24 of 11.001 listings match.
Time = 5y,
src = 10/10,
lang = all,
dict = D1,
terms = 8(ANY). last watermark · 2026-05-07
Demand index · last 4 wk vs window mean
2979%
above baseline
Reg. pressure (D5 mentions)
4
across all listings in window
Top mover · D1
DATEV
∞ (z=0.0)
Recurring clusters
1
near-duplicate templates
01All 20 terms · Tools
9/20 have ≥1 citation · sparkline = trend · click row to filter · zero-count terms shown at bottom
02Term trend · top 5
anomalies marked · z > 2.5
PostgresBigQueryDATEVSnowflakeExcel
03Top movers · current half vs prev half
largest |Δ%| · z-score (Poisson)
| Term | Now | Prev | Δ % | z |
|---|---|---|---|---|
| DATEV | 2 | 0 | ∞ | 0.0 |
| Postgres | 20 | 0 | ∞ | 99.0 |
| Snowflake | 2 | 0 | ∞ | 0.0 |
| BigQuery | 4 | 0 | ∞ | 99.0 |
| Tableau | 1 | 0 | ∞ | 0.0 |
| Looker | 1 | 0 | ∞ | 0.0 |
| Metabase | 1 | 0 | ∞ | 0.0 |
| Excel | 1 | 0 | ∞ | 0.0 |
| HubSpot | 1 | 0 | ∞ | 0.0 |
04Co-occurrence · top 20 pairs
Lift = P(A,B)/(P(A)·P(B)) · click to filter on both
| Term A | Term B | Count | Lift |
|---|---|---|---|
| Postgres | API | 5 | 1.20× |
| Postgres | integrieren | 4 | 1.20× |
| Postgres | pipeline | 4 | 0.96× |
| pipeline | API | 4 | 3.84× |
| Postgres | DSGVO | 3 | 0.90× |
| DATEV | Postgres | 2 | 1.20× |
| BigQuery | Snowflake | 2 | 6.00× |
| Postgres | automatisieren | 2 | 1.20× |
| automatisieren | integrieren | 2 | 6.00× |
| Postgres | exportieren | 2 | 0.80× |
| exportieren | DSGVO | 2 | 4.00× |
| DATEV | integrieren | 1 | 3.00× |
| Snowflake | ETL | 1 | 12.00× |
| BigQuery | ETL | 1 | 6.00× |
| BigQuery | reporten | 1 | 6.00× |
| Snowflake | Tableau | 1 | 12.00× |
| BigQuery | Tableau | 1 | 6.00× |
| Looker | Tableau | 1 | 24.00× |
| Metabase | Tableau | 1 | 24.00× |
| Looker | Snowflake | 1 | 12.00× |
05Recurring clusters · near-duplicate listings
indicates productizable workflows · click row for records
| Exemplar | Sources | Members | Median gap |
|---|---|---|---|
| Show HN: I extract recipes from TikTok, Instagram, and the messy web | Hacker | 2 | 0d |