Polaris Catalog: A Data Strategy Solution

New ScyllaDB in Action chapter this week! Good reads isn't just a website; they're critical for your database's performance. In Chapter 7, you'll dive into how ScyllaDB reads data, how your read queries influence Scylla's performance, and how Scylla helps you denormalize your data through materialized views and indexes. The only thing better than reading is reading about reading, and a new chapter awaits! http://mng.bz/46Jw

"Open standards are rapidly becoming the foundation for scalable business value, driving innovation, momentum and action. With the recent incubation of Apache Polaris, an open-source lakehouse catalog implementation for tracking Apache Iceberg tables, we are moving toward a world where data and its governance are truly portable," writes Alex Merced in this article from The Stack: https://lnkd.in/ezFBxfGE

Wait for hours to insert data? We faced a similar challenge. And we found a solution. Our recent project transformed a slow, inefficient database process into lightning speed. Are you curious to know how we were able to achieve a 98% reduction in processing time? Read my latest article. It gives a detailed breakdown. https://lnkd.in/d2k5nJK9

Is your data lakehouse getting a little…frosty? Thanks to a new native integration with Apache Iceberg, SingleStore has a way to turn up the heat, helping you bring sub-second performance at scale to those data lakehouses once and for all. Learn all about it in this article from our own Dave Eyler, writing in The New Stack.  Simply put, SingleStore is the best data platform for powering intelligent apps and operational use cases on Iceberg data. Want to learn more about how you can unfreeze your most valuable enterprise data and drive innovation at your company? Visit our website to try a free trial today. https://lnkd.in/gB_65YsD

Why wait for slow queries when you can speed things up? On the Apache Arrow blog, Ian Cook, David Li, and Matt Topol, break down how Arrow's format accelerates query result transfers by eliminating inefficiencies. Key takeaways: ✅ Columnar format reduces costly transpositions ✅ Zero-copy architecture minimizes overhead ✅ Universal language support ensures seamless integration across platforms Read the full post to learn how Arrow boosts query performance and discover real-world speedups from tools like Theseus, DuckDB, and Snowflake. 🔗 Read more: https://lnkd.in/eBbjz5uZ #DataAnalytics #ApacheArrow #BigData #OpenSource

With in-process, open source DuckDB, you can create an in-memory database that does not save data, or you can use a local file. Here's how to get started. By David EASTMAN

Worth reading: How the Apache Arrow Format Accelerates Query Result Transfer by Ian Cook One of the hidden problems of intersystem communication is transcription of data formats. Ian does a wonderful job of explaining the "why" of Apache Arrow as well as the "how" of its advantages. Read on...

You can't always get what you want. So here's a quick solution to inheriting a project that relies on a lot of seed data to achieve a green CI build without rewriting the tests so they no longer need the seed data. https://heyor.ca/1DnwsH

2025 is shaping up to be a breakout year for fast query result transfer with Apache Arrow. But what exactly makes it so fast? David Li, Matt Topol, and I break it down in this new blog post:

This article is a great read for those wondering how the Apache Arrow project is drastically improving database interactions.