04A Spark job that used to finish in minutes now takes hours. How do you diagnose and fix it?▼hardDatabricksSnowflakeMicrosoft2 repliesunlockedThe Databricks-flavored performance question. The signal is going straight to the usual suspects (skew, shuffle, spill) via the Spark UI, not guessing. Here is the diagnostic order and the fixes that actually move the needle.Open full answer →
23What is data skew in a distributed job (Spark), and how do you fix it?▼hardDatabricksAmazonMeta1 replies◆ premiumThe top cause of mysteriously slow Spark jobs: one partition does most of the work while the rest idle. The signal is reading the symptom (a few straggler tasks) and reaching for the right fix, salting, broadcast, or AQE.Open full answer →
58Turn a raw web crawl into a clean trillion-token LLM training corpus. Design the pipeline.▼hardNewNVIDIAAnthropicOpenAI◆ premiumAnyone can say 'filter and dedup.' The signal is the funnel in cost order, the MinHash/LSH banding math, and knowing the shuffle at billions of documents is what actually costs you, plus benchmark decontamination people forget until their eval numbers get challenged.Open full answer →
24What do Ray, Horovod, Spark, and Dask do, and when do you use each for distributed ML?▼mediumDatabricksAmazonNVIDIA1 replies◆ premiumThese four get conflated constantly, but they live at different layers: data processing, distributed training, and general orchestration. The signal is matching the tool to the workload instead of reaching for the one you know. Here is the answer.Open full answer →
