Databricks

Dandy

Databricks

Databricks runs on a consumption-based pricing model. Companies pay for the compute and storage they actually use on the Databricks platform, measured in "Databricks Units" (DBUs).

The more data you process, the more you pay. This is brilliant because it means revenue grows automatically as customers' data volumes grow — which in the age of AI, they always do.

The platform runs on top of the major cloud providers — AWS, Azure, and Google Cloud. Databricks doesn't own servers.

They're a software layer that makes those clouds dramatically more useful for data work. They take a margin on top of the underlying cloud compute costs, essentially acting as a "toll booth" between companies and their data.

They also pioneered the "lakehouse" architecture — a mashup of data warehouses (structured, fast querying) and data lakes (cheap, handles any data format). Before Databricks, companies had to maintain both.

The lakehouse collapses them into one system. This isn't just clever marketing — it genuinely saves enterprises millions in duplicate infrastructure.

Dandy

Vertical SaaS plus manufacturing. Dandy provides dental practices with intraoral scanners (often subsidized or free to eliminate the switching cost), cloud-based software for managing cases, and its own network of digital dental labs that manufacture the final restorations.

Dentists pay per case — each crown, bridge, veneer, or implant restoration is priced individually. The margin comes from manufacturing efficiency: digital workflows are faster, more precise, and require less manual labor than traditional hand-sculpted methods.

As volume grows, Dandy's labs get more efficient and per-unit costs drop. It's the classic razor-and-blades model — give away the scanner, make money on every restoration.

Databricks

Databricks started as a research project at UC Berkeley's AMPLab around 2009. Matei Zaharia, a PhD student, was frustrated with how slow Hadoop MapReduce was for iterative machine learning workloads.

His answer was Apache Spark — an open-source engine that could process data up to 100x faster than MapReduce by keeping data in memory instead of writing to disk after every step.

Spark took off fast in the open-source community. By 2013, it was the most active open-source project in big data.

Zaharia and six Berkeley colleagues — Ali Ghodsi, Andy Konwinski, Arsalan Tavakoli-Shiraji, Ion Stoica, Patrick Wendell, and Reynold Xin — decided to build a company around it. They incorporated Databricks in 2013 with the idea that Spark was powerful but brutally hard to set up and manage.

The company would offer a managed cloud platform that made Spark accessible to data teams who weren't distributed systems engineers.

Their first product was essentially "Spark as a service" — a collaborative notebook environment where data scientists and engineers could write Spark jobs without managing clusters. The bet was that enterprises had massive data problems but not enough PhDs to solve them.

They were right.

Dandy

Henry Stott was a repeat entrepreneur who had previously co-founded a tech company in the UK. When he looked at the dental industry, he saw a $15 billion lab market that was shockingly analog.

Here's how it worked: a dentist jams a tray of gooey putty into your mouth, waits for it to harden, mails the physical mold to a dental lab, where a technician hand-sculpts your crown out of ceramic. Turnaround: 2 to 3 weeks.

Error rate: high. Patient experience: miserable.

The technology to do this digitally had existed for years — 3D intraoral scanners, CAD/CAM software, CNC milling machines — but nobody had stitched it into a seamless end-to-end platform for the average dental practice. Stott started Dandy in 2020 to be that platform.

Provide the scanner, build the software, run the lab — and make it so easy that any dentist can switch from analog to digital without changing how they practice.

Databricks

Databricks grew by being genuinely useful before being profitable. They contributed massively to Apache Spark's open-source ecosystem, which meant thousands of companies were already using Spark when Databricks offered to manage it for them.

The open-source-to-enterprise pipeline is the most powerful go-to-market motion in software.

They also bet big on partnerships. The Microsoft partnership was transformational — Azure Databricks became a first-party service on Azure, meaning Microsoft's sales force was effectively selling Databricks to every enterprise customer.

That single deal probably added billions in annual recurring revenue.

Acquisitions were strategic and well-timed. MosaicML in 2023 for $1.3 billion gave them proprietary AI training capabilities right when every enterprise wanted to build custom AI models.

Tabular in 2024 brought the creators of Apache Iceberg, another critical open-source data format. They bought the talent and the technology simultaneously.

Dandy

Land-and-expand with dental practices. Dandy gives practices the scanner for free or at heavy discount, which eliminates the biggest barrier to switching from analog.

Once a practice starts submitting digital scans, they become recurring revenue — every patient who needs a crown is a Dandy order. Sales team targets mid-size practices (3 to 10 dentists) that are high-volume but haven't invested in digital yet.

Referral programs where existing dentists recommend Dandy to colleagues. Geographic density strategy — build lab capacity in a region, then saturate practices nearby to optimize logistics and turnaround times.

Content marketing educating dentists on why digital is better, faster, and more profitable than analog workflows.

Databricks

The elephant in the room is Snowflake. Both companies want to be the single platform where enterprises do all their data work, and the overlap is growing fast.

Snowflake started in SQL analytics and is pushing into data engineering and ML. Databricks started in data engineering and ML and is pushing into SQL analytics.

The collision is inevitable and expensive — both are spending billions on sales and R&D.

There's also the cloud provider threat. AWS, Azure, and Google Cloud all have their own data analytics services and could theoretically squeeze Databricks by making their native tools better or cheaper.

Databricks runs ON these clouds, which means their biggest partners are also their biggest potential competitors. It's the classic platform risk problem.

So far, Databricks has stayed ahead by innovating faster than the cloud providers' internal teams, but it's a race that never ends.

Dandy

Dental practices are notoriously resistant to change — many dentists have used the same lab for 20 years and switching feels risky. The scanner hardware is expensive to subsidize at scale, creating a capital-intensive land grab.

Quality control across distributed manufacturing is hard — a crown that doesn't fit means a remake, an unhappy patient, and a dentist who might switch back to their old lab. Competition from established digital players like Align Technology and legacy lab companies investing in their own digital capabilities.

The dental industry is fragmented — 200,000+ practices in the US, mostly small businesses, which means enterprise-style sales don't work. Each practice is its own decision maker with its own habits.

Databricks

Unity Catalog — a universal governance layer that lets companies manage permissions, lineage, and access control across all their data and AI assets in one place. Delta Lake — an open-source storage layer that brings reliability to data lakes with ACID transactions, schema enforcement, and time travel (yes, you can query your data as it existed at any point in the past).

Databricks SQL — a serverless SQL analytics product that competes directly with Snowflake on their home turf. Mosaic AI — their machine learning and generative AI platform, supercharged after acquiring MosaicML in 2023 for $1.3 billion.

Databricks Notebooks — collaborative workspaces where data teams write code, visualize results, and build pipelines together in real time.

Dandy

Dandy Scanner — provided to dental practices, captures a full 3D digital impression of the patient's mouth in minutes. No more putty molds.

Cloud-based case management platform where dentists submit scans, approve designs, and track orders. AI-powered restoration design that generates crown and veneer designs automatically from 3D scans, reducing turnaround from weeks to days.

Digital dental lab network with automated CNC milling and 3D printing for manufacturing restorations. Shade matching technology using AI to color-match restorations to surrounding teeth.

Integration with practice management software so cases flow seamlessly from scan to delivery.

Databricks

Andreessen Horowitz led multiple early rounds and has been the longest-standing institutional backer. Microsoft made a massive strategic investment alongside the Azure Databricks partnership.

T. Rowe Price, Tiger Global, and Franklin Templeton participated in later growth rounds.

NEA was an early investor. The $10 billion Series J in 2024 valued the company at $62 billion and was led by Thrive Capital with participation from Andreessen Horowitz, DST Global, GIC, Insight Partners, and WCM Investment Management.

Dandy

Investors include Bessemer Venture Partners, IVP, DST Global, and IA Ventures. Series C in 2023 valued the company at approximately $1.8 billion.