atwong
Joined 16 years ago
Karma
9
Submissions
45
Member since
Mar 18
Recent submissions
Comment · 1 year ago
Hmm... US has 330 million. JP has 120 million in the size of california. They have a long way to go.
Comment · 2 years ago
One company looking to capitalize on this is Onehouse, a three-year-old Californian startup founded by Vinoth Chandar, who created the open source Apache Hudi project while serving as a data architect at Uber. Hudi brings the benefits of data warehouses to data lakes, creating what has become known as a “data lakehouse,” enabling support for actions like indexing and performing real-time queries on large datasets, be that structured, unstructured, or semi-structured data.
Comment · 2 years ago
The idea of running workloads on the cheapest compute platform is interesting.
Comment · 2 years ago
Location: Irvine, CA
Remote: Preferred, but Hybrid fine within 20 miles of Irvine, CA
Willing to Relocate: No
Technologies: Strongest in Java, Kubernetes, MongoDB, SQL, Kafka, Data Lakehouse (Wrote Java apps as a developer lead at IBM, was on the 0.1 kubenetes team at Red Hat, 3+ years at MongoDB leading the technical direction at their #1 ARR account WW, Head of Community and DevRel at a 65 million VC-funded startup that was building an open source version of Snowflake)
Resume: http://linkedin.com/in/atwong
Email: [email protected]
About me:
My game is changing the world at warp speed. I've honed my skills across the board - sales engineer, developer advocate, lead developer, even infrastructure and operations maestro. Think of me as a multi-class strategist with 10 patents under my belt and 20+ certifications in my arsenal – Java, .net, you name it. I can build it, deploy it, talk the talk, and walk the walk, from bare metal to cloud-native code. So, what's the next challenge we'll tackle together?
I am looking for my next job in sales engineering or developer relations.
Comment · 2 years ago
Data-warehousing becomes ubiquitous and open-source with Iceberg. Powerful open-source RTA (real-time-analytics) engines like ClickHouse are designed for crunching numbers as fast as possible. How can you use Iceberg and ClickHouse together to burn the rubber over glacially slow storage?
Comment · 2 years ago
Hm... I'm not sure if I see anything more than I can get from https://ossinsight.io/analyze/StarRocks/starrocks#overview
Comment · 2 years ago
intake of 450,000 entries per second, and a daily data volume of 12TB
* Query performance improved 4x. * Query P90 time improved from 500ms to 150ms. * The cluster size reduced from 60+ nodes to less than 10 nodes. * Data storage volume reduced by about 40%. * Overall cost reduced by more than 80%.