Apr 9, 2018 · 32m · mad
Fireside Chat with Bob Muglia, CEO at Snowflake (FirstMark's Data Driven)
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this DataDrivenNYC fireside chat hosted by Matt Turck, Snowflake CEO Bob Muglia discusses Snowflake's rapid growth into a high-valuation cloud data warehouse, detailing its decoupled architecture, enterprise cloud migration trends, multi-cloud strategy, and ecosystem dynamics.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 18.4% of the talking time here. How this is scored →
Matt as informed peer 3.8 Guest teaching 3.0 Guest disagreement 1.0 Matt pushing back 1.0
speaking balance: gold is Matt, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 17:45 Bob dismisses Hadoop as a past technology
Bob firmly rejects traditional big data architectures, labeling Hadoop as a past technology with little incremental investment moving forward.
Hardest push from Matt ▶ 18:49 Matt questions co-opetition and conflict with AWS RedshiftMatt directly challenges Bob on the tension of competing directly with AWS Redshift while relying entirely on AWS cloud infrastructure.
Biggest teaching moment ▶ 5:11 Bob reframes client-server adoption timelinesBob corrects Matt's historical 10-year client-server timeline analogy, explaining that cloud adoption accelerates value creation into a compressed 5-to-7 year window.
Matt holds his own ▶ 18:49 Matt demonstrates deep knowledge of competitive cloud warehouse landscapeMatt cites specific competitor products including AWS Redshift, BigQuery, and Azure SQL Data Warehouse, pressing on market dynamics with expert precision.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Matt as informed peer | Guest teaching | Guest disagreement | Matt pushing back | Why |
|---|---|---|---|---|---|---|
| Welcome Back and Snowflake's Valuation Growth | 3 | 2 | 1 | 1 | Matt demonstrates background knowledge of Snowflake's valuation growth, Sequoia funding round, and previous Series B/C history. Bob gently corrects minor details regarding funding rounds while keeping a warm, collaborative tone. | |
| State of Cloud Adoption and Enterprise Migration | 5 | 3 | 1 | 1 | Matt brings specific revenue and growth metrics for AWS and Azure, as well as historical comparisons to Microsoft and Oracle's growth in the client-server era. Bob reframes the client-server timeline comparison, noting cloud adoption compresses a ten-year cycle into five to seven years. | |
| The Cultural Shift of Organization-Wide Data Access | 3 | 4 | 1 | 1 | Bob explains the technical and cultural shift of cloud elasticity, detailing real customer examples scaling up thousands of nodes per second. Matt offers light commentary on buzzwords like big data and digital transformation without pushing back. | |
| Bob Muglia's Career Path and Snowflake's Origins | 4 | 2 | 1 | 1 | Matt outlines Bob's career history at Microsoft and Juniper with high accuracy. Bob shares anecdotal context on cloud disruption and taking a leap of faith joining early-stage Snowflake. | |
| Snowflake Use Cases and Legacy Migration | 3 | 3 | 0 | 0 | Matt prompts with specific architecture patterns like data marts and data lakes. Bob breaks down actual enterprise migration dynamics away from legacy appliances like Netezza and Teradata. | |
| Evaluating Hadoop and Spark Ecosystems | 6 | 4 | 2 | 3 | Matt poses insightful technical questions about Hadoop, Spark, and competition with AWS Redshift while hosted on AWS infrastructure. Bob dismisses Hadoop as a legacy technology and diplomatically navigates co-opetition with Amazon. | |
| Product Vision and Cross-Cloud Data Replication | 3 | 3 | 1 | 1 | Matt asks if Snowflake plans to expand into ETL or BI tools. Bob reframes the focus, explaining that remaining core to the data warehouse while enabling cross-cloud replication is a far higher-value strategy. | |
| Data Sharing Architecture and Transition to Q&A | 3 | 3 | 1 | 0 | Matt asks about machine learning initiatives. Bob clarifies that Snowflake focuses on enabling data sharing across corporate boundaries rather than building proprietary AI models. | |
| Audience Q&A: Decentralized Private Data and AI | 4 | 3 | 1 | 1 | Audience members and Matt ask about decentralized AI, Splunk replacement, and GDPR compliance. Bob explains how modern cloud architectures displace legacy Splunk installations and how GDPR accelerates global cloud adoption. |
Statements from this episode (15)
Snowflake closed a $260 million funding round led by Sequoia
“Sequoia joined us and so we just closed a round that was just north of two hundred and sixty million raised.”
Snowflake bills per second and scales customers to thousands of nodes
“We now bill at a per second level of granularity, and we have customers, we have one customer that every Friday afternoon goes from a really, relatively modest amount of continuous usage of Snowflake to literally thousands of nodes running simultaneously, and …”
Snowflake runs all of its internal operational data on Snowflake
“All of the data about Snowflake is in Snowflake.”
Muglia: Snowflake had no customers and early alpha product in 2014
“At that point in time, the product was just in early alpha, and there were really no customers on it, and it had not been demonstrated at any level of scale.”
Muglia: Snowflake was named after Northstar California resort's logo
“Our founders liked skiing, and they were up at North Star. No, this is true. This is all true, right? Everybody's like, oh, it must be the Snowflake Ski Moe, or whatever. I mean, and there's a million different stories, but the actual story was there were skie…”
Bob Muglia: Hadoop will not see much incremental investment
“So I think Hadoop is, is, is a past technology. I think it's, although it's still gonna, people will still use it still has a place, I think it's not an area where there's gonna be a lot of incremental additional investment.”
Bob Muglia: Apache Spark scenarios are complementary to Snowflake
“Spark, I think, is being used very, very broadly for advanced analytics, machine learning, in some cases for streaming data, and those scenarios are all very, very complimentary to Snowflake.”
Muglia: Snowflake competes with only three AWS services, primarily Redshift
“We do compete with Redshift. I think Amazon has a 150 different services in their portfolio. We compete with maybe Three, depending on whether you want to include Athena and Spectrum in that.”
Muglia: AWS will remain the leading cloud provider for the foreseeable future
“They're the leading cloud provider. We anticipate they'll be in that role, in that situation for the foreseeable future, perhaps, perhaps in, in, you know, certainly through my career.”
Snowflake plans to expand to Microsoft Azure and Google Cloud
“And so, you know, we are getting requests for other clouds, and we hear, you know, hear them about Microsoft and Google and so you'll, you'll see some things coming up in the future.”
Snowflake will strictly focus on data warehousing over ETL or BI
“We see ourselves staying very close to home. I think when we think about what can be done with the data warehouse We don't have to become, think about ETL or ELT, nor do we have to think about BI and advanced analytics to do really meaningful things to drive o…”
Muglia: Nearly all large Snowflake customers use multiple accounts for data sharing
“And that organization can be another division within a company, and we have, you know, if you look at all of our large customers, they're pretty much all using multiple accounts, snowflake accounts, and doing data sharing between them as a way to allow the dat…”
Muglia: The underlying engine behind Splunk needs a refresh
“The engine underneath Splunk needs a refresh.”