$SNOW
Contact is sales rep at data analytics partner that works with both $Snowflake(SNOW.US$ and $Databricks.
Key points are 1) $Salesforce(CRM.US$ data cloud doing well and should continue to grow, but unlikely to impact broader data analytic plays, 2) $Snowflake(SNOW.US$ analytics are loved by end-users and should continue growing within install base but at more mature rate, 3) $Databricks is going to negatively impact $Snowflake(SNOW.US$ growth rates: lots of customers moving more $Snowflake(SNOW.US$ data / ETL workloads into $Deutsche Bank(DB.US$ to save money, and $Snowflake(SNOW.US$ is way behind in AI.
Again, a reminder that infrastructure investments are black diamond ski runs. I prefer the green circle ease of application software / infra platforms.
$Salesforce(CRM.US$ Highlights
- $Salesforce(CRM.US$ data analytics has improved a lot over past couple years – more analytics modules, etc. Not very impressed by integration w/ Tableau – data is not very fresh.
- $Salesforce(CRM.US$ data analytics has improved a lot over past couple years – more analytics modules, etc. Not very impressed by integration w/ Tableau – data is not very fresh.
- $Salesforce(CRM.US$ obviously taking direct competitive shot at $Snowflake(SNOW.US$ but skeptical how successful that is going to be. Have very long road ahead as far as growth for CRM-based BI and marketing campaigns.
- $Salesforce(CRM.US$ data extremely important for real-time inferencing and genAI data. $Salesforce(CRM.US$ is vital source for customer data needed for any sort of meaningful analytics or machine learning.
-Challenge is $Salesforce(CRM.US$ doesn’t have any other data that is stored in data warehouse / lakehouse.
-Don’t see a huge cost in copying $Salesforce(CRM.US$ data to $Deutsche Bank(DB.US$ or $Snowflake(SNOW.US$
$Snowflake(SNOW.US$ / $Databricks Highlights
-See two patterns w/ $Snowflake(SNOW.US$ / $Deutsche Bank(DB.US$ : First is customer who has structured operational data in $Snowflake(SNOW.US$ and then puts copy of this along with all unstructured data in $Deutsche Bank(DB.US$ . Works fine, and can run analysis on it. Second is customer who just puts everything into $Deutsche Bank(DB.US$ and customers uses $Deutsche Bank(DB.US$ SQL warehouse – this is cleaner since now only one copy of data, save money on storage, easier to run ML / AI.
-See two patterns w/ $Snowflake(SNOW.US$ / $Deutsche Bank(DB.US$ : First is customer who has structured operational data in $Snowflake(SNOW.US$ and then puts copy of this along with all unstructured data in $Deutsche Bank(DB.US$ . Works fine, and can run analysis on it. Second is customer who just puts everything into $Deutsche Bank(DB.US$ and customers uses $Deutsche Bank(DB.US$ SQL warehouse – this is cleaner since now only one copy of data, save money on storage, easier to run ML / AI.
-Generally seeing customers split 50/50 between First/Second, but customers now realizing $Snowflake(SNOW.US$ is very expensive and pretty far behind in AI. So see biggest customers of $Snowflake(SNOW.US$ starting to migrate workloads to $DB to save costs and get better. $Snowflake(SNOW.US$ spend still grows but at much reduced rate.
- $Snowflake(SNOW.US$ is really far behind in AI from a market fit perspective and from a full stack perspective
-See $Snowflake(SNOW.US$ top 10 customers all starting to migrate workloads to $DB to save on $Snowflake(SNOW.US$ costs.
-Typically customers migrate ETL to $Deutsche Bank(DB.US$ to start and leave $Snowflake(SNOW.US$ in place to serve BI and be gold standard for that layer. This helps customers dramatically reduce their $Snowflake(SNOW.US$ bill bc most of spend on $Snowflake(SNOW.US$ tends to be ETL anyways.
-See accounts hiring people specifically to come in and help reduce $Snowflake(SNOW.US$ spend by using more $Deutsche Bank(DB.US$
-Capital One is $Snowflake(SNOW.US$ ’s biggest customer and they wrote a software product called Snowflake Optimizer that can be used to reduce $Snowflake(SNOW.US$ bill.
-Don’t see accounts near-term shutting off $Snowflake(SNOW.US$ bc end users love it and have great user experience. No need to rock boat with users and their analytical tools, but can optimize behind the scenes.
-Meanwhile data scientists love $Databricks – see more configurations where operational data first goes to $DB, and then gets duplicated to $Snowflake(SNOW.US$ . But $Deutsche Bank(DB.US$ becomes the primary store of data. People fine w/ this configuration bc still cuts $Snowflake(SNOW.US$ bill by 33-50%!
-See $Deutsche Bank(DB.US$ , OpenAI, Anthropic, $Alphabet-A(GOOGL.US$ , $Microsoft(MSFT.US$ outmaneuvering, outcompeting and out-innovating $Snowflake(SNOW.US$ in AI space. “It’s not even a fair race”.
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practical NyanCat_76 : Do that mean the coming results is bad for SNOW?
骨神 : No..