9c7f7950-f8cd-4f16-ac46-5f9dbeeb1aa1_Snowflake+Premium+Analysis+v1.pdf
Snowflake Premium Analysis:
You can find my previous analysis of Snowflake here. previous analysis of Snowflake here.
After being nearly 50% off from all-time highs, Snowflake still has a neck-breaking valuation. In this premium analysis, we spend time digging into the financials and valuation as this is a critical piece for this particular company. We also discuss product, especially as it relates to the upcoming Databricks public offering, and a few reasons why Snowflake outpaces this important competitor in terms of revenue growth. We also discuss areas where Databricks is stronger and what Snowflake must do to maintain its lead in the future.
Best-in-Show Key Metrics:
Snowflake is a leading cloud software company in terms of revenue growth, net retention rate and remaining performance obligations (RPOs). Like all cloud software, Snowflake is declining year-over-year as the base increases.
Snowflake went public with 121% year-over-year growth, which has settled to 117% in the most recent quarter. The outlook for Snowflake is growth of 82% at the midpoint with adjusted operating margins of (19%) up from adjusted operating margins of (38%) last year.
Snowflake's two areas of strength include a net retention rate that improved sequentially from 158% to 168% in the most recent quarter. The reacceleration is important as it shows customer satisfaction and is a number that tends to decelerate.
Remaining performance obligations (RPO) is also very high at 213% year-over-year, with $1.3 billion in the pipeline. This represents the revenue that is contracted but not yet realized. The number is higher than forward revenue guidance because the future period may require more than a year. Regardless, the point is it provides a glimpse as to Snowflake’s strength in the future and 200%+ growth shows customer satisfaction. The topic of Snowflake’s future strength, particularly 3-5 years out, is discussed in the valuation section below.
The company grew Fortune 500 customers by 46% from 127 to 186. These customers tend to remain loyal due to their size. I also pointed out Snowflake's impressive growth in customers with product revenue greater than $1 million in my previous analysis.
At the time of IPO the accounts >$1M had grown 56% compared to 22% in the year-ago period. The most recent growth shows an acceleration at 88%. The company is also a usage-based model rather than subscription. The more the world relies on data, the more revenue Snowflake will make. These larger accounts also matter more in a usage-based model.
It’s important to note that Snowflake is bleeding on the bottom line on a GAAP basis with sales and marketing costs equal to its revenue. Sales and marketing costs were at 111% of revenue in FY2020 and at 81% of revenue FY2021. These costs are high due to growing the sales and marketing departments. The company plans to hire 1,200 new employees this year with an emphasis on these two departments.
The operating expenses are nearly 200% of total revenue after R&D and G&A. The translation is that Snowflake is in the middle of a land grab. I imagine the aggressive sales and marketing budget strategy is that the high switching costs of data warehousing solutions will support the LTV needed. In other words, the strategy of establishing customer relationships at any cost should pay off in the long run.
Frank Slootman, CEO of Snowflake and prior CEO of ServiceNow and Data Domain, is the main reason investors are not concerned about the bottom line. The CEO improved the cash efficiency of Data Domain, a company he took from near bankruptcy to a significant acquisition after "selling more than all its competitors combined.” He also grew Service Now from $75 million to $1.5 billion.
The free cash flow margin is set to improve this upcoming year again to "break-even" with operating losses of adjusted (19%) compared to (38%) in FY2021. The operating losses saw a 5X improvement from two years ago when the adjusted operating margins were (105%) in FY2020.
Ultimately, Snowflake has best-in-show key metrics. There is a lot of supporting evidence that Snowflake is well-loved by its customers and is positioned well for future/sustained growth. Databricks has an upcoming IPO and this is a primary risk to Snowflake as there will be a competitor on the public markets once this happens. I expect Databricks to split investor interest – more on this below.
Product Overview:
Snowflake’s decoupled architecture allows for compute and storage to scale separately with the storage provided from any cloud provider the customer chooses. By processing queries using massively parallel processing (MPP), where each node in the cluster stores a portion of the data set locally, the virtual warehouses can access the storage layer independently so as not to compete for compute power. With the competitors, such as Redshift, where compute and storage are coupled, more time is spent reconfiguring the cluster.
Snowflake calls this offering a virtual data warehouse where workloads share the same data but can run independently. This is crucial because Snowflake’s competitors combine compute and storage and require customers to size and pay based on the largest workload.
Data warehouses are centralized data repositories that collect and store information across many sources that are both internal and external. The raw data is ingested into the data warehouse and processed to answer queries. To ingest data, warehouses follow the ETL process, which is: (1) Extract the data from the internal or external database or file, (2) Transform by cleaning and preparing the data to fit the schema and constraints of the data warehouse and (3) Load into the data warehouse. The ETL method helps to organize the data into a relational format. Notably, Snowflake supports both ETL and ELT, which allows for data transformation during or after loading.
One key product differentiator is that Snowflake is not built on Hadoop, rather the company uses a new SQL database engine with cloud-optimized architecture. Overall, this translates to faster queries and also reduces costs by scaling up or down for both capacity and performance. This also allows the shift to the cloud while still honoring traditional relational database tools. Just like cloud infrastructure does not require you to hold server space for peak times year-round, a cloud data warehouse does not require you to plan, acquire or manage resources for peak data demand (i.e. elasticity).
The need for resources could change by either increasing or decreasing (scaling up or down). Customers that have a need for storage but less of a need for CPU computations do not have to pay up front and can shrink the environment dynamically. Users either pay for terabytes or are billed on a per-second basis for computations. Notably, Snowflake charges by execution-based usage and is not a cloud SaaS-company that charges by subscription.
Snowflake has a multi-cluster architecture which is unique from single cluster databases. The multi-cluster approach allows the clusters to access the same underlying data yet to run independently. This allows for heavy queries and operations to run very quickly and with fewer errors because the queries are not accessing the same data warehouse.
Queries are made with standard SQL, for analytics, and integrates with R and Python programming languages. The company delivers the ability to handle all incongruent data types in a single data warehouse. Because the data is accessible through SQL, there is widespread developer uptake as it’s the most common database language.
Snowflake supports both structured data and semi-structured data. As machine-generated data grows to include applications, sensors and mobile devices, Snowflake allows semi-structure data to be handled without preparation or schema definitions. The result is handling JSON, Avro, ORC, Parquet or XML data as if it were relational and structured.
Snowflake uses a compressed columnar database. Columnar databases are optimized for the fast retrieval of columns of data and is used for analytic data queries. Other features include centralized metadata management that is stored in a single-key value store that allows cloning of tables and databases. Security is baked into the platform to where Snowflake automatically encrypts all data to the point where unencrypted data is not even allowed. There is third-party certification and validation for security standards like HIPAA.
Beyond the value proposition of separating storage from compute for speed, and also scaling up or down to reduce costs, the third takeaway is that Snowflake is also much easier for customers to use as it’s designed to remove the role of a database administrator for monitoring and/or to tune query performance.
The end goal of choosing Snowflake is that you load data, run queries, and do little else – which is an immense value proposition due to the amount of time wasted prepping, balancing, tuning and monitoring traditional data warehouses originally built for on-premise.
Snowflake is capitalizing on the multi-cloud trend and growing rapidly with customers who want a choice in public cloud provider despite the cloud giants having their own data warehouse systems, such as Amazon Redshift, Azure Synapse and Google Big Query.
Generally speaking, Big Query is a closer competitor as Google’s offering also separates storage and compute. The differences between BigQuery and Snowflake include pricing structure where Snowflake is a time-based pricing model where users are charged for execution time and BigQuery is a query-based pricing model, where users are charged for the amount of data returned from the queries. BigQuery has a serverless feature that makes it easier to begin using the data warehouse a the serverless feature removes the need for manual scaling and performance tuning. Dremel is the query engine for BigQuery.
When it comes to deciding between BigQuery and Snowflake, it can come down to what you do with the database due to pricing structure differences. For instance, Snowflake is a better choice for concurrent users and business intelligence. It’s also a great choice for data-as-a-service, where you might give client access to your data in the form of analytics. BigQuery is perhaps a better choice for ad hoc reporting, where you have occasional complex reports on a quarterly basis or recommendation models and machine learning that require high idle time. Again, these examples are mainly due to pricing structure.
Despite BigQuery having a strong following with nearly twice the number of companies as Snowflake and growing around 40%, it tested slower than Snowflake in field tests performed by GigaOm in 2019. Vendor lock-in from BigQuery is also undesirable as companies may prefer AWS or Azure and/or more interoperability or best-in-breed solutions – we can see this in the growing trend of multi-cloud. AWS Redshift has the biggest market presence but growth is nearly flat at 6.5% and AWS is the leading partner for Snowflake.
Here's a great write-up from the Hashmap Engineering and Technology Blog that points out why implementing optimized row columnar (ORC) format data loads is ideal for either Snowflake or Amazon Redshift due to the ORC file format. Again, ultimately the choice in which system you use comes down to the individual needs for implementation although Snowflake is designed to be a competitor in nearly every case.
There’s a great write-up from analyst David Vellante that discusses how Snowflake competes with cloud native database giants. His analysis discusses survey responses from CIOs and IT buyers with Snowflake having a lead over the tech giants in spending intentions. The Enterprise Technology Research study he highlights showed 80% of AWS accounts plan to spend more on Snowflake in 2020 relative to 2019 with 35% adding Snowflake as new compared to 12% adding Redshift as new. In Azure, 78% plan to spend more on Snowflake with 41% adding new. On Google Cloud, 80% plan to increase spending on Snowflake. We can see the people have spoken.
Pricing pressure is the weakness we must monitor with Snowflake as it’s sandwiched heavily between tech giants in an area where these tech giants are very protective of their turf. How cheap will BigQuery become to attract developers to Google Cloud, which Google can then monetize the life span a customer in various ways?
You'll also see below that the tech giants are also private investors in Databricks.
Valuation Update:
Snowflake is the fastest growing cloud stock with 124% revenue growth over the last 12 months and a projected 85% over the next 12 months. The recent sell-off in tech stocks has led to a significant contraction in Snowflake's valuation. Snowflake trades at 56.8x EV/NTM revenue after seeing its valuation peak above 80x forward revenue following its IPO.
Snowflake guided for 82% revenue growth in 2021 at the midpoint of its target. As covered in the financials, the company is also targeting breakeven in Free Cash Flow for the full year in 2021 after a -12% FCF margin in 2020. Snowflake sees Operating Margin improving to -19% in 2021 from -38% in 2020.
SaaS stocks are typically valued based on a multiple of forward revenue. Below, we take a look at the ten highest valuations in the space:
As we can see, Snowflake has the highest projected growth rate over the next 12 months by a wide margin. Only one other SaaS stock in the table is projected to grow over 40% YoY in 2021 (CRWD). Conversely, Snowflake is projected to grow 85% YoY in 2021, 48 percentage points above the median.
Consensus expectations show that Snowflake is projected to grow revenue 65% YoY in 2022 and 57% YoY in 2023. In fact, Credit Suisse’s DCF Model on SNOW calls for its growth rate to exceed 40% YoY in 2024 and 2025, before retreating to 37% in the year ending 2026.
This growth is unparalleled by any other public SaaS stock, as the median NTM growth rate for the 10 highest valuations is currently 37%. This means that analysts are expecting SNOW to grow revenue in 2026 at the same rate that Cloudflare, Zscaler, and ZoomInfo will this year. It becomes even more extraordinary when you consider that Snowflake is projected to achieve this growth rate after posting $3.5B in revenue in 2025.
This revenue number comfortably exceeds the combined annual revenues of Cloudflare, Zscaler, and ZoomInfo. To put Snowflake's growth into perspective, analysts expect a higher growth rate from Snowflake in 2025 than any SaaS stock this year outside of CrowdStrike.
This is why we believe it is essential to look beyond the NTM revenue multiples, as Snowflake will separate itself by continuing to grow faster than any of its peers over the next 5 years. With this level of growth, SNOW will be able to compound its revenues to bigger and bigger totals.
The farther out we look into the future, the more reasonable SNOW's valuation becomes compared to its peers.
Major Competitor: Databricks
In my most recent Motley Fool podcast, I pointed out that Databricks planned to go public, which was a risk to Snowflake's valuation.
We don't have an S-1 filing at this time, but TechCrunch has done some preliminary work based on a $28 billion private valuation. The tech media site estimates that the company grew from a $200 million annual run rate to a $350 million annual run rate between Q3 2019 and Q3 2020.
TechCrunch's current number is a run rate of $425 million to $485.6 million for Q1 2021. CNBC reported, "$425 million in annualized sales," representing 70% growth for last year. This is quite a bit lower than Snowflake's growth last year, which was 123% for full-year growth.
We believe Databricks could open up at a higher valuation than where Snowflake is currently trading. This is because the forward growth is lower, which we estimate to be 60% for FY2021. We are assuming a $50 billion market cap at the opening. The $50 billion represents about 2X the private valuation while Snowflake opened around 3X its last private valuation.
Early backers for Databricks include Microsoft, which was later joined by Amazon Web Services, Google via CapitalG, and Salesforce. Venture firm A16Z and Tiger Global are also among the investors. This strong lineup participated in the latest $1.9 billion round. The articles that reported on the recent round point out the rarity in getting backing from all four major cloud vendors.
What’s the difference between Databricks and Snowflake?
Yesterday on our forum, there was an excellent post by a subscriber around the differences between Snowflake and Databricks.
The major difference between Snowflake and Databricks from a customer standpoint is that Snowflake is laser-focused on the public cloud/cloud native while Databricks is differentiated in that it can build information pipelines across silos, including on-premise and hybrid architectures. Snowflake's main value proposition is to reduce the time required to prep and monitor data so that a customer does not need to manage software or hardware. Even if a team has the technical skills, they may not want to spend the time required for Databricks, which is perhaps one reason why Snowflake is reporting decent growth in the Fortune 500 and other key accounts.
The architecture of a data lakehouse allows for business intelligence and machine learning through a more open paradigm. The idea is to combine best of data warehouses and data lakes to span unstructured and semi-structured data while keeping costs low. By combining both, teams can move faster and without duplicating the data. This is a key benefit to Databricks DeltaLake, and this is especially important for data analytics and machine learning. With that said, Databricks is more advanced and expert-level.
I want to point out that Snowflake is very clear as to why it's done well – which is that it handles migrations to the public cloud from legacy on-premise systems better than the competitors. Snowflake's priority is to compete with other SQL databases right now, although the company will need to eventually compete with Databricks. Management has discussed rolling out support for unstructured data, for instance, but no timeline has been set.
Looking longer-term, what Snowflake needs to answer is how will it compete with Databricks on machine learning? Databricks is superior here for ML as it’s built on top of Apache Spark and supports Spark, Python, Scala and also SQL. This was discussed in the thread on the forum here.
The forum thread points out that Databricks is more complex to upload the data, monitor and manage, but there are benefits to going through this hassle. One of the primary benefits is support for Python and Scala, which are programming languages for machine learning. For now, you must use an outside vendor or tool as connectors or integrations in order to support these programming languages and libraries with Snowflake. It’s also worth mentioning that Databricks is cheaper for processing a lot of data at petabyte scale.
Growth is the great equalizer when comparing products and my preliminary understanding is that Snowflake is growing much faster than Databricks and expects to continue to outpace the competitor. I will need to look into Databrick’s financials and see an earnings report or two to determine more about the competitor’s sustained growth rate. Here’s a snapshot of Snowflake Google search queries compared to Databricks. We should ignore the spike as it was IPO related.
What I find to be very intriguing is what Snowflake will do to compete on ML. This gap in product capability is not lost on the Snowflake team and management. Being laser-focused on the public cloud/cloud-native lends itself well for Snowflake to compete here theoretically, yet its laser-focus on SQL is getting in the way strategically speaking. The company is aware of this and plans to roll out support for unstructured data. Essentially, Snowflake is capable of evolving to meet the growing demands of ML … so let’s see what happens here as Snowflake's management likely has a plan.
Conclusion:
Snowflake is disrupting the data warehousing market (or cloud data market) with a superior cloud data platform that delivers across key differentiators. Snowflake demonstrates excellent product-market fit, clear competitive advantages, and strong management — the primary ingredients for a great growth stock.
I prefer to not pay over 40 forward P/S and certainly not over 50 forward P/S yet Snowflake's forward growth requires additional consideration as it promises to outpace its peers. In this case, we initiated in the 55x range and will build more into the position either on a pullback or a breakout.