Source connector
Everhour connector
Everhour integration for databases, warehouses and BI tools
Time tracking and budgets for project teams. Datrise captures Everhour entities such as time entries, projects, tasks, users, clients and budgets, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.
Of the 6 objects, 6 sync as upserts keyed on the primary key and 0 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `everhour_time_entries`) with a schema derived from the Everhour API rather than a flattened CSV, in any of the 43 supported destinations.
Datrise is in early access: pick a destination below to open the Everhour pipeline page, and join the waitlist to get the Everhour connector with guided onboarding.
What Everhour data Datrise extracts
Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.
| Object | Landing table | Sync mode |
|---|---|---|
| time entries | everhour_time_entries | Upsert by primary key, incremental on updated-at |
| projects | everhour_projects | Upsert by primary key, incremental on updated-at |
| tasks | everhour_tasks | Upsert by primary key, incremental on updated-at |
| users | everhour_users | Upsert by primary key, incremental on updated-at |
| clients | everhour_clients | Upsert by primary key, incremental on updated-at |
| budgets | everhour_budgets | Upsert by primary key, incremental on updated-at |
Popular destinations for Everhour
Choose a destination below to open the ready pipeline page.
- AAirtable
Relational spreadsheet destination for ops and go-to-market teams.
View pipeline - AAmazon Athena
Serverless SQL over S3 data lake tables.
View pipeline - AAmazon DynamoDB
Serverless key-value and document store on AWS.
View pipeline - AAmazon QuickSight
AWS serverless BI with SPICE and embedded analytics.
View pipeline - AAmazon Redshift
AWS petabyte-scale warehouse with Spectrum.
View pipeline - AAmazon S3 Data Lake
Object storage landing zone for parquet and snapshots.
View pipeline - AApache Superset
Open-source BI for SQL exploration, charts, and dashboard publishing.
View pipeline - AAzure Data Lake Storage
ADLS Gen2 object storage for analytics workloads.
View pipeline - AAzure Synapse
Microsoft analytics workspace with SQL pools.
View pipeline
Everhour connector FAQ
- What Everhour data does the connector extract?
- 6 objects: time entries, projects, tasks, users, clients and budgets. Each lands in its own table with types preserved; history-style objects (0) load as append-only streams.
- Is the Everhour integration incremental?
- Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
- Where can I load Everhour data?
- Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each Everhour → destination pair has its own page with the object-to-table mapping.
Related sources
Other sources in the same category, each with its own connector page.
Early access
Launch your Everhour pipeline with guided onboarding
Join the waitlist to get priority access for Everhour integrations with guided setup, schema-aware mapping, and production-grade incremental sync.