Datanika just gained four new connectors: Oracle, Pipedrive, Freshdesk, and Asana. That brings us to 36 connectors — and, like most of the others, each one was mostly a config exercise on top of dlt, not a from-scratch integration.
Here's what each one unlocks.
Oracle — get your data out of Oracle
Oracle is the database enterprises ask for most, and almost always for the same reason: they want to move data off Oracle into a modern warehouse without paying for GoldenGate or a legacy ETL suite. Datanika connects to Oracle Database 12c and newer as a source, extracts full schemas or individual tables with incremental loading, and lands the data in BigQuery, Snowflake, PostgreSQL, or any other destination — where dbt can reshape it.
It's source-only for now: extract from Oracle, load elsewhere. If you need Oracle as a destination too, open an issue — it's a small addition on the same SQLAlchemy path.
Pipedrive — sales analytics past the dashboard ceiling
Pipedrive's built-in reports are fine until you want to join deals against revenue or model win-rates the way you define them. This connector pulls deals, persons, organizations, activities, and pipelines into your warehouse, where you can build proper sales analytics with dbt — and join Pipedrive against Stripe for real revenue attribution.
Freshdesk — support metrics you actually control
Freshdesk joins Zendesk as a support-data source. Extract tickets, contacts, agents, companies, and groups, then track resolution times, SLA compliance, and agent performance in your own warehouse — no more exporting CSVs by hand or fighting the built-in reporting.
Asana — project reporting across teams
Asana's native reporting is famously thin. This connector extracts tasks, projects, sections, users, and custom fields so you can build portfolio and velocity reporting dbt-side — and if you run more than one delivery tool, join Asana against Jira or GitHub for a single picture of throughput.
How to connect one
Same three steps as every other connector:
- Add a connection under Connections → New, pick the connector, and paste your credentials (encrypted at rest with Fernet).
- Create an upload with the new source and your warehouse destination, choose a load mode, and run it. Datanika handles extraction, schema mapping, and loading via dlt.
- Write dbt models to transform the loaded data, then schedule the whole pipeline.
And because Datanika bills per GB processed — not per connector, not per row — adding these to your stack doesn't change your per-seat or per-connection math. Every connector is available on every plan, including Free.
Top comments (0)