73 of 21.217 listings match.
Time = 5y,
src = 10/10,
lang = all,
dict = D1,
terms = 5(ANY). last watermark · 2026-05-08
Demand index · last 4 wk vs window mean
3205%
above baseline
Reg. pressure (D5 mentions)
4
across all listings in window
Top mover · D1
Postgres
∞ (z=99.0)
Recurring clusters
4
near-duplicate templates
01All 20 terms · Tools
13/20 have ≥1 citation · sparkline = trend · click row to filter · zero-count terms shown at bottom
02Term trend · top 5
anomalies marked · z > 2.5
sevdeskPostgresTableauDATEVBigQuery
03Top movers · current half vs prev half
largest |Δ%| · z-score (Poisson)
| Term | Now | Prev | Δ % | z |
|---|---|---|---|---|
| Postgres | 25 | 0 | ∞ | 99.0 |
| DATEV | 4 | 0 | ∞ | 99.0 |
| sevdesk | 39 | 0 | ∞ | 99.0 |
| Tableau | 6 | 0 | ∞ | 99.0 |
| Snowflake | 2 | 0 | ∞ | 0.0 |
| BigQuery | 4 | 0 | ∞ | 99.0 |
| Lexoffice | 2 | 0 | ∞ | 0.0 |
| Looker | 1 | 0 | ∞ | 0.0 |
| Metabase | 1 | 0 | ∞ | 0.0 |
| Excel | 2 | 0 | ∞ | 0.0 |
| Jira | 1 | 0 | ∞ | 0.0 |
| Lexware | 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 |
|---|---|---|---|
| sevdesk | API | 14 | 1.38× |
| Postgres | API | 5 | 0.77× |
| Postgres | integrieren | 4 | 2.34× |
| Postgres | pipeline | 4 | 1.95× |
| pipeline | API | 4 | 2.56× |
| DATEV | exportieren | 3 | 7.82× |
| sevdesk | exportieren | 3 | 0.80× |
| Postgres | exportieren | 3 | 1.25× |
| Postgres | DSGVO | 3 | 2.19× |
| DATEV | sevdesk | 2 | 0.94× |
| DATEV | Postgres | 2 | 1.46× |
| BigQuery | Snowflake | 2 | 18.25× |
| Lexoffice | sevdesk | 2 | 1.87× |
| sevdesk | automate | 2 | 1.87× |
| Excel | Postgres | 2 | 2.92× |
| Postgres | automatisieren | 2 | 2.92× |
| automatisieren | integrieren | 2 | 14.60× |
| exportieren | DSGVO | 2 | 5.21× |
| DATEV | integrieren | 1 | 3.65× |
| Tableau | reporten | 1 | 6.08× |
05Recurring clusters · near-duplicate listings
indicates productizable workflows · click row for records