Databricks

Pinterest

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.

Pinterest

Pinterest makes money through advertising — specifically through "Promoted Pins" that look nearly identical to organic content. This is the magic of Pinterest's business model: ads don't interrupt the experience because the experience IS discovering products and ideas.

A promoted pin for a kitchen knife set appears right alongside organic pins of kitchen designs. The user doesn't distinguish between "ad" and "content" because both serve the same purpose.

Shopping ads are the fastest-growing segment. Brands upload their product catalogs, Pinterest matches products to user searches and boards, and users can buy directly through the platform or click through to the retailer's site.

Pinterest gets paid per click or per thousand impressions.

Revenue reached $3.65 billion in 2024, up from $3.05 billion the year before, and 2024 was its first genuinely profitable year. Average revenue per user is growing but still well below Meta's — the upside is enormous if Pinterest can close that gap.

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.

Pinterest

Ben Silbermann was a former Google ad operations employee who quit in 2008 to build apps. His first attempt was an iPhone app called Tote — essentially a mobile catalog that let women browse and bookmark products from fashion retailers.

Nobody downloaded it. But Silbermann noticed something in the data: users were saving products obsessively.

The collecting behavior was more interesting than the shopping behavior.

He teamed up with Paul Sciarra, a classmate from Yale, and Evan Sharp, a designer who was studying architecture at Columbia. Together they built Pinterest — a visual bookmarking tool that let people "pin" images from around the internet to organized boards.

Think of it as a digital mood board that anyone could make.

Pinterest launched as an invite-only beta in March 2010. Growth was painfully slow at first.

Silbermann personally wrote to the first 5,000 users, giving them his phone number and asking what they wanted. The early community was overwhelmingly women interested in home decor, fashion, recipes, and DIY projects.

By 2011, Time magazine named Pinterest one of the 50 best websites. By 2012, it was the fastest site in history to reach 10 million unique monthly visitors.

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.

Pinterest

Pinterest grew organically through women sharing boards with each other. The weddings use case was the killer app — brides-to-be would create boards for dresses, venues, flowers, and invitations, then share them with their wedding parties.

That viral loop drove millions of signups.

SEO is the secret weapon. Pinterest pages rank extremely well in Google Image Search.

Someone searching "modern living room ideas" often sees Pinterest results on page one. That drives a large stream of organic traffic from people who were not looking for Pinterest at all.

Unlike other social platforms, which compete with Google for attention, Pinterest quietly benefits from it.

The shopping pivot has been the growth unlock. Under CEO Bill Ready (former Google and PayPal executive who took over in 2022), Pinterest aggressively invested in shopping features — catalog integrations, buyable pins, merchant verification, and visual search for products.

The thesis: Pinterest users are already in a shopping mindset, so removing friction between "I like this" and "I bought this" is the straightforward path to revenue growth.

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.

Pinterest

Pinterest's demographics are both an advantage and a limitation. The platform skews heavily female (over 60% women) and is strongest in home, fashion, food, and weddings.

Expanding beyond these categories to attract male users, younger demographics, and different use cases has been slow.

Competition for ad dollars is fierce. Pinterest competes with Meta, Google, TikTok, and Amazon for advertising budgets.

Most advertisers allocate the bulk of their spend to Meta and Google first, then consider others. Pinterest needs to prove its return on ad spend is competitive to win larger budget allocations.

Creator economy is underdeveloped. While Instagram, TikTok, and YouTube have massive creator ecosystems with monetization tools, Pinterest has historically been about content discovery, not content creation.

Users pin other people's content — the original creators often don't even know their work is on Pinterest. Building a creator program and driving original content creation on the platform has been a recent focus but lags far behind competitors.

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.

Pinterest

Pinterest Home Feed — the core discovery surface showing personalized pins based on user interests, boards, and search history. Pinterest Lens — visual search technology that lets users take a photo of any object and find similar items to buy on Pinterest.

Pinterest Shopping — integrated e-commerce allowing users to browse and purchase products directly from pins linked to retailer catalogs. Pinterest Boards — the organizing system where users save and categorize pins into collections, used for wedding planning, home renovation, recipes, fashion, and more.

Pinterest Shuffles — a collage-making app for Gen Z users to create aesthetic mood boards, driving younger user adoption.

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 later rounds multiplying that several times over with participation from Andreessen Horowitz, DST Global, GIC, Insight Partners, and WCM Investment Management.

Pinterest

Bessemer Venture Partners, FirstMark Capital, and Andreessen Horowitz were early investors. Fidelity and Valiant Capital participated in later rounds.

Rakuten invested strategically. The April 2019 IPO raised $1.4 billion at a $12.7 billion valuation.

Elliott Management, the activist investor, took a large stake in 2022 and pushed for operational improvements that contributed to the company's path to profitability.