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May 8, 2025 · 35m · mad

Rewriting Success: What InfluxDB 3.0 Teaches About Scaling—and Scrapping—Your Core Tech

Evan Kaplan · 25m spoken Matt Turck · 6m spoken
0:00 / 0:00
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In this episode of The MAD Podcast, host Matt Turck interviews Evan Kaplan, CEO of InfluxData, about the technical evolution and business strategies behind InfluxDB. They discuss rebuilding their database engine in Rust to form the FDAP stack, open-source monetization, and the expanding role of time-series databases in IoT and real-time control systems.

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 21.2% of the talking time here. How this is scored →

Matt as informed peer 4.0 Guest teaching 5.3 Guest disagreement 1.4 Matt pushing back 1.3
05100:0010:0020:0030:001:31–5:32 · Matt as informed peer 3/10 The Origins of InfluxDB and Founder Paul Dix Matt opens by setting historical context around InfluxData's early meetups and product iterations. Evan shares lighthearted anecdotes about co-founder Paul Dix and the company's early pivot from SaaS server monitoring to a dedicated time-series database in Go.5:32–15:40 · Matt as informed peer 4/10 Time Series 101: Purpose-Built vs. General-Purpose Databases Matt presses Evan to define technical concepts like high cardinality and operational vs transactional workloads. Evan explains the four architectural pillars behind InfluxDB 3.0, including storage/compute decoupling and admitting defeat on their proprietary Flux language in favor of native SQL.15:40–19:40 · Matt as informed peer 5/10 The FDAP Stack and Apache DataFusion Matt demonstrates familiarity with modern data architectures by spelling out the FDAP stack acronym. Evan details how Apache DataFusion, Flight SQL, and Parquet form an open, long-term foundation written in Rust.19:40–22:51 · Matt as informed peer 4/10 InfluxDB's Role in Lakehouse Architectures and Real-Time Control Matt inquires about data movement and placement relative to Lakehouses like Databricks and Snowflake. Evan clarifies that InfluxDB acts as the real-time operational engine and sensor collector while analytical intelligence models run upstream in the lakehouse.22:51–27:54 · Matt as informed peer 4/10 Real-World IoT, Tesla Powerwalls, and the TICK Stack Matt challenges Evan on whether IoT lived up to its mid-2010s venture capital hype. Evan acknowledges the initial buzzword phase but explains that sensor telemetry now drives over 60 percent of their business, citing real-world deployments like Tesla Powerwalls.27:54–31:51 · Matt as informed peer 4/10 Competitive Strategy and the AWS Partnership Matt asks how InfluxDB differentiates itself against hyperscalers and competing database startups. Evan outlines their bottom-up developer strategy and explains how AWS chose to partner and host InfluxDB rather than fork the open-source code.31:51–35:14 · Matt as informed peer 4/10 Open-Source Monetization and the Two Home Runs Rule Matt asks about staying focused on a specialized niche versus broadening product scope. Evan explains the two home runs rule in open source—building developer adoption first, then discovering an effective monetization framework.1:31–5:32 · Guest teaching 4/10 The Origins of InfluxDB and Founder Paul Dix Matt opens by setting historical context around InfluxData's early meetups and product iterations. Evan shares lighthearted anecdotes about co-founder Paul Dix and the company's early pivot from SaaS server monitoring to a dedicated time-series database in Go.5:32–15:40 · Guest teaching 6/10 Time Series 101: Purpose-Built vs. General-Purpose Databases Matt presses Evan to define technical concepts like high cardinality and operational vs transactional workloads. Evan explains the four architectural pillars behind InfluxDB 3.0, including storage/compute decoupling and admitting defeat on their proprietary Flux language in favor of native SQL.15:40–19:40 · Guest teaching 5/10 The FDAP Stack and Apache DataFusion Matt demonstrates familiarity with modern data architectures by spelling out the FDAP stack acronym. Evan details how Apache DataFusion, Flight SQL, and Parquet form an open, long-term foundation written in Rust.19:40–22:51 · Guest teaching 6/10 InfluxDB's Role in Lakehouse Architectures and Real-Time Control Matt inquires about data movement and placement relative to Lakehouses like Databricks and Snowflake. Evan clarifies that InfluxDB acts as the real-time operational engine and sensor collector while analytical intelligence models run upstream in the lakehouse.22:51–27:54 · Guest teaching 5/10 Real-World IoT, Tesla Powerwalls, and the TICK Stack Matt challenges Evan on whether IoT lived up to its mid-2010s venture capital hype. Evan acknowledges the initial buzzword phase but explains that sensor telemetry now drives over 60 percent of their business, citing real-world deployments like Tesla Powerwalls.27:54–31:51 · Guest teaching 5/10 Competitive Strategy and the AWS Partnership Matt asks how InfluxDB differentiates itself against hyperscalers and competing database startups. Evan outlines their bottom-up developer strategy and explains how AWS chose to partner and host InfluxDB rather than fork the open-source code.31:51–35:14 · Guest teaching 6/10 Open-Source Monetization and the Two Home Runs Rule Matt asks about staying focused on a specialized niche versus broadening product scope. Evan explains the two home runs rule in open source—building developer adoption first, then discovering an effective monetization framework.1:31–5:32 · Guest disagreement 1/10 The Origins of InfluxDB and Founder Paul Dix Matt opens by setting historical context around InfluxData's early meetups and product iterations. Evan shares lighthearted anecdotes about co-founder Paul Dix and the company's early pivot from SaaS server monitoring to a dedicated time-series database in Go.5:32–15:40 · Guest disagreement 2/10 Time Series 101: Purpose-Built vs. General-Purpose Databases Matt presses Evan to define technical concepts like high cardinality and operational vs transactional workloads. Evan explains the four architectural pillars behind InfluxDB 3.0, including storage/compute decoupling and admitting defeat on their proprietary Flux language in favor of native SQL.15:40–19:40 · Guest disagreement 1/10 The FDAP Stack and Apache DataFusion Matt demonstrates familiarity with modern data architectures by spelling out the FDAP stack acronym. Evan details how Apache DataFusion, Flight SQL, and Parquet form an open, long-term foundation written in Rust.19:40–22:51 · Guest disagreement 1/10 InfluxDB's Role in Lakehouse Architectures and Real-Time Control Matt inquires about data movement and placement relative to Lakehouses like Databricks and Snowflake. Evan clarifies that InfluxDB acts as the real-time operational engine and sensor collector while analytical intelligence models run upstream in the lakehouse.22:51–27:54 · Guest disagreement 2/10 Real-World IoT, Tesla Powerwalls, and the TICK Stack Matt challenges Evan on whether IoT lived up to its mid-2010s venture capital hype. Evan acknowledges the initial buzzword phase but explains that sensor telemetry now drives over 60 percent of their business, citing real-world deployments like Tesla Powerwalls.27:54–31:51 · Guest disagreement 2/10 Competitive Strategy and the AWS Partnership Matt asks how InfluxDB differentiates itself against hyperscalers and competing database startups. Evan outlines their bottom-up developer strategy and explains how AWS chose to partner and host InfluxDB rather than fork the open-source code.31:51–35:14 · Guest disagreement 1/10 Open-Source Monetization and the Two Home Runs Rule Matt asks about staying focused on a specialized niche versus broadening product scope. Evan explains the two home runs rule in open source—building developer adoption first, then discovering an effective monetization framework.1:31–5:32 · Matt pushing back 0/10 The Origins of InfluxDB and Founder Paul Dix Matt opens by setting historical context around InfluxData's early meetups and product iterations. Evan shares lighthearted anecdotes about co-founder Paul Dix and the company's early pivot from SaaS server monitoring to a dedicated time-series database in Go.5:32–15:40 · Matt pushing back 2/10 Time Series 101: Purpose-Built vs. General-Purpose Databases Matt presses Evan to define technical concepts like high cardinality and operational vs transactional workloads. Evan explains the four architectural pillars behind InfluxDB 3.0, including storage/compute decoupling and admitting defeat on their proprietary Flux language in favor of native SQL.15:40–19:40 · Matt pushing back 1/10 The FDAP Stack and Apache DataFusion Matt demonstrates familiarity with modern data architectures by spelling out the FDAP stack acronym. Evan details how Apache DataFusion, Flight SQL, and Parquet form an open, long-term foundation written in Rust.19:40–22:51 · Matt pushing back 1/10 InfluxDB's Role in Lakehouse Architectures and Real-Time Control Matt inquires about data movement and placement relative to Lakehouses like Databricks and Snowflake. Evan clarifies that InfluxDB acts as the real-time operational engine and sensor collector while analytical intelligence models run upstream in the lakehouse.22:51–27:54 · Matt pushing back 2/10 Real-World IoT, Tesla Powerwalls, and the TICK Stack Matt challenges Evan on whether IoT lived up to its mid-2010s venture capital hype. Evan acknowledges the initial buzzword phase but explains that sensor telemetry now drives over 60 percent of their business, citing real-world deployments like Tesla Powerwalls.27:54–31:51 · Matt pushing back 2/10 Competitive Strategy and the AWS Partnership Matt asks how InfluxDB differentiates itself against hyperscalers and competing database startups. Evan outlines their bottom-up developer strategy and explains how AWS chose to partner and host InfluxDB rather than fork the open-source code.31:51–35:14 · Matt pushing back 1/10 Open-Source Monetization and the Two Home Runs Rule Matt asks about staying focused on a specialized niche versus broadening product scope. Evan explains the two home runs rule in open source—building developer adoption first, then discovering an effective monetization framework.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 69.1% · guest 30.9%0:00 · Matt 69.1% · guest 30.9%3:00 · Matt 2% · guest 98%3:00 · Matt 2% · guest 98%6:00 · Matt 18.8% · guest 81.2%6:00 · Matt 18.8% · guest 81.2%9:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%12:00 · Matt 1.2% · guest 98.8%12:00 · Matt 1.2% · guest 98.8%15:00 · Matt 37.6% · guest 62.4%15:00 · Matt 37.6% · guest 62.4%18:00 · Matt 13.8% · guest 86.2%18:00 · Matt 13.8% · guest 86.2%21:00 · Matt 23.7% · guest 76.3%21:00 · Matt 23.7% · guest 76.3%24:00 · Matt 31.3% · guest 68.7%24:00 · Matt 31.3% · guest 68.7%27:00 · Matt 19.5% · guest 80.5%27:00 · Matt 19.5% · guest 80.5%30:00 · Matt 5.5% · guest 94.5%30:00 · Matt 5.5% · guest 94.5%33:00 · Matt 34.8% · guest 65.2%33:00 · Matt 34.8% · guest 65.2%
Sharpest disagreement ▶ 26:37 Evan debunking early IoT hype

Evan directly pushes back on historical industry narratives, noting that IoT started as an overhyped buzzword before eventually maturing into a legitimate revenue driver.

 Hardest push from Matt ▶ 26:14 Matt questioning IoT's market reality

Matt pushes back from a VC perspective, asking Evan to reconcile early venture capital hype around IoT with actual present-day commercial adoption.

 Biggest teaching moment ▶ 10:15 Evan on abandoning Flux for SQL

Evan educates the host on product strategy pivots, admitting that despite heavy investment in their custom Flux language, market reality forced them to adopt native SQL.

 Matt holds his own ▶ 16:50 Matt decoding the FDAP stack acronym

Matt displays sharp domain awareness by instantly listing each component of the FDAP acronym before the guest can explain it.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
The Origins of InfluxDB and Founder Paul Dix 3410 Matt opens by setting historical context around InfluxData's early meetups and product iterations. Evan shares lighthearted anecdotes about co-founder Paul Dix and the company's early pivot from SaaS server monitoring to a dedicated time-series database in Go.
Time Series 101: Purpose-Built vs. General-Purpose Databases 4622 Matt presses Evan to define technical concepts like high cardinality and operational vs transactional workloads. Evan explains the four architectural pillars behind InfluxDB 3.0, including storage/compute decoupling and admitting defeat on their proprietary Flux language in favor of native SQL.
The FDAP Stack and Apache DataFusion 5511 Matt demonstrates familiarity with modern data architectures by spelling out the FDAP stack acronym. Evan details how Apache DataFusion, Flight SQL, and Parquet form an open, long-term foundation written in Rust.
InfluxDB's Role in Lakehouse Architectures and Real-Time Control 4611 Matt inquires about data movement and placement relative to Lakehouses like Databricks and Snowflake. Evan clarifies that InfluxDB acts as the real-time operational engine and sensor collector while analytical intelligence models run upstream in the lakehouse.
Real-World IoT, Tesla Powerwalls, and the TICK Stack 4522 Matt challenges Evan on whether IoT lived up to its mid-2010s venture capital hype. Evan acknowledges the initial buzzword phase but explains that sensor telemetry now drives over 60 percent of their business, citing real-world deployments like Tesla Powerwalls.
Competitive Strategy and the AWS Partnership 4522 Matt asks how InfluxDB differentiates itself against hyperscalers and competing database startups. Evan outlines their bottom-up developer strategy and explains how AWS chose to partner and host InfluxDB rather than fork the open-source code.
Open-Source Monetization and the Two Home Runs Rule 4611 Matt asks about staying focused on a specialized niche versus broadening product scope. Evan explains the two home runs rule in open source—building developer adoption first, then discovering an effective monetization framework.

Statements from this episode (10)

Insight
Kaplan: General-purpose databases fail when scaling to high-volume telemetry
“So yes, in some cases there are, for many use cases, you can use a general purpose database. People have built stuff on Postgres. People have built time series on Mongo. Like those are highly useful. But if you're ingesting huge amounts of data, you want the q…”
Evan Kaplan May 8, 2025 ▶ 6:26
Prediction Not checkable as stated
Kaplan: Lakehouses will absorb adjacent tools like ETL and governance
“You know, from my historical perspective on the industry, I think lots of capability that sits outside those things will fall inside those things. Whether it's data governments, whether it's different ETL, whether it's different conversions, these things are g…”
Evan Kaplan May 8, 2025 ▶ 12:02
Opinion
Kaplan: Apache Iceberg is becoming the standard base table format
“And now it seems like Iceberg is certainly with Databricks buying Tabular. That'll be interesting, but Iceberg feels like the base format, although DeltaShare is also relevant.”
Evan Kaplan May 8, 2025 ▶ 17:53
Prediction Not checkable as stated
Kaplan: AI models will run on lakehouses, not time-series databases
“We don't think the intelligence get built on our platform. Like, I know that's a hard thing to say because everything's AI now. We believe that we're fundamentally mining the data and making it available so that the intelligence models, which are largely going…”
Evan Kaplan May 8, 2025 ▶ 21:02
Disclosure
Kaplan: 60% to 70% of InfluxData's business is IoT
“Now, here we are, it's 20, 25, and probably 60 to 70% of our business is now IoT.”
Evan Kaplan May 8, 2025 ▶ 27:00
Opinion
Kaplan: CIOs do not care about their company's time-series database
“It's not useful for me to have a meeting with the CIO. The CIO does not care about his time series database. I mean, he should maybe, but he doesn't.”
Evan Kaplan May 8, 2025 ▶ 28:49
Disclosure
InfluxData delays sales calls until users run software for three months
“We don't even talk to a customer unless they've been running our stuff for three months, generally, or they've built something.”
Evan Kaplan May 8, 2025 ▶ 29:46
Assertion Supported
Amazon approached InfluxData for a partnership due to Timestream customer demand
“Last year we did a very unique deal with Amazon. Where they have Amazon's time stream, but their customers kept asking them for influx. And so they approached us.”
Evan Kaplan May 8, 2025 ▶ 30:11
Insight
Commercial open-source success requires distinct product and monetization home runs
“So with open source, you have to hit two home runs. You have to build a project that developers love. And they deploy and they use and they love. And then you have the second home run, which you have to figure out a model to monetize it.”
Evan Kaplan May 8, 2025 ▶ 32:20
Assertion Not checkable as stated
InfluxData has 1.3 million open-source users but only 2,600 paying customers
“I argue that our first versions of one and two in open source were so good that you have 1.3 million users and only 2600 paying customers.”
Evan Kaplan May 8, 2025 ▶ 33:01
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