Data Panda brings your Airtable bases and tables together with the data from the rest of your business. From one place, we turn your bases into dashboards, automations, AI workflows and custom apps your team uses every day.
Airtable was founded in San Francisco in 2012 by Howie Liu, Andrew Ofstad and Emmett Nicholas, and hit an 11 billion dollar valuation in December 2021 on a 735 million dollar Series F. The homepage today claims 500,000 teams, with logos from Amazon, Walmart, Netflix, OpenAI and Anthropic. Unlike a docs-first tool, Airtable is database-first: every base is a set of tables with typed fields, linked records across tables, and a grid, gallery, kanban, calendar or timeline view over the same rows.
Product surfaces on top of that core are Interface Designer, Automations, Sync and the AI layer (Omni for app building, Field Agents for in-row AI). What makes Airtable interesting to report on is the same thing that makes it spread: every team designs their own tables, so a marketing team's content calendar, a product team's feature tracker, an ops team's vendor list and a sales team's deal pipeline all live as bases with different shapes. Pulling Airtable into a warehouse turns that collection of bases into something you can audit, join to the rest of the business, and keep honest.
Record counts, field fill rates, view usage and edit cadence across every base in the workspace.
Let Airtable records drive the rest of your stack, without someone re-typing a row into HubSpot or Jira on Monday morning.
Put your real bases behind AI that answers on your data, not on whatever a field agent hallucinates inside one record.
Small tools on top of Airtable for people who shouldn't need a base view to do their job.
A list of concrete reports, automations and AI features we have built on Airtable data. Pick the one that matches your situation.
How many bases do we run, and which ones still get used?
Bases grouped by last-edited age, record volume and unique contributors per month, rolled up per team. Separates the bases that carry weekly operations from the ones that stopped moving after a project wrapped. That's the starting point before any consolidation effort.
Where is the same customer, vendor or asset living in more than one base?
Records matched across bases on email, company name or external ID, flagged where two or three teams have built parallel lists. Tells you which team's list is the most complete, which one is stalest, and where an automation should be pointing at a single source instead of one copy per base.
Do our Airtable project rows match what engineering delivered?
Feature or project rows joined to Jira tickets, GitHub PRs or release notes, compared against the status each Airtable row claims. Surfaces rows marked done with no linked delivery, and work that shipped without ever being reflected back in the base the business reads.
When Airtable holds vendor registers, contract renewal bases or lightweight spend trackers, finance gets the same coverage numbers from Airtable that the ERP already delivers. No more cross-reading a base to see which renewal slipped through the cracks.
For sales teams running deal pipelines or account notes in Airtable, those rows land next to the CRM record. Rep activity and pipeline entries in a side-base stop being invisible to the VP Sales dashboard.
A company-wide view on base ownership, stale tables and duplicate records across teams. Ops runs a quarterly consolidation with a concrete list instead of a feeling about which team is the biggest base-hoarder.
Teams running a deal or account base in Airtable get two-way sync with HubSpot: new Airtable rows create HubSpot contacts, and HubSpot updates flow back into the Airtable record. The result is one truth per contact instead of a sales team keeping its real notes in Airtable and a half-maintained record in the CRM.
Feature or project rows in an Airtable base create the matching Jira ticket in the right project and epic, with description, priority and target release carried across. Status updates flow back so the Airtable row reflects what Jira shows, and product owners stop maintaining the same list in two places.
Edits on flagged Airtable tables or status changes on key records post a compact update to the Slack channel that owns the topic, with a link back to the row. Teams stop relying on someone watching the base, and record owners see a light audit trail of who changed what next to the conversation where the work lives.
A content-calendar base in Airtable drives Klaviyo campaigns: when a row flips to approved with a scheduled send date, the matching Klaviyo campaign gets built or updated with the right segment, subject line and asset links. Marketing maintains one calendar instead of a base plus a mirror campaign in Klaviyo that drifts by Wednesday.
These are the 2 tables we currently pull from Airtable into your warehouse. Query them directly in SQL, join them to the rest of your stack, or build reports on top.
Missing a table you need? We can extend the sync. Tell us what is missing and we will build it for you.
You keep the reporting tool you already have. We connect it to the warehouse where your Airtable data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Airtable bases and tables are user-defined, so there's no single schema to pre-model. The sync pulls the Bases and Tables the Peliqan connector exposes, with the fields each table carries, landed as columns in your warehouse. What we do not do is pretend a generic 'CRM' or 'project' model exists underneath. We land the raw shape, and the analytics layer is where per-base business logic sits, one table at a time.
Linked-record fields in Airtable point from one table to rows in another table, usually inside the same base. The sync preserves those record IDs as foreign-key-style columns, so you can join the two tables in SQL the same way the base joins them in the UI. Lookup and rollup fields that Airtable computes on read can be recomputed in the warehouse, which is usually where you want that logic to live anyway.
Airtable's API has a published per-base rate limit (5 requests per second per base, last confirmed on the official developer docs) and a page size of up to 100 records per list request. On a workspace with dozens of large bases this matters. We run incremental sync based on the last-modified field, pull per base so one heavy base does not block the others, and batch writes where a two-way automation needs them.
We review your Airtable setup and the systems around it. Together we pick the first thing worth building.