Databricks – Product Investments & Growth Outlook

1.

What is Databricks and what does it do? Could you highlight 2-3 key trends or drivers that you think are most important for us to pay attention to when we’re looking at the business?

2.

Snowflake reported spend on database management systems as a percentage of customers’ total software spend improved a lot over the past few years – 11% in 2016, up to 13% in 2021, and the company expects it to be as much as 17% by 2026. How would you characterise that state of demand for Databricks, its offerings or just the data cloud or system software space? Why do you think demand is so healthy?

3.

To what extent do you think the pandemic era increased enterprises’ interest in cloud-based software, cloud-based analytics tools and the data cloud software space overall?

4.

When you mention adoption has accelerated, would you expect no slowdown or pullback in demand as we exit the pandemic era demand environment?

5.

There are a lot of signs that we’re entering a possible period of flat-to-moderate economic decline, so a potential recession. How might the data cloud software sector or Databricks perform in that overall market environment? Could you expand on why we could see increased demand in a downturn?

6.

Could you estimate the TAM for Databricks? The company has been adding a lot of new products to the suite, seemingly increasing use cases and widening its adoptability.

7.

Do you consider that the TAM for Databricks should be broken down by any specific customer size or vertical? When you say any business with data in the cloud, where does the company potentially work or function the best? Are we still primarily thinking about the Fortune 500 or Fortune 1000 with mass amounts of data or any specific verticals?

8.

How much of the Fortune 500 and Fortune 1000 market is already on a modern data management system, either working with a data warehouse or a data lake or other? What percentage has already adopted the technology so we can better understand what the runway looks like?

9.

How are you assessing the pace of adoption of these modern data cloud products? How much room do we have for continued acceleration? When might we reach an inflection point in adoption of these solutions? Could it be over the next five years?

10.

Do you think that Databricks will continue to view Snowflake as its main competition in the near-to-long term?

11.

We talked about Databricks and Snowflake being at competitive parity. Do you still think we have room to grow there until the two are matching?

12.

Why do you think customers choose Databricks over a Snowflake? When is the competitive environment or deal more favourable for Databricks over Snowflake?

13.

The customer perception around Databricks is it typically comes in cheaper. Do you think that makes a big difference to most customers? What might be the customer perception around Snowflake and Databricks relative to their price points?

14.

Do you think Snowflake is looking to reduce that TCO [total cost of ownership] calculation for its customers to compete effectively on price with Databricks? Is it not so worried about that short- or near-term share loss? Do you think it’s making a material difference on Snowflake for Databricks’s ability to prove that it has a reduced TCO?

15.

Could you expand on your thoughts about the barriers to entry in the data cloud software sector? How easy is it for a customer with a built-out data analysis team to build this in-house and simply choose not to work with Databricks or Snowflake for their needs on this?

16.

How do you think Snowflake and Databricks compare in enterprise? You mentioned they weren’t really coming up on deals too often, but presumably they’ll come head-to-head more often as they continue to grow. What does the runway look like within the Fortune 500 or Fortune 1000 customer base?

17.

What do you think about hyperscalers and their product offerings in this arena? BigQuery has plans to launch, or it may have already launched, BigLake. Synapse now has a serverless product. Do you think hyperscalers are increasing investments in their products, which, in turn, is probably increasing competition for Databricks? Do you think other cloud vendors reaching some sort of competitive parity in their own right?

18.

Are there any other small competitors or disruptors that we should be paying attention to? Is there anyone that is in a smaller hyper-growth stage that could pose a threat to Databricks? What do you think this looks like in terms of competitive saturation?

19.

What are some of the advantages or disadvantages of Databricks being built on top of Apache Spark? What does this mean for the business, and is the open-sourced infrastructure a positive or negative in the medium-to-long term?

20.

How much overlap is there between Snowflake and Databricks and their offerings? As Databricks has seemingly moved a bit deeper into what Snowflake has considered its territory by expanding its product offerings and use cases, are these vendors coming more into conflict and offering more overlapping solutions, or is there just enough runway for them to both continue to grow?

21.

Do you think Databricks should focus more or less on leveraging channel partners and indirect sales to continue to scale and grow? Snowflake has about a 30% mix of channel sales.

22.

Are there any key verticals Databricks might be able to unlock through partnerships that it might not have had any access to? What do you think about the approach of leveraging the channel to open new areas to sell into that haven’t been as active for the company? Is there a focus on this or do you think it’s mostly improving partnerships with ISVs [independent software vendors] to continue to increase functionality?

23.

You said Databricks’ technology was more difficult to sell than Saleforce’s. What components of Databricks have the easiest go-to-market strategy and sales approach vs what doesn’t resonate as quickly with customers?

24.

Do you think Snowflake has a comparable proof of concept that’s demonstrable to customers? Is that a key differentiator for Databricks?

25.

How expensive does it get to rip and replace a solution or actually switch a product once it’s integrated, either from Snowflake to Databricks or vice versa?

26.

Could you foresee any circumstances in which a Databricks customer would look to rip and replace its solution and either migrate to a different vendor or just churn completely? What expense would that incur for the client? Why would they actually go to that level or do that?

27.

Would you expect more customer churn as these businesses continue to scale and customers look at other available products in the market, such as hyperscaler products if they continue to make investments and reach some competitive parity with Snowflake and Databricks?

28.

What do you make of Cloudera and its ability to try to drive growth? It has its Cloudera data platform, and it’s now a private company. Do you think it’s trying to mount a comeback vs Databricks, Snowflake or the hyperscalers?

29.

Palantir has historically not offered the broader ecosystem, but do you think there are such vendors that can become greater competitors to the Snowflakes and the Databricks of the world in the medium-to-long term, or do you think this is not so much of a threat?

30.

Snowflake recently reported 106% YoY product revenue growth. Do you think this YoY revenue growth seems sustainable? What overall top-line growth rate do you think is either achievable or sustainable for Databricks and this sector? Databricks previously disclosed USD 100m in ARR, and the company’s CEO announced in August 2022 that it had reached a USD 1bn run rate. How much room for growth is there?

31.

There’s been a rumoured Databricks IPO for a while. The CEO disabused a lot of thoughts around that, mentioning the company isn’t looking at an IPO anymore in 2022. Do you think it’s still on that trajectory? Could it happen in 2023, and does it still make sense for the business?

32.

Are there any possible risks that Databricks would not look to go public if we enter a more-than-harsh recession? Is there anything we can monitor that might indicate an IPO might be further postponed?

33.

In which regions or geographies might Databricks look to garner the most traction? Do you think there’s a larger opportunity for the company in any specific regions or territories?

34.

You mentioned Databricks was looking a lot at AWS traction in regions to gauge how successful the business could be in growing adoption in those regions. Are there any other regions that you think are early adopters of the cloud? Is this a good way to measure what territories Databricks might have the most opportunity to grow in? Are there any other factors we should put in here?

35.

What inning would you say we’re in in terms of international or ex-US adoption of Snowflake, Databricks or the sector as a whole?

36.

Could you discuss the mix of customers in terms of customer size for Databricks? What presents the most opportunity between the enterprise and mid-market customers? Do you think there’s a best-case strategy for the company to target one specific bucket over the other? Might it still be trying to grow through all customer sizes?

37.

Might Snowflake or any other competitors have a reflective mix of the two-thirds enterprise, one-third mid-market? Is anyone else trying to position itself to focus more downmarket, or is it still too early to tell?

38.

Could you discuss Databricks’s go-to-market sales approach for enterprise accounts, given you said it’s easier to sell the enterprise? Is it differentiated between what other vendors are doing? What makes the company’s approach to enterprise unique?

39.

How long does it take Databricks to ramp up an account from lead to proof of concept to integrating the account? Do you think this is best-in-class vs what Snowflake or other vendors are offering in terms of time to onboard, integrate and ramp up a customer?

40.

How long does the proof of concept take in the process for Databricks? Is it the quickest part of the overall sales process?

41.

What are your expectations around ACV [average contract value] and contract expansion? Do you think there’s any scenario where existing accounts don’t increase their usage, based on customer growth and increased consumption?

42.

Do you think the sales process is getting more or less efficient as Databricks grows and scales? As it’s continuing to add onto the force, is it getting more complex or is it more lean and efficient?

43.

Are there any other customer types or buckets where Databricks hasn’t been as successful and you see opportunity? I’m thinking perhaps about the public sector.

44.

Should Databricks focus the vast majority of resources on new logo acquisition or expansion within existing customers, given the consumption-based model?

45.

Is Databricks relatively underpenetrated in any particular markets? Alternatively, do you see increased opportunity in any markets, or have you touched on all of them?

46.

What does Databricks do to grow the business once landed within an account? Could you discuss this expansion? What levers can the business pull?

47.

Is there a trade-off between the amount of efficiency Databricks can garner relative to how integrated it has to be with customers? Is the headcount staffed up to better handle those client needs? Is this an inefficient practice or is there any room for efficiency there?

48.

Are there any other areas where Databricks may be able to pursue incremental cost efficiencies? Is there anything you think is glaringly driving inefficiencies within the business?

49.

We were discussing the hyperscalers as offering their own products in the competitive landscape. What does that look like in that frenemy or co-opetition dynamic between hyperscalers and Databricks? Do you think there’s still enough growth in the space that this is a rising tide that’ll lift all ships, or do you expect us to see increased competition between the cloud providers and the company itself?

50.

How efficient is Databricks’ sales team? Do you think the business has the right amount of headcount, incentive or quota mix to support the amount of future growth it’s looking to have?

51.

What is the relative split between the amount of investment that’s been on Databricks’ internal sales motions – that expand model – vs the sales force’s ability to acquire a new logo? Do you think it’s relatively the same or is it still pretty much new logo-centric?

52.

What’s your 3-5-year outlook for Databricks? How do you see the business shaping out over that time period?

53.

Are there any other commonly held industry assumptions about Databricks, Snowflake or the broader industry that we should potentially reconsider?

54.

If competition were to heat up, could you see Databricks potentially cutting or reducing prices or trying to reduce that TCO calculation to incentivise adoption and standardisation among potential customers?

55.

Is there anything additional you’d like to highlight regarding Databricks and the broader industry?

56.

Do you have any thoughts about Databricks’ leadership team? How well-placed do you think the team is to handle the challenges the business may face and continue to execute on that roadmap?