The thing that makes AI agents useful ("non-deterministic reasoning") is exactly what makes them impossible to debug. Traditional automation was boring because it was predictable. Same input, same code, same output. AI is useful precisely because it breaks that model. It can "think on its feet", at 1000x human speeds. So, if you give an agent the same goal twice and it may take a different path, call different tools, retrieve different context, and produce a different answer. That autonomy is the value prop. The balance lies in ensuring that when something goes wrong, you can reproduce what actually happened. Teach it, Coach it. For that, versioning the model is not enough. You need the exact state of everything that influenced the result: • source data • transforms • embeddings • indexes • policy/config • model checkpoint Miss one, and you are not replaying the same run. AI makes systems non-deterministic. Versioning is how we make that non-determinism debuggable, auditable, and reproducible.

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.

The 30x jump in open-model adoption this year hides a costly blind spot. As Meta, GLM, Kimi, Qwen, and Mistral models get more capable, more teams are customizing them. But SFT, preference tuning, and RFT aren't interchangeable—and the differences determine whether you spend $50 or $50,000 for the same result. 1️⃣ Supervised fine-tuning (SFT) You provide prompt/response pairs. The model learns by imitation. Think: prompt → ideal response 2️⃣ Preference tuning (DPO) For each prompt, provide multiple responses and mark which is better. The model learns from comparisons. Think: prompt → preferred response + rejected response 3️⃣ Reinforcement fine-tuning (RFT) The model produces an answer, a grader scores it, and that becomes the reward signal. The grader can verify correctness, run tests, or apply domain rules. Think: task → response → grader → reward 📊 Each approach demands different data. SFT needs accurate, representative prompt/response pairs with consistent formatting. Preference tuning needs the full comparison record: what outputs were judged, which won, and the context. RFT needs a representative task distribution, execution context, and clear lineage to the source. ⚠️ The bottleneck across all three: data management. Training data is a data engineering problem: 🔍 Curate datasets from production ✅ Validate before training 🔗 Track provenance for every example 📦 Version each dataset 🔄 Make every run reproducible The training API is the easy part. The leverage is in the data infrastructure underneath.

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.

Most AI engineers describe fine-tuning as: make a file, hit a training endpoint. What I saw at Uber and LinkedIn: ETA prediction and People You May Know didn't win on just better models. They won by nailing the full data lifecycle: Operational data → reliable training data → better models → better products → better outcomes → more operational data. ✅ The advantage: infrastructure that democratized access to signals and experimentation. Better models powered better products, whose usage generated even better data. 🔄 AI training data works the same way. Examples arrive continuously. Labels get corrected. Eval failures create hard negatives. Customer feedback shifts preferences. Policies require deletion. Parsers, pipelines, and models change the derived data. The model trains periodically. The data changes constantly—and it's mutable. 🛠️ That requires production data discipline: • Canonical records and derived views • Incremental updates and backfills • Reproducible snapshots • Lineage and quality checks • Delete propagation and retention • Schema evolution and observability ⚠️ Consider one mislabeled support conversation. Fixing it means updating the source, regenerating training examples, revising eval data, refreshing embeddings, updating hard-negative pools—across every snapshot. Deleting won't unlearn what the model saw. But if we can't reliably remove it from future training, evaluation, and retrieval, we don't control the lifecycle. 🔁 AI teams are rediscovering database problems: mutable state, consistency, lineage, backfills, deletes, retention, reproducibility, derived-data management. The final training file may look simple. The system that makes it trustworthy is not.

Apache XTable 0.4.0 (incubating) is out. 45 commits from 19 contributors, including patches from companies running it in production. Format support went from 3 to 5. Paimon and Parquet join Hudi, Iceberg, and Delta Lake. Still zero data rewritten—just metadata conversion. What shipped: 🔧 Spark runtime jar that reuses your cluster's existing Hudi/Iceberg/Delta libraries. Add it with --jars, call XTableSyncService after your write, or submit XTableSparkSync standalone. Supports Spark 3.4 and 3.5. 🔗 REST service for format translation, integrates with Polaris for catalog-aware sync. ⚡ Delta Kernel source and target—read and write Delta tables through delta-kernel-api without needing a SparkSession. Project's been quiet, but that changes. We plan to contribute upstream features around data governance and semantic layers in the coming months. Download: xtable.apache.org/downloads Release notes: xtable.apache.org/blog/apach…