Databricks

Uber

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.

Uber

Uber is a marketplace that connects riders with drivers. You request a ride through the app, the nearest driver accepts, picks you up, drops you off, and Uber takes a cut — typically 25-30% of the fare.

The driver keeps the rest. Uber doesn't own any cars.

They don't employ any drivers. They built a $150 billion company by being the middleman with a really good app.

The model expanded into Uber Eats (food delivery, same concept — restaurants cook, drivers deliver, Uber takes a cut), Uber Freight (connecting truckers with shippers), and advertising. The advertising business is quietly enormous — Uber has data on where millions of people go every day, and brands will pay handsomely for that.

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.

Uber

The idea started in Paris in December 2008. Travis Kalanick and Garrett Camp were at the LeWeb tech conference and couldn't find a cab.

Camp had been obsessing over the idea of summoning a car with your phone. He bought the domain UberCab.com, built a prototype, and recruited Kalanick to help run it.

The first version launched in San Francisco in 2010 as a black car service — not the cheap rideshare everyone knows today. You'd tap a button, a Lincoln Town Car would show up, and it cost about 1.5x a regular taxi.

Ryan Graves answered a tweet from Kalanick looking for an "entrepreneurial product manager" and became employee number one. He ran operations while Kalanick was still finishing up another startup.

Graves would later become CEO briefly before handing the reins to Kalanick. The app launched with just a handful of cars in San Francisco.

It worked so well that riders couldn't shut up about it.

The real inflection point came in 2012 when they launched UberX — regular people driving their own cars at prices cheaper than taxis. That one decision turned Uber from a luxury black car service into a verb.

Within two years, UberX was available in hundreds of cities and the word "Uber" had entered the dictionary.

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.

Uber

Uber's early growth strategy was beautifully ruthless. They'd roll into a new city, launch without asking permission, and deal with the regulatory fallout later.

They called it "Travis's Law" — it's easier to ask forgiveness than permission.

The playbook was simple: launch in a new city, give massive discounts to riders (sometimes completely free rides), pay drivers signing bonuses and guaranteed hourly rates, and flood the zone until the city was hooked. Then slowly raise prices and cut driver incentives once the market was locked.

They burned billions doing this but it worked — by 2016 Uber was in 500+ cities across 70 countries.

They also weaponized word of mouth with referral codes. Every rider could give free rides to friends.

Every new driver got a bonus for signing up. The viral loop was insane.

At peak growth, Uber was adding a new city every day.

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.

Uber

Where do you even start? Uber might have faced more simultaneous existential crises than any company in history.

Regulatory wars. Taxi unions, city governments, and entire countries tried to shut Uber down.

London revoked their license. France arrested two executives.

Uber was banned, unbanned, re-banned, and sued in dozens of jurisdictions simultaneously.

The toxic culture. In 2017, former engineer Susan Fowler published a blog post describing rampant sexual harassment, discrimination, and HR cover-ups at Uber.

It went nuclear. Investigation after investigation followed.

Board members resigned. Executives were fired.

Travis Kalanick's ouster. After the culture scandals, a leaked video of him berating an Uber driver, and a federal investigation into stolen trade secrets from Google's self-driving car unit Waymo, the board forced Kalanick to resign as CEO in June 2017.

Dara Khosrowshahi came in from Expedia to clean things up.

The cash burn was legendary. Uber lost $8.5 billion in 2019 alone.

They subsidized rides so heavily that riders were paying less than the actual cost of the trip. The company didn't turn its first operating profit until Q3 2023 — fourteen years after founding.

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.

Uber

Uber Rides is the core product — get from A to B in someone else's car. UberX is the standard option, Uber Black is the premium black car tier, UberXL fits bigger groups, and Uber Reserve lets you schedule rides in advance.

Uber Eats is the food delivery arm and competes directly with DoorDash and Grubhub. Uber Freight is the logistics play — basically Uber for semi-trucks, connecting carriers with shippers.

Uber for Business lets companies manage employee rides and meals. Uber now also offers package delivery, grocery delivery, and even boat rides in some cities.

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.

Uber

Benchmark Capital, First Round Capital, Menlo Ventures, Jeff Bezos, Goldman Sachs, Google Ventures, Saudi Arabia's Public Investment Fund, SoftBank, Toyota, PayPal co-founder Peter Thiel, Tencent