Data Panda lifts data from your CRM, ERP, ecommerce, finance and product systems into Snowflake on a known schedule. Once it lives in one warehouse, your BI tools, your Cortex AI workloads and your internal apps all read the same numbers instead of each one stitching its own version.
Snowflake was founded in 2012 by Benoit Dageville, Thierry Cruanes and Marcin Zukowski, with Bob Muglia joining as CEO the next year. It went public on the NYSE in September 2020 under the ticker $SNOW, raising roughly $3.4 billion in what was at the time the largest software IPO on record. Headquarters sit in Bozeman, Montana, and the platform runs natively on AWS, Azure and Google Cloud, with the same SQL surface across all three.
The architectural choice that made Snowflake what it is: storage and compute scale independently. Tables live once in cloud object storage, and any number of virtual warehouses can read or write them in parallel without contending for the same hardware. That decoupling is what gives Snowflake near-zero-copy cloning for dev and test environments, time travel for point-in-time recovery, and the ability to spin a warehouse up for a single ETL run and shut it back down. Snowpark added Python, Java and Scala execution next to the SQL engine, and Cortex put LLM and ML functions inside the warehouse so Cortex Analyst, document AI and embedding queries run against governed data without copying it out. The flip side, which every BE/NL Snowflake account learns within a quarter: virtual warehouse cost runs away fast when poorly tuned queries fire on auto-resume. We land the data, model it once, and size the warehouses so the bill matches the workload.
BI tools read curated Snowflake schemas instead of stitching across operational systems.
Data lands in Snowflake on a schedule that matches the business, not the loudest dashboard.
Cortex LLM, ML and document functions run inside the warehouse on the same tables BI reads.
Internal apps, customer portals and partner data exchanges read the same Snowflake account.
A list of concrete reports, automations and AI features we have built on Snowflake data. Pick the one that matches your situation.
Why is our Snowflake credit bill growing faster than our data?
Almost always because virtual warehouses auto-resume on poorly tuned queries that scan whole tables instead of partitions. A handful of dashboards or notebooks running on XL warehouses can double the monthly bill in a quarter. Right-sizing the warehouses per workload, clustering the largest tables and reviewing the top-cost queries usually cuts the trend without touching the BI side.
We have raw tables in Snowflake but every team writes their own ELT. How do we consolidate?
Map which schemas duplicate the same business entities, then promote one curated layer with revenue, customer and product defined once. Team-specific marts read from the curated layer instead of the raw tables. Snowflake's role-based access keeps the raw and curated zones separate, and the credit bill drops because everyone stops scanning the raw history.
Should we use Cortex AI features inside Snowflake or pull data out to a separate AI stack?
If the question runs on warehouse-grade data, Cortex usually wins on governance and on round-trip cost. LLM functions, document AI and embeddings stay inside the same role and audit boundary as the tables. Pulling data out makes sense when the model has to live somewhere specific, but most BE/NL Snowflake accounts find the in-warehouse path quicker to govern and easier to bill.
The CFO gets a Snowflake-fed close pack that ties to the boekhouding. Revenue, margin and AR carry one definition, sourced from the same warehouse the sales board reads, so the close stops being three people reconciling exports.
Sales leaders see pipeline, forecast and quota next to invoiced revenue and product usage on warehouse-grade data. The same numbers travel to the QBR pack, the standup and the steering committee without copy-paste from a spreadsheet.
Operations and data leads track Snowflake virtual warehouse usage, query cost and refresh runtime in one view. The credit bill becomes predictable, and the curated layer stops growing sideways with team-specific copies of the same tables.
You keep the reporting tool you already have. We connect it to the warehouse where your Snowflake 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.
Three habits: virtual warehouses sized per workload instead of one XL handling everything, query review on the top-cost statements every cycle, and clustering on the largest fact tables so partition pruning fires properly. Most BE/NL accounts that have been running for a year find the bill drops materially in the first month of cleanup, without touching the dashboards on top.
Yes, and it is one of the cleaner ways to add AI without moving data. Cortex Analyst answers natural-language questions on curated schemas, document AI parses PDFs into warehouse rows, and the embedding and LLM functions run inside the same role-based access as the tables themselves. The curated layer we build is exactly what those features want to read.
Not for what we do. The SQL surface is the same on all three clouds, and the loading patterns we use work against any of them. Account region matters more than cloud choice for latency to your BI users and for data-residency questions, especially in BE/NL where keeping data in-EU is often a procurement requirement.
We review your Snowflake setup and the systems around it. Together we pick the first thing worth building.