Datasets:

FlyRank
/

internship-warehouse

license: other
language:
  - en
tags:
  - seo
  - content-performance
  - data-warehouse
  - tabular
  - education
  - flyrank-internship
pretty_name: FlyRank Internship — Warehouse Star Schema (Pseudonymized, Gated)
size_categories:
  - 10M<n<100M
extra_gated_prompt: >-
  By requesting access you agree to the FlyRank Internship Data Use Terms:
  anonymized research and education use only; no attempt to re-identify clients,
  domains, queries, keywords, or content; no redistribution of the raw data; and
  no client-identifying data in any public output (case study, repo, chart, or
  demo).
extra_gated_fields:
  Name: text
  Email: text
  Affiliation or cohort: text
  I agree to the FlyRank data-use terms: checkbox
configs:
  - config_name: dim_clients
    data_files: dim_clients.parquet
  - config_name: dim_content
    data_files: dim_content.parquet
  - config_name: fact_content_daily_performance
    data_files: fact_content_daily_performance/**/*.parquet
  - config_name: fact_content_query_90d
    data_files: fact_content_query_90d.parquet

FlyRank Internship — Pseudonymized Warehouse Release (v20260703)

The open-ended, warehouse-shaped dataset (~81.8M rows; daily fact 78,835,655 rows) for advanced capstone work. Star schema with salted, namespaced, fingerprinted hash keys. Built from warehouse v2 full history (frozen snapshot, export date 2026-07-03): an unbalanced panel — per-client history depth differs; see dim_clients.gsc_data_start / ga4_data_start.

Table	Rows	Grain
`dim_clients`	104	one row per pseudonymized client
`dim_content`	519,606	one row per pseudonymized content item
`fact_content_daily_performance`	78,835,655	one row per report date, pseudonymized client, and pseudonymized content item
`fact_content_query_90d`	2,414,248	one row per pseudonymized client, content item, and query hash over the fixed 90-day window

fact_content_daily_performance is partitioned by month=YYYY-MM. The _sample file is the latest full month — start there.

fact_content_query_90d is query-level: salted query hashes over a fixed 90-day window with last-30/prev-30 sub-windows; the rare tail (< 10 impressions) and Google-anonymized impressions are preserved as per-content aggregate shares.

Load

from datasets import load_dataset
ds = load_dataset("FlyRank/internship-warehouse", "fact_content_daily_performance", streaming=True, split="train")

Or DuckDB (works in Colab; no full download needed):

import duckdb
con = duckdb.connect()
con.execute("CREATE SECRET (TYPE huggingface, TOKEN 'hf_your_read_token')")  # accept the gate in-browser first, then paste a READ token
rel = "hf://datasets/FlyRank/internship-warehouse"
con.sql(f"SELECT COUNT(*) FROM read_parquet('{rel}/fact_content_daily_performance/**/*.parquet')")

Terms

Anonymized research and education use only. No re-identification attempts, no redistribution, no client-identifying output. Hash keys are salted and namespaced; the salt never ships.