Caching is supposed to make queries faster. But on lakehouses, simply moving Parquet files into memory or fast SSDs often barely moves the needle. The common assumption is that data access is network-bound. With modern cloud networks offering amazing throughput, the bottleneck often isn’t the network anymore. It’s the CPU.

AWS EMR and GCP Dataproc both ship Hudi 1.x now. Most AI apps require four systems: Structured data → lakehouse Embeddings → vector DB Binary objects → object storage Semi-structured → document store Every write has to propagate across all four. Keeping them consistent is brutal. Hudi 1.2 collapses that. Native VECTOR, BLOB, and VARIANT types in one table. Vector search, binary storage, hierarchical queries—same transactional guarantees. The consistency problem disappears because there's nothing to sync.

🔥 📣 Your lakehouse already has the data. Your training pipeline doesn't know how to use it. We're announcing AI training data pipelines on Onehouse for @baseten , @FireworksAI_HQ , and @togethercompute Training data curation today feels like carrying lake water in a leaky bucket. Most teams "version" training inputs by dumping JSONL files into a cloud folder. When a label changes or an evaluation surfaces a gap, there's no way to trace which examples came from where, or rebuild the dataset from updated source tables. Consider a support agent learning from past conversations. Useful examples require joining each conversation to its outcome: Was the issue resolved? Was the answer correct? Did someone intervene? The conversation alone isn't enough. The evidence lives in operational tables. When the model handles a class of requests poorly, the next training dataset needs to reflect updated labels, corrected data, and new selection logic. Teams need the selection query, the source-table versions it read, and the history of those corrections. 🔄 These are familiar data engineering requirements: joins, versioning, lineage, backfills. The lakehouse already handles them. Here's what we built: 1️⃣ Quanton curates examples from lakehouse tables using SQL selection logic—join conversations to resolution status, filter by outcome quality, batch vector search for good/bad examples at scale. It writes versioned exports as training datasets. 2️⃣ Onehouse submits the training job to Baseten, Fireworks, or Together AI and tracks status in the UI. The provider trains the model and returns the fine-tuned weights or adapter. 3️⃣ Lakegres serves point-in-time context during training or eval—queries lakehouse tables for the exact documents, records, or state needed to replay a past trace. The dataset remains reproducible. Engineers can inspect the selection query, trace examples to source records, and rebuild the dataset when labels change or evaluation surfaces gaps. Go to quanton.dev for a free account and give it a spin, let us know!

Companies spend billions on Spark compute every year. Most assume open-source accelerators can close the gap to commercial engines. We ran TPC-DS 10 TB across 5 engines on the same 11-node cluster to find out. OSS Spark: 12,200s (baseline) Comet (tuned): 9,122s (25% faster) Gluten (tuned): 8,563s (30% faster) Databricks Photon 18.2: 2,550s (5× faster) Quanton: 2,384s (5× faster) Open-source accelerators can get you partway there—under ideal conditions.

Turns out AWS pricing is negotiable if you blog about it. AWS S3 Tables launched at $5.04 to compact 100GB Iceberg . EMR: $0.17. 29× markup. When we published the teardown, AWS dropped the price by 10× right after. Then it was $0.52 for the same job. EMR still 3× cheaper. Who knew transparency was a feature request? AWS has so many amazing services. But, 2 years in, S3 Tables still ships with the same foundational limitations it had at launch. Sadly, still only seems to beat competition by calling customer execs with some FUD about risk and support gaps if they don't adopt the managed service. Price cuts are good. Price cuts after being caught aren't really a win. They just make you wonder what else sold this way.