{
  "id": 16172066,
  "title": "A Successful ETL Job Can Hide Thousands of Data Discrepancies",
  "url": "https://urgent.news/2026/10/23/a-successful-etl-job-can-hide-thousands-of-data-discrepancies",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-23T08:49:40.000Z",
  "source": {
    "name": "HackerNoon",
    "slug": "hackernoon",
    "url": "https://hackernoon.com/a-successful-etl-job-can-hide-thousands-of-data-discrepancies?source=rss"
  },
  "original_language": "en",
  "account": "A Successful ETL Job Can Hide Thousands of Data Discrepancies\n\nAn ETL workflow that runs without errors may still contain significant data discrepancies. While the system may report success, the actual data in the target tables could be incomplete, missing identifiers, or invalid. This issue is common in enterprise data engineering projects.\n\nTo ensure data accuracy, it's essential to distinguish three key questions: Did the pipeline execute successfully? Did the expected data arrive? Does the resulting data satisfy business rules? These questions require different validation methods.\n\nRelying solely on record counts can be misleading. For instance, if a previous dataset had 248,750 records and the current dataset has 236,420 records, one might conclude that 12,330 records are missing. However, this number only represents the net change in dataset size, not the specific records added or removed.\n\nA more thorough approach involves bidirectional reconciliation. By comparing datasets using unique business identifiers, engineers can identify records present in one dataset but missing from the other. This method reveals not only the missing records (12,845) but also the unexpected additions (515), totaling 13,360 distinct identifiers.\n\nThe difference between the total record count (12,330) and the total distinct identifiers (13,360) highlights the distinction between net record changes and unique identifier discrepancies. While the former measures the overall size difference, the latter provides insight into the actual data content.\n\nHowever, one-directional comparisons can miss unexpected additions. For example, if 5,000 identifiers disappear from one cycle and 5,000 new identifiers appear in the next, the record count remains unchanged, but 10,000 unique identifiers have changed. Therefore, bidirectional comparisons are preferred to detect both missing and added records.\n\nTo determine why records are missing, engineers should compare the missing identifiers against a master dataset. This helps identify if the record was legitimately removed, filtered by business rules, or eliminated due to processing issues. Understanding the cause of missing records is crucial for addressing the root problem.\n\nIn summary, while record counts provide a quick indicator of dataset changes, a comprehensive reconciliation process involving bidirectional comparisons, identifier-level validation, and investigation of the underlying causes is necessary to ensure data quality and business rule compliance.",
  "summary": "Learn why successful ETL jobs can produce incorrect data and how SQL, Python, and bidirectional reconciliation help detect missing records and data errors.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}