Franco Patano

Aspiring pizzaiolo, data hippie, fun dad, thinker and writer, amateur DJ

[Ali Ghodsi @alighodsi]

𝐆𝐞𝐧𝐢𝐞 is now the most important way to do data analysis in Databricks. What's unique about it is its ability to extract semantics from your entire Lakehouse, enabling it to answer complex data questions that cripple agents without a deep data understanding. We've now added a Mobile version, added Unstructured data processing, as well as enabled it to operate on all your dashboards and notebooks. Check it out:

https://t.co/bqPvg2lYS7

[Ali Ghodsi @alighodsi]

It's not often the co-founders and I author blog posts together, but we thought this one is important so we took time to write this together with the Neon team. In this blog we outline why we think a new type of database will slowly take over and replace all existing databases, we call it the Lakebase. We cover the WHY and the WHAT here:

𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐨𝐟 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞
https://t.co/hL19TVTIaH

[Ali Ghodsi @alighodsi]

I now constantly get questions about the SAAS meltdown, role of AI, system of records etc. I don't have an answer to all these.

But I do know that we saw an acceleration in our business in Q2, Q3, and now finished the year with accelerating Q4.

The question is, why?

Short answer: AI. But the underlying reason is subtle. We are growing fast because we are finally removing the biggest bottleneck in data: the technical barrier to entry.

For years, if you didn’t know SQL, Python, you were locked out of the value chain. That has changed fundamentally with the 𝐆𝐞𝐧𝐢𝐞 𝐟𝐚𝐦𝐢𝐥𝐲, and it is the "secret sauce" behind our recent momentum:
• 𝐆𝐞𝐧𝐢𝐞: Analysts can query data without any SQL. I use this every day myself.
• 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐆𝐞𝐧𝐢𝐞: Builds end-to-end AI models for you, similar to Cursor for ML on your data.
• 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐆𝐞𝐧𝐢𝐞: Write Spark pipelines, does plumbing, troubleshooting.

We've been talking about DATA + AI democratization, but generative AI finally enabled it in a way that wasn't possible before. That's why we're seeing a market response.

Take 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬. We launched this serverless engine for agents and apps recently. At 8 months into its journey, its revenue is already 2x what our Data Warehouse product was at the same stage.

All this taken together, we ended up with the following stats for Q4:
🚀 $5.4B Revenue Run-Rate, growing >65% YoY
🚀 $1.4B AI Revenue Run-Rate
🚀 FCF Positive for the year
🚀 NRR >>140%

https://t.co/yq3riYyr8r

[Franco Patano @fpatano]

@netcapgirl We use it. Internally.

[Parviz @pdeyhim]

“Levenshtein distance vs. AI-Powered Similarity in Databricks SQL” by Parviz Deyhim https://t.co/lFdQbqxvXC

[Parviz @pdeyhim]

“Levenshtein Distance and WHERE wildcard in Databricks SQL” by Parviz Deyhim https://t.co/ShF96wkT1U

[Axios @axios]

@github @ashtom @inafried Catch @Databricks co-founder & CEO @alighodsi in conversation with @danprimack at the #AxiosAISummit SF.

Tune in tomorrow at 4:00pm PT: https://t.co/AqGMIbvCUu

[Jonathan Frankle @jefrankle]

A demo of our new synthetic evaluation product at @databricks!

[Databricks @databricks]

See how Sigma empowers non-technical banking users with Databricks Apps & Unity Catalog:

Seamless access to data
Self-service analytics for faster decisions
Scalable, governed insights

Apply these tools to your organization! https://t.co/65MEEdoflN

[Databricks @databricks]

We recently introduced new support for named parameter markers in the SQL editor - unifying parameters across queries, notebooks, and dashboards 🎉

Enhance your queries with secure, efficient, and flexible parameter handling. https://t.co/uz1tdRqSJV

[Databricks @databricks]

Over 7k customers use #DatabricksSQL as their data warehouse. We recently added new features & performance improvements that make it simpler, faster & lower cost. Including:
- AI-powered performance
- AI Assistant for SQL Analysts
- AI Functions updates

https://t.co/Rg8870WUIo

[Matei Zaharia @matei_zaharia]

Super excited about the new 1B and 3B Llama models and multimodal Llama! The cost-performance of open source AI is improving dramatically, and private, device-local AI is becoming practical with these models. Proud that @databricks is a launch partner.

[Mike Cornell @TXMikeC]

👀 As announced today at #FabConEurope, OneLake in #MicrosoftFabric can now work with data managed by @Databricks Unity Catalog. Great example of other tools building integrations on top of Unity Catalog to serve their users. https://t.co/n8BTDlTdzC

[Franco Patano @fpatano]

@laurenbalik Can someone ELI5? What does this mean, and why is it so bad? I don't understand why they would do this with 1.3 B in reserves and 891 M in free cash flow.
Doing stock buybacks seems like a way to prop up a dying stock artificially.
What can they invest in to recover?

[caio temer @canalCCore2]

If we built a neural network where the weights were lenses instead of vectors, for instance, and the input was light-shaped, the inference cost would be zero.

[Paul Graham @paulg]

I'm skeptical that technology has advanced so far yet, but I'm retweeting this because McKay is generally trustworthy.

[Franco Patano @fpatano]

@laurenbalik Almost like valuing a startup on slack community volume

[Franco Patano @fpatano]

What's the difference between Databricks and Snowflake?

https://t.co/2u8yywaEkA

[Franco Patano @fpatano]

I've said it before and will say it until people understand. SNOW is the biggest pump-and-dump scheme of our generation. @laurenbalik gets it.

[Steven Balik @laurenbalik]

Many have pinged me about Berkshire selling Snowflake $SNOW as part of 13-F season.

All of my dear children on X, listen up.

You'll learn more from this post than from tinkering around with DCF model assumptions.

You need to understand Snowflake Queen Linda Apsley.

Oh, you're a TMT analyst? You follow the tech and software spaces, do you? You have positions on Snowflake $SNOW but you don't know Linda Apsley?

Let's dive in. 👇

---

Linda put Snowflake into several marquee accounts Snowflake likes to market and show off - Capital One, Citi, and GEICO.

Berkshire owns GEICO. In fact, GEICO's CEO Todd Combs is also an investment manager at Berkshire and along with Ted Weschler is often touted as Warren Buffett's successor.

Now, it had been long known that Capital One was Snowflake's biggest customer leading up to Snowflake's IPO.

Capital One was spending so much money on Snowflake, that in the 2 fiscal years leading up to Snowflake's IPO they were 17% and 11% of all Snowflake revenue. (See Snowflake's S-1, below.)

I also note that Capital One's VC arm was an investor in Snowflake.

Big spender.

So on the Snowflake IPO in September 2020, what does Berkshire do? Two things.

1) They buy up a bunch of Snowflake stock.

2) They hire Linda Apsley, the person who had blown up Snowflake at Capital One, then went to Citi to do the same. She started as CTO at GEICO right in September, when Berkshire bought up Snowflake stock.

It's really not hard to get out a spreadsheet or a pen and paper and do some basic math of how this could benefit Berkshire.

If they fire even an incremental $10-20M a year into the Snowflake hole, and Snowflake trades at ridiculously high multiples, Berkshire gets to pump their equity by raiding the balance sheet of GEICO to finance their $SNOW spend.

The margin was this thin here in 2020 into 2021. Even an extra few million fired into the $SNOW hole on a quarter to beat a consensus estimates is enough to juice the stock trading at high revenue multiples.

And who better to hire at GEICO to run Snowflake spend than Linda Aspley? After all, she did the same thing for Capital One.

---

This is how $SNOW has always worked as a stock.

Many of Snowflake's bigger spenders (like Instacart $CART, where former Snowflake CEO Frank Slootman served on the Board along with Snowflake investors from D1 Capital and Sequoia) also have related party conflicts which are just a hair off from needing to be disclosed.

---

Now, with multiple compression, elevated interest rates, and many companies scaling back hiring the nth data engineer/scientist, Snowflake has leaned into this AI story, on which they will fail.

Snowflake can't compress their gross margins enough to make AI feasible for customers doing anything at scale.

Snowflake has the FBI knocking on their door about Snowflake's string of customer security incidents, not to mention a bipartisan effort from Republican Senator Josh Hawley and Democrat Senator Richard Blumenthal asking for detailed timelines, from Snowflake, about Snowflake's customer security issues.

I don't even think anything meaningful ever got accomplished at GEICO using Snowflake - one big smoke-and-mirrors show.

[Steven Balik @laurenbalik]

Many have pinged me about Berkshire selling Snowflake $SNOW as part of 13-F season.

All of my dear children on X, listen up.

You'll learn more from this post than from tinkering around with DCF model assumptions.

You need to understand Snowflake Queen Linda Apsley.

Oh, you're a TMT analyst? You follow the tech and software spaces, do you? You have positions on Snowflake $SNOW but you don't know Linda Apsley?

Let's dive in. 👇

---

Linda put Snowflake into several marquee accounts Snowflake likes to market and show off - Capital One, Citi, and GEICO.

Berkshire owns GEICO. In fact, GEICO's CEO Todd Combs is also an investment manager at Berkshire and along with Ted Weschler is often touted as Warren Buffett's successor.

Now, it had been long known that Capital One was Snowflake's biggest customer leading up to Snowflake's IPO.

Capital One was spending so much money on Snowflake, that in the 2 fiscal years leading up to Snowflake's IPO they were 17% and 11% of all Snowflake revenue. (See Snowflake's S-1, below.)

I also note that Capital One's VC arm was an investor in Snowflake.

Big spender.

So on the Snowflake IPO in September 2020, what does Berkshire do? Two things.

1) They buy up a bunch of Snowflake stock.

2) They hire Linda Apsley, the person who had blown up Snowflake at Capital One, then went to Citi to do the same. She started as CTO at GEICO right in September, when Berkshire bought up Snowflake stock.

It's really not hard to get out a spreadsheet or a pen and paper and do some basic math of how this could benefit Berkshire.

If they fire even an incremental $10-20M a year into the Snowflake hole, and Snowflake trades at ridiculously high multiples, Berkshire gets to pump their equity by raiding the balance sheet of GEICO to finance their $SNOW spend.

The margin was this thin here in 2020 into 2021. Even an extra few million fired into the $SNOW hole on a quarter to beat a consensus estimates is enough to juice the stock trading at high revenue multiples.

And who better to hire at GEICO to run Snowflake spend than Linda Aspley? After all, she did the same thing for Capital One.

---

This is how $SNOW has always worked as a stock.

Many of Snowflake's bigger spenders (like Instacart $CART, where former Snowflake CEO Frank Slootman served on the Board along with Snowflake investors from D1 Capital and Sequoia) also have related party conflicts which are just a hair off from needing to be disclosed.

---

Now, with multiple compression, elevated interest rates, and many companies scaling back hiring the nth data engineer/scientist, Snowflake has leaned into this AI story, on which they will fail.

Snowflake can't compress their gross margins enough to make AI feasible for customers doing anything at scale.

Snowflake has the FBI knocking on their door about Snowflake's string of customer security incidents, not to mention a bipartisan effort from Republican Senator Josh Hawley and Democrat Senator Richard Blumenthal asking for detailed timelines, from Snowflake, about Snowflake's customer security issues.

I don't even think anything meaningful ever got accomplished at GEICO using Snowflake - one big smoke-and-mirrors show.