Data Panda brings your Coda docs, tables and pages together with the rest of your business data. From one place, we turn your workspace into dashboards, automations, AI workflows and small apps your team uses every day.
Coda was founded in June 2014 in Mountain View, California by Shishir Mehrotra, formerly product lead on YouTube at Google, and Alex DeNeui, both ex-Microsoft. The product opened to the public in 2019. In December 2024 Grammarly announced it was acquiring Coda, with Mehrotra stepping in as CEO of the combined company. Customers include Spotify, Pinterest, Square and Uber.
What makes Coda different from a regular doc is the model. A page can hold rich text, tables with typed columns, buttons that run formulas, and Packs that pull in live data from Slack, Jira, Salesforce, GitHub or Figma. A team's project tracker, OKR review, hiring pipeline or product spec all live in the same doc, with the same row-and-column primitives behind them. That programmability is also why Coda content is worth pulling into a warehouse: every doc is a small app with real schema, not just prose.
Doc count, table activity, ownership coverage and edit cadence across the entire workspace.
Let Coda events drive the rest of your stack, instead of someone copying a status into Slack every Monday.
Put your real Coda content behind AI that knows your docs, instead of a generic assistant that invents the answer.
Small tools sitting on top of Coda content for people who do not live in the Coda sidebar.
A list of concrete reports, automations and AI features we have built on Coda data. Pick the one that matches your situation.
How much of our Coda workspace is in active use?
Docs and tables grouped by last-edited age and write activity, per team and parent folder. Splits the small share of docs that absorb most of the work from the long tail that froze when the project closed. That is the baseline before any cleanup.
Which tables are missing ownership, status or other key fields?
Per table, the share of rows with blanks on owner, status or any column you mark as required. Trended over time so you see whether discipline is climbing, flat, or drifting as new people come in.
Do our Coda roadmap items match what engineering shipped?
Coda roadmap rows joined to Jira tickets, GitHub PRs or release notes, compared against the status the Coda row claims. Surfaces items marked done in Coda with no linked delivery, and shipped work that never made it back into the Coda roadmap.
When Coda hosts vendor lists, budget trackers or light CRM tables, finance gets the same coverage figures from Coda that the core systems already deliver. No more reading through a doc to check whether a renewal got logged.
For sales teams using Coda as account or deal notes, those rows land next to the CRM record. Rep activity and pipeline updates kept in Coda stop being invisible to the VP Sales dashboard.
A company-wide view on doc ownership, stale tables and Pack usage. Ops can run a quarterly workspace cleanup with a real list, instead of guessing which team is the worst offender.
Roadmap rows in a Coda table 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 Coda roadmap reflects what Jira shows, and product owners stop maintaining two copies of the same list.
Planning rows in Coda generate the delivery project and the first tasks in Asana, keyed on a project ID. As tasks close in Asana, the Coda row shows live status instead of a stale checkbox someone forgot to tick, and the planning doc and the task board stop drifting apart mid-quarter.
Updates on flagged Coda tables, like a status change on a launch row or a new hire row, post a compact message in the Slack channel that owns the topic, with a link back to the table. Teams stop relying on someone watching Coda all day, and table owners get a light audit trail next to the conversation where it matters.
Teams that run account or deal notes in a Coda table get two-way sync with HubSpot: new Coda rows create HubSpot contacts, and HubSpot updates flow back into the Coda row. Result is one truth per contact instead of a sales team keeping the real notes in Coda and a half-updated record in the CRM.
Account managers run their close-plan checklist, internal owners and notes in a Coda table per opportunity. The matching Salesforce opportunity stays the system of record for stage, amount and close date, and the Coda row syncs the working layer around it, so the rep stops choosing between updating Salesforce and keeping notes that are useful in the room.
A Coda doc keeps the planning, OKRs and context. The matching monday.com board mirrors the rows for the people who live in monday all day. Status updates go in either direction, so a status change on the board moves the Coda row and the other way around, without anyone copy-pasting on Friday afternoon.
Coda holds the live operational tables, like the launch tracker or hiring pipeline. Notion holds the long-form playbook and wiki around them. The connector links each Coda row to its Notion playbook page, and surfaces the current Coda status inside the Notion page, so the wiki stops being a static description of how the work used to run.
You keep the reporting tool you already have. We connect it to the warehouse where your Coda 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.
Coda tables are user-defined, so there is no single schema to pre-model across docs. The sync uses Coda's API to pull docs, tables, columns and rows with the column types each table carries, exposed as warehouse columns. We do not pretend a generic 'tasks' or 'CRM' model exists under the workspace. We land the raw shape, then the analytics layer is where per-doc business logic sits, one table at a time.
Formula and Pack columns return their computed value via the Coda API, so the warehouse sees what the user sees in the table at sync time. The formula expression itself is metadata, not data, so we capture which columns are formula-driven for governance, but we do not try to re-evaluate Coda formulas inside the warehouse. If a Pack call inside a formula goes stale, that drift shows up in the synced value.
Coda's API is rate-limited per token and per workspace. On a workspace with hundreds of docs and tens of thousands of rows that matters. We use incremental sync where the table exposes a usable updated-at, schedule reads around the cap, and skip archived docs unless you want them. The first backfill takes longer, after which ongoing syncs are small.
We review your Coda setup and the systems around it. Together we pick the first thing worth building.