new database service. Eager to learn more about pgvectorscale and how it works? Head over to our blog post with all the details: https://lnkd.in/gcMcxrVb

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Durable Workflows with Effect Cluster At Effect Days 2024, Mattia Manzati discussed the concept of durable workflows using Effect Cluster, which helps handle failures and retries in distributed systems, and provides building blocks for handling activities and workflows. Highlights from the talk: → Durable workflows with Effect Cluster help handle failures and retries in distributed systems. → Activities in Effect Cluster can perform various tasks such as database operations and HTTP calls. → Persistence in Effect Cluster is pluggable, allowing different storage options. Link to full presentation: https://lnkd.in/d9427Xcz

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“Just Use Postgres For Everything” It feels like it’s everywhere at the moment, the recent surge from developers isn’t just a battle cry; it’s a revolution. People often talk about “how” to use PostgreSQL for everything, but no one has actually covered the “why.” Until now. Why is this mantra being widely adopted? Why would I want a single database? Why does this affect the future of data? 🐯 Ajay Kulkarni tackles these questions head-on in our latest blog. 💪 https://lnkd.in/g-6Xw5im

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YugabyteDB’s automatic sharding overcomes PostgreSQL’s partition key limitations by allowing date range queries without modifying table partitioning.💡 Check out the new blog from Yugabyte expert Franck Pachot for more information, and step-by-step instructions!⬇️ https://hubs.la/Q02s_wKx0 #Yugabytedb #postgresql #sharding #partitionkey #cloudnative #partitioning

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Use AWS Generative AI CDK Constructs To Speed up App Development https://buff.ly/3xIbVwm In this blog, we will use the AWS Generative AI Constructs Library to deploy a complete RAG application composed of the following components: Knowledge Bases for Amazon Bedrock: This is the foundation for the RAG solution. OpenSearch Serverless collection: It supports the vector search collection type that provides similarity search capability. An S3 bucket: This will act as the data source for the Knowledge Base. AWS Lambda function (written in Python) along with an API Gateway that uses the RetrieveAndGenerate API to query the knowledge base and generate responses from the information it retrieves.

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Building Event-Driven Architectures with Apache Kafka In today’s fast-paced digital landscape, real-time data processing is crucial for modern applications. At TrueulyMarket, we leverage Apache Kafka to build robust and scalable event-driven architectures that enhance the performance and reliability of our applications. Apache Kafka is a powerful stream-processing platform that enables us to handle real-time data feeds efficiently. Here’s how we utilize Kafka to create event-driven architectures: 1. Real-Time Data Processing: Kafka allows us to process and react to data in real-time, ensuring that our applications can handle live updates and events seamlessly. 2. Scalability: With Kafka’s distributed architecture, we can scale our applications horizontally to handle increased data volumes and user loads. 3. Reliability: Kafka’s fault-tolerant design ensures high availability and reliability, making it an ideal choice for mission-critical applications. 4. Decoupled Services: By using Kafka, we can decouple services and components, enabling independent development, deployment, and scaling of each service. 5. High Throughput: Kafka’s ability to handle high throughput makes it perfect for applications that require processing large streams of data quickly and efficiently. 6. Durability: Kafka ensures data durability through its distributed log storage, guaranteeing that no data is lost even in the event of system failures. 7. Integration: Kafka integrates seamlessly with various data sources and sinks, allowing us to build flexible and interconnected systems. 8. Enhanced Analytics: With Kafka, we can build powerful real-time analytics and monitoring solutions, providing actionable insights from live data streams. At TrueulyMarket, our expertise in Apache Kafka and event-driven architectures enables us to build high-performing, scalable, and reliable applications. Partner with TrueulyMarket to leverage the power of real-time data processing and enhance your digital solutions. #TrueulyMarket #ApacheKafka #EventDrivenArchitecture #RealTimeData #ScalableSolutions #TechInnovation #DigitalSolutions #DataProcessing #HighThroughput #DecoupledServices #Reliability #FaultTolerance #TechCommunity #SoftwareEngineering #ModernDevelopment #TechExcellence #DevelopmentTrends #TechSavvy #DigitalInnovation #AppOptimization #UserExperience #DataIntegration #Analytics #StreamProcessing #FutureOfTech #KafkaDevelopment #AppEnhancements #ScalableArchitecture #RealTimeAnalytics #Durability #EventDrivenDevelopment

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We're live with our Deep Dive on Timescale with AI Product Lead 🔥 Avthar Sewrathan! Timescale is making PostgreSQL better for AI ⚡️ In this conversation, Avthar shares insights into Timescale’s bet on PostgreSQL, their new open-source extensions for AI developers, and how their solutions are empowering developers to easily build sophisticated AI applications. Link below 👇 https://lnkd.in/efxJyGzA

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Apache IoTDB 1.3.2 is out! Check out the new features in query module and scripts: 📈 Storage Module: Improved insertRecords performance 🔍 Query Module: - New Explain Analyze statement for detailed monitoring - New UDAF framework - MaxBy/MinBy functions for timestamped values - Better performance for value filtering queries 🔄 Data Synchronization: - Path pattern matching - Metadata synchronization 🔧 Stream Processing: New Alter Pipe statement for plugin updates 📊 System Module: Data point stats include TsFile imports 🛠️ Scripts and Tools: - Local upgrade backup tool - New export/import scripts for CSV, TsFile, SQL - Windows environment improvements Discover more: https://lnkd.in/g-R55BEp The full release note: https://lnkd.in/ejKDVTv5 Try it now 👉 https://lnkd.in/edyn7ya7 #opensource #timeseries #database #datamanagement #UDAF #streamprocessing #ratis #csv #tsfile #sql

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