Data Panda brings the Postgres databases behind your applications together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.
PostgreSQL started at UC Berkeley in 1986 and has been open-source since. It is ACID-compliant, extensible and used as the primary operational database behind a very large share of custom-built business applications, from internal tools to SaaS backends and ecommerce platforms. When your engineers built something important and picked a database, the answer was almost always Postgres.
The point of pulling Postgres into a warehouse is that reporting directly on the application database is slow, risky and quickly becomes wrong. Schemas change as developers ship features, large joins compete with live traffic, and the weekly dump someone set up two years ago now powers half the dashboards. In a warehouse, the application's Postgres becomes a first-class source next to Salesforce, Exact Online, Shopify and Stripe, without putting reporting load on the database that has to stay up.
Live operational data joined to the rest of the business, without querying the production database.
Let changes inside the Postgres-backed app fire actions across the stack.
Score, classify and forecast directly on the operational data you already capture.
Internal tools built on Postgres data without shipping raw database credentials.
A list of concrete reports, automations and AI features we have built on Postgres data. Pick the one that matches your situation.
Are our reports running on the live production database?
A survey of which dashboards query Postgres directly, with load, query cost and risk flagged. Identifies the reports that will start timing out at the next traffic spike, and the reports that need to move into the warehouse first.
What changes when the app ships a new schema version?
Schema-change log with added, renamed and removed columns, tied to the dashboards that use each. The release conversation stops breaking reporting quietly on the Tuesday deploy, because the impact is visible before the migration runs.
Which users will churn this month based on what they do inside the app?
Churn scoring built on real product-usage signals from the Postgres schema, not on a proxy like login count. Flags the accounts whose usage pattern has shifted in a way that predicted churn in previous cohorts.
Revenue and usage data from the app joined to the accounting ledger, without reporting load on production. Subscription, usage-based and transactional revenue all tie back to the same customer record.
Product usage and engagement on every CRM account, sourced from the app database. Reps see who is about to expand and who is going quiet, before the renewal call.
Schema drift, data-quality gaps and load shifts monitored in one place. Reporting stops being a surprise every release and becomes part of the deploy check.
Users, accounts and key usage events from the Postgres-backed application push into HubSpot as contacts, companies and timeline events. Sales and CS stop asking product for a CSV and start seeing usage signal on the record in real time.
Postgres account records match to the Stripe customer and subscription that belongs to them, so product-usage signals and billing state live on one line. Churn-risk scoring, billing reconciliation and plan-change triggers all run on the same id.
Invoices generated inside the Postgres-backed application post to Exact Online with customer, VAT and ledger coding resolved. The app keeps its own invoicing logic and finance still gets a clean set of sales journal entries.
Usage signals from the application database push onto Salesforce accounts as custom fields and timeline activity. Account executives see expansion and churn signals before renewal, without building a separate BI tool for sales.
You keep the reporting tool you already have. We connect it to the warehouse where your Postgres 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.
Logical replication or change-data-capture against a read replica is the default, so reporting workload never touches the primary. For smaller databases, scheduled incremental sync on updated_at is an option. The schema is replicated and the load profile is tuned to the tenant.
Schema changes are tracked and versioned in the warehouse. Added columns appear, renamed ones are linked to their history, and removed columns stay read-only so older reports still run. Dashboards that depend on a removed column get flagged instead of silently returning null.
We review your Postgres setup and the systems around it. Together we pick the first thing worth building.