Every ML failure I have debugged traces back to unversioned data. Code has Git. ML data has DVC, lakeFS, Delta Lake, and Iceberg. Most teams use none of them. Here is the data versioning landscape and how to pick, grouped into 3 capability areas: 𝐖𝐡𝐲 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 ① Reproduce a model months later without guessing ② Debug regressions: was it the code or the data ③ A/B test with different data snapshots ④ Roll back bad training data before it hits production 𝐓𝐨𝐨𝐥 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 ⑤ DVC: Git-like, works with any storage, best for file-level ML datasets ⑥ LakeFS: Git for object storage, branch/merge on S3, best for lakehouse workflows ⑦ Delta Lake: table-level time travel + versioning, best for Databricks/lakehouse ⑧ Iceberg: open table format with snapshots, best for multi-engine environments 𝐖𝐡𝐢𝐜𝐡 𝐭𝐨 𝐏𝐢𝐜𝐤 ⑨ File-based ML datasets (images, audio, docs): DVC ⑩ Lakehouse-native workflows on S3: LakeFS or Iceberg ⑪ Databricks or Delta-committed shops: Delta Lake native ⑫ Snowflake shops adopting open formats: Iceberg via Polaris Unversioned data is the #1 hidden cause of "the model was working last week." Real senior data engineers make data versioning the default, Not an afterthought triggered by a production incident. The gap between "our model is broken and we do not know why" And "we rolled back to yesterday's data snapshot in 10 minutes" Is where data versioning earns its keep. Which tool are you using today? 👇 Follow for more. If you are looking for a Data Engineering Roadmap, Resume Template or Interview questions with answers do check out my Product — lnkd.in/gpcJ5rxj hashtag#DataEngineering hashtag#ML hashtag#MLOps hashtag#Reproducibility