The 10 Most Common Data Ingestion Failures — and How to Detect Them Early
- Vexdata
- Apr 30
- 2 min read
Data ingestion isn’t as simple as drag and drop.
It’s more like: “Is this the same format? Are all the fields there? Does it break anything downstream?”
Here are 10 common ingestion failures — and how to catch them before things go wrong.
1. Missing Files
What it is: A file wasn’t sent.
How to detect it: Set up checks for expected delivery time and file count.
2. Wrong File Format
What it is: You expected CSV, got XLSX.
How to detect it: Check file extensions and headers before ingestion.
3. Schema Changes
What it is: Columns are reordered or renamed.
How to detect it: Compare column names and order to a schema baseline.
4. Inconsistent Data Types
What it is: A date becomes text, a number becomes NULL.
How to detect it: Validate column types on every new file.
5. Truncated Files
What it is: File has fewer records than expected.
How to detect it: Compare row counts to rolling averages or previous values.
6. Duplicate Records
What it is: Same data, sent twice.
How to detect it: Check for duplicate primary keys or identical rows.
7. Corrupt Files
What it is: File opens but is unreadable by the system.
How to detect it: Set up parser-level checks for readability.
8. Unexpected Nulls
What it is: Mandatory fields suddenly show up blank.
How to detect it: Flag nulls in required columns.
9. Misaligned Headers
What it is: Column headers are present in the wrong row.
How to detect it: Validate header row contents before parsing.
10. Partial Uploads
What it is: File transfer was interrupted.
How to detect it: Check file size and integrity using checksums.
Real-world tip: If your ingestion pipeline deals with daily transactional data from vendors, even one schema mismatch can cost hours of manual cleanup and wrong analytics.
🛠️ Automating these checks saves time and prevents errors — before they cascade.
Comments