121 of 21.217 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
4942%
above baseline
Reg. pressure (D5 mentions)
4
across all listings in window
Top mover · D1
sevdesk
∞ (z=0.0)
Recurring clusters
7
near-duplicate templates
01All 20 terms · Tools
15/20 have ≥1 citation · sparkline = trend · click row to filter · zero-count terms shown at bottom
02Term trend · top 5
anomalies marked · z > 2.5
DATEVPostgresSnowflakeExcelBigQuery
03Top movers · current half vs prev half
largest |Δ%| · z-score (Poisson)
| Term | Now | Prev | Δ % | z |
|---|---|---|---|---|
| sevdesk | 2 | 0 | ∞ | 0.0 |
| Snowflake | 23 | 0 | ∞ | 99.0 |
| Excel | 5 | 0 | ∞ | 99.0 |
| SAP | 1 | 0 | ∞ | 0.0 |
| Postgres | 25 | 0 | ∞ | 99.0 |
| Confluence | 1 | 0 | ∞ | 0.0 |
| BigQuery | 4 | 0 | ∞ | 99.0 |
| Tableau | 1 | 0 | ∞ | 0.0 |
| Looker | 1 | 0 | ∞ | 0.0 |
| Metabase | 1 | 0 | ∞ | 0.0 |
| Power BI | 1 | 0 | ∞ | 0.0 |
| Jira | 1 | 0 | ∞ | 0.0 |
| HubSpot | 1 | 0 | ∞ | 0.0 |
| Lexware | 1 | 0 | ∞ | 0.0 |
| DATEV | 72 | 1 | +7100.0% | 71.0 |
04Co-occurrence · top 20 pairs
Lift = P(A,B)/(P(A)·P(B)) · click to filter on both
| Term A | Term B | Count | Lift |
|---|---|---|---|
| DATEV | exportieren | 7 | 1.16× |
| Postgres | API | 5 | 3.02× |
| Postgres | integrieren | 4 | 4.84× |
| Postgres | pipeline | 4 | 3.87× |
| pipeline | API | 4 | 12.10× |
| DATEV | Excel | 3 | 0.99× |
| DATEV | Pflege | 3 | 1.66× |
| DATEV | API | 3 | 0.62× |
| DATEV | Buchhalter | 3 | 1.66× |
| Postgres | exportieren | 3 | 1.45× |
| Postgres | DSGVO | 3 | 3.63× |
| DATEV | sevdesk | 2 | 1.66× |
| sevdesk | exportieren | 2 | 12.10× |
| Snowflake | ETL | 2 | 5.26× |
| DATEV | REST | 2 | 1.66× |
| DATEV | SOAP | 2 | 1.66× |
| API | REST | 2 | 15.13× |
| API | SOAP | 2 | 15.13× |
| REST | SOAP | 2 | 60.50× |
| DATEV | Steuerfachangestellte | 2 | 1.66× |
05Recurring clusters · near-duplicate listings
indicates productizable workflows · click row for records