Specialist
Former senior executive at Benchmark Mineral Intelligence Ltd
Agenda
- Head-to-head customer evaluation criteria and differentiating factors between Snowflake (NYSE: SNOW) and Databricks from a customer standpoint
- Opportunities for hyperscalers and products from Google’s BigQuery (NASDAQ: GOOGL), Amazon’s Redshift (NASDAQ: AMZN) and Microsoft’s Synapse (NASDAQ: MSFT)
- Product differentiation, highlighting serverless and machine learning
- Increased use cases, new chip designs impact and durability of market share dynamics
- 2023 outlook and demand environment
Questions
1.
Could you share an overview of Snowflake and Databricks, their offerings, value propositions and perhaps some context around these technologies’ evolution and why they’re so integral to data analytics and in the enterprise today?
2.
Could you give some context on when you started working with Databricks and Snowflake? How would you describe your experience and the value those products created for you?
3.
You mentioned selection criteria and the ROI calculation. What main criteria do you look at when selecting a data warehouse or lakehouse technology?
4.
You mentioned gaps within the products and that those are seemingly closing. What do you think are the main product gaps remaining between Snowflake and Databricks?
5.
In terms of the ROI calculation from a customer when deciding between Snowflake and Databricks, as you mentioned, Databricks seemingly comes in at a lower TCO [total cost of ownership] than Snowflake, but they appear comparably priced at the onset. Could you discuss the customer’s perspective of the two companies’ price points relative to other options in the market?
6.
In terms of technical aspects, I’m curious as you brought up AWS, why do you think it’s so hard for Redshift to copy Snowflake’s data house architecture? How much re-architecturing would it take from Amazon to better compete with Snowflake? What are the competitive dynamics here?
7.
As Snowflake and Databricks continue to offer products that incrementally overlap each other’s strengths, do you think customers will be more hesitant to standardise on one vendor as opposed to continually working with multiple over the medium-to-long term? Why might that be?
8.
What do you think is the prevailing rationale to work with both Snowflake and Databricks in one customer? How many use cases would you say are still unique relative to how many are overlapping, based on where the products are today?
9.
Looking at a specific use case that has come to light recently, I think Snowflake announced Unistore, a row-based storage engine for transactional data. How feasible is it to use Snowflake’s architecture in OLTP [online transaction processing] workloads?
10.
Does Databricks have any initiatives similar to Snowflake’s Unistore?
11.
Looking at hyperscaler product offerings, could you share your opinion on BigQuery and its initiatives there? Do you think the integration of Omni was a differentiator for the company in those offerings? How do you assess Snowflake and Databricks’ competitive or product moats relative to what the hyperscaler cloud providers offer? Do you see any of them trying to reach competitive parity on any particular timeline?
12.
Do you consider the data cloud sector as primarily a two-horse race between Databricks and Snowflake for the medium-to-long term? Are there any competitive threats out there that could take market share from them? Might the two take share from each other?
13.
What do you think will be the number one threat overall to Snowflake and Databricks’ continued growth? Could this be disrupted by a Dremio, Firebolt or Starburst, or a hyperscaler reaching parity?
14.
How long does it take to implement or ramp up with Snowflake vs Databricks? What does the procurement cycle look like?
15.
Snowflake posts very high net revenue retention. We’ve heard Databricks is comparable. To the extent that these are extremely sticky products, many people seem to have questions about how a tough macro backdrop could impact consumption. Do you think there’s any scenario in which accounts might not increase usage or consumption of their data cloud products, especially if we were to enter a recession?
16.
What do you see as the biggest opportunity for Databricks and Snowflake on a product front? How could this factor into their long-term product roadmap? What might customers be looking for that might not be addressed today?
