Data Panda brings the MongoDB 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.
MongoDB was founded in 2007 and is the dominant document-store NoSQL database. Its JSON-like documents, flexible schemas, horizontal scaling and native change streams made it the default choice for modern web apps, product catalogs, IoT platforms and event-heavy backends. MongoDB Atlas runs it as a managed cloud service across AWS, Azure and GCP.
The point of pulling MongoDB into a warehouse is that document data is hard to join to the rest of the business in its native shape. A user record with an embedded array of orders is convenient for the app and awkward for the CFO. In a warehouse, we flatten and version the document schema into SQL-queryable tables, stream changes via MongoDB's change streams, and join the result to Salesforce, Stripe and the accounting ledger. Reporting stops hitting the live Atlas cluster and starts reflecting business reality across systems.
Nested JSON flattened into SQL tables, joined to CRM and finance.
Let MongoDB changes fire actions across the rest of the stack.
Turn flexible document data into scoring that makes sense to SQL users.
Internal tools on MongoDB data without exposing Atlas credentials.
A list of concrete reports, automations and AI features we have built on MongoDB data. Pick the one that matches your situation.
How do we report on MongoDB without writing Mongo aggregations?
Documents are flattened into SQL tables in the warehouse with nested arrays handled as related tables. Business users query with SQL, ids align with CRM and Stripe, and no one needs the Mongo aggregation pipeline to answer a finance question.
What happens when the application adds a new field or changes a type?
Schema drift is tracked per collection. New fields appear as columns on the next sync, renamed fields are mapped to their history, and type changes are versioned so old queries still run. The change is surfaced before it breaks a downstream dashboard.
How do we pull MongoDB without hitting production?
Change streams against a secondary replica are the default, so read load never touches the primary Atlas node. For smaller collections, scheduled incremental sync is available. The load profile is tuned to the tenant.
App-generated revenue and usage data joined to the accounting ledger, without developer time for every export. Transactional and subscription revenue tie back to the same document customer record.
Product usage and feature adoption on every CRM account, sourced from MongoDB documents. Reps see the customer about to expand before the renewal call, in SQL they can query without a developer.
Collection schema drift, data-quality gaps and change-stream throughput monitored in one place. Reporting becomes part of the deploy check instead of the first thing that breaks after a migration.
User and account documents, plus key event streams, push into HubSpot as contacts, companies and timeline events. CS and sales see app behaviour on the record in real time, without product-team exports.
MongoDB user ids resolve to the Stripe customer and subscription they belong to, so app behaviour and billing state live on one warehouse record. Churn and upsell models read the same key.
Feature usage and product events aggregated from MongoDB push onto Salesforce accounts as fields and timeline activity. Account executives see adoption and churn risk on the same record as pipeline.
Invoices generated in the MongoDB-backed application post to Exact Online with customer, VAT and ledger coding resolved. The app keeps its own invoicing logic and finance still gets clean sales journal entries.
You keep the reporting tool you already have. We connect it to the warehouse where your MongoDB 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.
Change streams against a secondary replica are the default, with incremental cursor-based sync as a fallback where change streams are not enabled. Both paths land in the same warehouse tables and are deduplicated on document id.
Documents are flattened into one or more SQL tables per collection, with nested arrays modelled as related tables. Schema is versioned so new fields appear, renamed fields are mapped and type changes do not silently break reports.
We review your MongoDB setup and the systems around it. Together we pick the first thing worth building.