At Snowflake, we are mobilizing the world's data.
For decades data has been stuck.
Businesses could not handle the volume,
They could not complete the workloads,
and they could not scale them out to execute concurrently.
And the systems available cost an absolute fortune, putting them out of reach for most.
At the same time, data volumes exploded under the influence of machine-generated data.
And for digital enterprises, a data-centric operating discipline is central to how they work.
It was a perfect storm--huge growth in need
with unmet progress at scale in performance and economics.
Pressure has been building for a long time.
Until now.
My name is Frank Slootman, and I'm the chairman and CEO of Snowflake.
I'm here with you today to talk about something new and cutting edge.
I've seen a lot in my years in software, but what gets me so excited about Snowflake
is building new technology that is at the leading edge of the industry.
Everything, from the business model to the architecture to
the platform to the new Cloud we are creating,
is entirely new, modern, and forward leaning.
Secondly, we're helping companies completely re-imagine
what they thought was possible with their data.
And we're just getting started.
So with that, let me dive in.
The public cloud platforms have laid a critical foundation
with its unlimited capacity for data and compute,
as well as business models that enable utility and elasticity.
But it wasn't enough. The public cloud is necessary but not sufficient by itself.
If it was, it would be unlikely that Snowflake would even exist.
Most cloud software out there is not designed for cloud-scale computing.
They have legacy designs. They come from machine-centric backgrounds.
They are simply hosted in the cloud, not designed for the cloud.
Most SAS companies that we know are that way.
Most of the software running in the cloud was not designed
for cloud computing at all.
It runs the same in the cloud or on-premise. There is no difference.
Legacy architectures have been crumbling under the strain of demand for analytics for a long time.
So what Snowflake did was design from the ground up and reimagine for cloud scale.
The Snowflake founders started over with a clean sheet of paper.
They also knew everything about legacy database architectures and what was in need of improvement.
The decision was made that Snowflake would not run anywhere other than in the public cloud.
The founders burned the ships behind them,
rather than try to straddle cloud and on-premise environments.
No hedging.
They went all in on cloud.
What was needed was a cloud-native architecture.
Now what do we mean by that?
For one, it had to innovate on multiple vectors at once for it to become an actual breakthrough.
You see those five vectors here in the visual.
Let's start with data.
We had to address the physics of accessing orders of magnitude greater volumes of data.
Data volume had to be considered unlimited,
without performance deteriorating, making it otherwise unusable.
Second, we had to spin up huge compute clusters but
also operate many clusters concurrently on the same data.
This gave rise to an architectural distinction that we're known for,
and that is the multi-cluster shared data architecture.
It effectively removed workload limitations from this entire class of software.
The idea of variable capacity has been a key driver in our growth.
No longer would you max out with a single cluster on capacity.
You can spin up as many clusters as you want, literally in seconds,
executing against the same data.
It is hard to keep a lid on this.
Third is the utility model, which in our case means consuming and paying by the machine second.
This dramatically increased our TAM,
because the market was no longer limited to deep-pocket customers.
We are, of course, following the public cloud business model,
and we believe all software will eventually be charged for this way.
No more licensing of use rights on a subscription basis.
We are not a SAS company. We are a consumption company.
And we are a next-generation cloud software company.
Fourth is the elasticity of capacity.
Elasticity ensures a customer can commandeer
an enormous amount of capacity, storage, and especially compute for short periods of time,
which is what many of our customers do, especially at the end of a quarter.
Huge jobs are run,
but there's no need for anywhere near that much firepower during the rest of the quarter.
They would never ever buy that much.
But using it for a couple of hours at the end of a quarter
makes total sense.
Finally, an often overlooked aspect of what Snowflake has done is ease of use.
The industry actually went in the opposite direction for a decade,
with the likes of Spark and Hadoop and other Open Source initiatives.
Things got incredibly complicated for not much gain.
Snowflake is a deceptively simple platform.
It is completely self-managing, and it retired the role of the database administrator.
Many customers are amazed how Snowflake lowered the bar
for moderately skilled SQL engineers.
It scales down to two men and a dog with a handful of files,
and it scales up to the largest data estates in the world.
I personally have never seen this in enterprise software.
Snowflake is Snowflake regardless of what cloud or cloud region it runs on.
Then the world beat a path to our door.
Going back, the company burst onto the scene with its cloud-native architecture
and it positioned itself as the data warehouse built for the cloud.
That was a good initial way to connect with opportunity
because the product was so highly differentiated
compared to legacy data warehouse approaches.
And it was easy to benchmark workloads, like in-desk jobs and complex queries.
The selling motion obviously worked.
Last year, we decided to expand our positioning from data warehousing
to a broader set of workloads.
This was overdue. Customers were already seeing us as a cloud data platform, more broadly capable.
And it also signaled to customers where we were headed--a much more strategic positioning
and a platform capable of accommodating a range of workloads.
It effectively became a reinvention of the database management system.
That is not the end game, though.
We have set our sights on developing what we call the Data Cloud,
probably one of the more original ideas in software to come along.
I will discuss the Data Cloud in more detail in just a moment here.
I work for Square. I've been with Square about eight years.
I am the head of the platform engineering team,
and we are the ones who did the migration from an on-premise data system to Snowflake.
Snowflake already operates what I consider a world-class data platform.
We went from an on-premise data system
to Snowflake in just a few months--a few very hectic intense months.
The greatest thing about Snowflake is our ability to scale it instantly.
With our on-premise data system, we had to choose at the beginning of each month
whether we were going to reconcile our books or whether marketing was able to run.
Now we have unlocked the ability for all of the groups to perform new and different data analysis.
It has helped us expand and grow the business, and has helped us to find insights
that we never would have found with our on-prem data system.
Data analytics is already changing the world.
What I think is amazing about Snowflake
is the ability to share data
instantly and transparently between organizations, between data sets.
And I predict that the insights that we gain over time will just amaze us and astound us.
Let's talk about these workload expansions first.
The Cloud Data platform shifted last year from a single workload orientation of data warehousing
to a broader set. There are six to be precise.
Customers mostly saw it as evolutionary.
They thought it made total sense.
We were already well underway to a broadly capable data platform.
As a company, though, we had not fully operationalized these workloads as markets.
We have now leaned into that.
This, of course, expands our TAM and engages us with customers
in many more conversations than before.
Data engineering is our second biggest workload consumer.
It refers to the prep
transformation and ingest processes prior to landing data on the platform.
A substantial portion of our overall consumption is already in this bucket.
The spent in this category is huge.
Entire companies and industries, such as the ETL crowd, live here.
We have, in the last year, signaled that we are developing products,
such as our Snowpipes, that are now in broad use.
We have new initiatives in flight that will allow customers
to bring their Spark platform operations onto Snowflake, as well.
Data lake is how many customers view us and use us because of our separation of storage and compute.
We are actually emblematic of the data lake idea.
We also have broad support for different data types, from structured to unstructured
external tables and functions.
Lately, we have also introduced global search, which is a key item for this workload.
We also see traction with security data lakes on Snowflake,
which is an application of the data lake idea to cyber security.
Data warehousing is our base business.
Data warehousing refers to analytical data processing workloads,
not the migration of data from a specific data warehouse platform.
Numerous analytical data workloads run on generic database platforms,
not specialized data warehouse platform products.
Data sciences--this refers to analytics machine learning and artificial intelligence.
We have a data analyst capability, which we built from an acquired technology
called Snowsites,
which is our embedded data analyst workbench and visualization offering.
Data applications refers to software companies
who embed Snowflake as the data layer in their stack.
This already is a substantial business for Snowflake.
We just held our first conference for builders. It was a virtual event called Snowflake Build.
We had 3,200 developers attend the event.
Then there is the Data Exchange, based on our data-sharing architecture.
Data sharing is inherent in the Snowflake architecture, not a bolt-on separate product,
and we see it as the single most differentiating and strategic thing we do.
Data sharing is what enables the Data Cloud.
We recently announced our Data Cloud strategy.
The notion of a Data Cloud is new, the way we define it.
The problem we are addressing with Data Cloud is the siloing and bunkering of data.
Data is literally living inside machines and clusters in public cloud regions
and in application clouds, like Salesforce, Servicenow, Workday, SAP, as well as on-premise.
As long as data is fragmented and segregated, there is no way to mobilize it,
get the value and unlock the insights and signals from it.
Our first conversation with Salesforce happened because they noticed that Snowflake
was a top destination for Salesforce data, and that peaked their interest.
Customers are already pumping data from all over into Snowflake
to break these silos and bunkers.
It's an onerous, time-consuming,
and expensive process to move and transform data.
The basic approach of Snowflake data sharing is that once data is in Snowflake somewhere,
it becomes readily accessible to anybody else on Snowflake--across clouds and across geographies.
Data sharing can be one-on-one between two parties, or many-to-many,
such as on the Snowflake Data Marketplace,
which is a public data exchange where data can be discovered,
explored, and accessed by any Snowflake user .
The key thing here is that, with the Data Marketplace,
data does not generally need to be copied or moved.
It stays where it is at.
This has numerous advantages.
The provider does not have to relinquish physical custody of the data.
This can be a big thing from a data governance perspective.
Second, powered by our data sharing capability, the latency is always zero.
The moment data changes on the provider's side, it is reflected on the consumer side elsewhere.
Data sharing turned Snowflake into a giant federation of data--a Data Cloud,
in which everybody who has a Snowflake account is a participant.
It comes with having a Snowflake account.
Many prominent data providers make their data available on our Data Marketplace.
So they can get exposure to all Snowflake accounts out there.
As an example, we got a huge uptake on COVID-19 incident and fatality data in our customer base.
This data set is provided by an external data provider called Starschema.
More than half our customers do this already.
We're very proud to have partnered with Snowflake on creating, collating,
and distributing the Starschema COVID-19 data set.
I think it's a unique data set.
What we add as a value to this is, first of all,
distributing it through Snowflake's incredibly solid
and well-controlled and well-maintained Data Exchange facility,
while at the same time,
we're also ensuring that this data is unified along the same identifiers,
so that multiple sources of information can be brought in.
And the boost that Snowflake helps us provide
is being able to give a curated, unified, and correlated data set.
And this can then lead to more intricate analyses.
For instance, we know case counts, but on their own, they mean relatively little.
With our data, you can map this against the
number of staffed ICU beds, by county, and see where there's a particularly high risk
that the health care system will be overwhelmed by the influx of cases.
This is a unique time and probably a historical time for computational epidemiology,
as well as for data science in general.
And we're very happy that Snowflake has joined us in this endeavor
to distribute this data openly and for free to a wide range of public and private parties.
We see the beginnings of powerful data network effects.
Another reason to be on Snowflake becomes whoever else has data on Snowflake.
Both data providers and data consumers need to be on Snowflake for this reason.
That is the network effect.
The importance of the Data Cloud for Snowflake is that it's not just about
staying ahead in the arms race of cloud data architectures and speed and feeds.
The Data Cloud changes those dynamics to a completely
different realm--the realm of content and networks.
Our multi- and cross-cloud architecture is a key consideration for customers
who aspire to a multi-cloud posturing strategy, which in our experience,
is the vast majority of the enterprise audience.
Snowflake is quite a formidable ecosystem of partners.
We have the traditional implementation crowd of GSIs and SIs, of course.
Some of the more prominent ones are Deloitte, Cognizant, and Accenture.
We have a large and varied group of technology partners--
the ETL crowd, the analytics companies, the ML and AI gang, and many others.
Snowflake is at heart a database platform, so it is expected that we would attract
a diverse partner ecosystem.
What's different here are the data application builders,
data providers, and the data service providers.
Those are specific to our Cloud Data platform.
I can say that Snowflake has by far the best
technology team I have seen or worked with in my career .
It fills me with excitement to have a group of people like this, and what we will be able to do.
These folks truly invent technology.
We are not an application company.
Our founders Benoit ,Thierry, and Marcin are obviously brilliant technologists,
but they're also fine, common sense, everyday members of the team.
We have reimagined data management for the cloud with a cloud-native architecture
that has allowed us to innovate across multiple vectors.
We've made our platform incredibly approachable
through a consumption model, ease of use, and elasticity that expands our market,
we are creating the Data Cloud--a new class of cloud,
which is driving powerful network effects in our business.
And finally, we have a proven and experienced team that is built to scale.
As exciting as our momentum may be to this point,
we are convinced we have just started this journey,
as we give rise to the Data Cloud on our mission to mobilize the world's data.