Facebook

Facebook Ads Campaign Export Sample Dataset

Facebook Ads Manager exports campaign-level reports as UTF-8 CSV files with exact production headers: Campaign name, Campaign ID, Ad Set name, Amount spent (USD), Impressions, Clicks (all), CTR (all), CPC (all) (USD), Reporting starts, and Reporting ends. This dataset contains 3,200 synthetic campaign performance rows spanning 14 days of multi-objective ad delivery. The Facebook Ads export quirk that breaks downstream analytics is that Amount spent (USD) and CPC (all) (USD) values inconsistently include the $ currency symbol prefix depending on the account's regional settings. When pandas or DuckDB attempts SUM('Amount spent (USD)'), the $ prefix forces the entire column to be parsed as string type, returning zero or throwing a type-cast error. Additionally, Reporting starts and Reporting ends use MM/DD/YYYY format while other date columns may use YYYY-MM-DD. The dirty data inventory includes: 890 Amount spent (USD) cells prefixed with $ (e.g., $1,234.56), 340 rows where date ranges span different reporting windows, and 215 Campaign name values containing unquoted commas that split into phantom columns. Expected Output: Yields 3,200 clean campaign-day rows ready for ROAS pivot analysis. Ideal for: ETL testing, DuckDB demos, Power BI, Excel Power Query, CI pipelines. Run this dataset through the ads-roas-pivot tool to strip currency formatting and build a clean daily spend-to-revenue pivot table.

This dummy dataset simulates a standard export from Facebook, containing realistic yet fully anonymized records that mirror the structure and common data quality issues found in real production environments. The CSV file includes typical problems such as inconsistent formatting, missing values, duplicate entries, and non-standardized categorical fields.

It is designed to be used as a safe sandbox for testing data cleaning workflows directly in your browser — no uploads, no server round-trips, no third-party data exposure. Whether you are validating a transformation pipeline, benchmarking a cleaning tool, or simply exploring common data quality patterns, this sample provides a representative starting point without risking any sensitive business data.

Data Schema

Column NameData TypeDescription
idstringUnique record identifier
created_attimestampRecord creation date and time
statusstringCurrent status of the record