StarRocks’ Post

🌊 #apachepaimon and #starrocks nailing #olap on object storage; a true Streaming Lakehouse 🤘 ------- With the joint efforts of StarRocks kernel optimization and Apache Paimon, the StarRocks + Apache Paimon Lakehouse analysis capability is 15 times the query performance of the previous version. 👆 Although this article is from last year; so if you think this is exciting, do keep an eye on the new support coming for optimizing via Deletion Vectors. 👉 https://lnkd.in/dNXn8ip3 #datalake #lakehouse #olap #dataengineering

May marked the release of Apache Flink CDC 3.1.0, part of a significant evolution in stream processing and data integration. In a recent collaboration, Giannis Polyzos spoke with Leonard Xu, PMC of Apache Flink and Flink CDC Lead at Alibaba Cloud, about the details of this release, culminating in a comprehensive blog post that dives into Apache Flink CDC 3.0's capabilities. https://lnkd.in/grpAgyzj In essence, Apache Flink CDC is maturing into a sophisticated streaming data integration solution, and your feedback is crucial to its continuous improvement. After reading, testing, or implementing, consider contributing to the project's progress by starring it on GitHub: https://lnkd.in/g4-pQa9E Flink CDC announcement: https://lnkd.in/g3HJEbBT #ApacheFlinkCDC #StreamProcessing #DataEngineering #Flink #CDC #DataIntegration #OpenSourceDevelopment #ReleaseNote

Excited to share my first official blog for Redis! In it I explore how to implement, interpret, and improve RAG results using the RAGAS evaluation framework.

LLMs and RAG systems are a new paradigm in machine learning and they require new ways to be evaluated beyond the metrics that classification, regression and clustering models typically rely on. here's a great resource on how to do that evaluation.

It’s critical for SWEs to understand this workflow as this new age of AI implementations will force scaled LLM adoption across industries

Have you ever thought about what .ai or .io in the domain name mean? redis.io for example? Random thing I learned recently: .ai is the Internet country code top-level domain (ccTLD) for Anguilla, a British Overseas Territory in the Caribbean. It is administered by the government of Anguilla. .io is a top-level domain nominally assigned to the British Indian Ocean Territory.

Are you building a real-time GenAI application? Our latest article explores how Redis can help you achieve responsiveness that adheres to the 100ms Rule. Making your applications feel FAST. 😉

Today, I read this article as my daily lesson. Redis Pub/Aub in detail :) https://lnkd.in/gpyHFV4k

Apache Beam: How Beam Runs on Top of Flink Apache Flink and Apache Beam are open-source frameworks for parallel, distributed data processing at scale. Unlike Flink, Beam does not come with a full-blown execution engine of its own but plugs into other execution engines, such as Apache Flink, Apache Spark, or Google Cloud Dataflow. In this blog post we discuss the reasons to use Flink together with Beam for your batch and stream processing needs. We also take a closer look at how Beam works with Flink to provide an idea of the technical aspects of running Beam pipelines with Flink. We hope you find some useful information on how and why the two frameworks can be utilized in combination. Article link - https://lnkd.in/gJ6G7vjW Video link at Flink Forward - https://lnkd.in/gDVWHSE9 #apacheflink #flink #apachebeam 👉 I publish posts on #dataengineering, #datastreaming, #analytics, #realtimeanalytics and #architecture regularly. Follow me or see my articles on https://jaehyeon.me/.

For Go developers, the latest Redis Go client library 9.7.0 has full support for search (includes vector search, full-text search, geo-indexing, numeric and exact match) and now supports JSON documents. Take a look at the GA release! https://lnkd.in/dWTZj5U7 Find examples to search through hashes and JSON documents. https://lnkd.in/dwXKxh-T https://lnkd.in/dKWvCeuw #redis #go #golang #vectorsearch