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Sep 17, 2018 · 21m · mad

3 Heretical Ideas on the Future of Data // Ajay Kulkarni, TimescaleDB (FirstMark's Data Driven NYC)

Ajay Kulkarni · 17m spoken Matt Turck · 35s spoken
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

At FirstMark's Data Driven NYC, Timescale co-founder and CEO Ajay Kulkarni presents three key arguments detailing why the big data era has given way to the time-series era. He demonstrates how time-series data is ubiquitous across modern industries and explains how specialized database architecture empowers real-time analytics.

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 2.9% of the talking time here. How this is scored →

Matt as informed peer 0.4 Guest teaching 0.4 Guest disagreement 1.2 Matt pushing back 0.2
05100:0010:0020:000:00–3:36 · Matt as informed peer 0/10 Data Driven NYC Event Title Sequence Ajay presents an overview of TimescaleDB and introduces his presentation on the future of data. This segment is a solo presentation monologue with no host presence.3:36–6:57 · Matt as informed peer 0/10 Key Trends: Rise of Machines and Data Resolution Ajay outlines trends in machine-generated data and the evolution of data resolution. The host does not participate in this monologue segment.6:57–9:41 · Matt as informed peer 0/10 Summary: Entering the Time Series Era Ajay highlights time series database adoption and presents performance benchmarks against MongoDB and Cassandra. As this is a keynote presentation segment, there is no host interaction.9:41–13:33 · Matt as informed peer 0/10 Expansion from Niche Market to Ubiquitous Use Cases Ajay argues that all data is fundamentally time series data and shares user case studies. The segment is a solo talk without host participation.13:33–21:36 · Matt as informed peer 2/10 TimescaleDB Open Source Community & Resources Matt Turck moderates Q&A on open source tactics, followed by audience questions on database architecture. The dynamic is supportive and collaborative throughout.0:00–3:36 · Guest teaching 0/10 Data Driven NYC Event Title Sequence Ajay presents an overview of TimescaleDB and introduces his presentation on the future of data. This segment is a solo presentation monologue with no host presence.3:36–6:57 · Guest teaching 0/10 Key Trends: Rise of Machines and Data Resolution Ajay outlines trends in machine-generated data and the evolution of data resolution. The host does not participate in this monologue segment.6:57–9:41 · Guest teaching 0/10 Summary: Entering the Time Series Era Ajay highlights time series database adoption and presents performance benchmarks against MongoDB and Cassandra. As this is a keynote presentation segment, there is no host interaction.9:41–13:33 · Guest teaching 0/10 Expansion from Niche Market to Ubiquitous Use Cases Ajay argues that all data is fundamentally time series data and shares user case studies. The segment is a solo talk without host participation.13:33–21:36 · Guest teaching 2/10 TimescaleDB Open Source Community & Resources Matt Turck moderates Q&A on open source tactics, followed by audience questions on database architecture. The dynamic is supportive and collaborative throughout.0:00–3:36 · Guest disagreement 1/10 Data Driven NYC Event Title Sequence Ajay presents an overview of TimescaleDB and introduces his presentation on the future of data. This segment is a solo presentation monologue with no host presence.3:36–6:57 · Guest disagreement 1/10 Key Trends: Rise of Machines and Data Resolution Ajay outlines trends in machine-generated data and the evolution of data resolution. The host does not participate in this monologue segment.6:57–9:41 · Guest disagreement 2/10 Summary: Entering the Time Series Era Ajay highlights time series database adoption and presents performance benchmarks against MongoDB and Cassandra. As this is a keynote presentation segment, there is no host interaction.9:41–13:33 · Guest disagreement 1/10 Expansion from Niche Market to Ubiquitous Use Cases Ajay argues that all data is fundamentally time series data and shares user case studies. The segment is a solo talk without host participation.13:33–21:36 · Guest disagreement 1/10 TimescaleDB Open Source Community & Resources Matt Turck moderates Q&A on open source tactics, followed by audience questions on database architecture. The dynamic is supportive and collaborative throughout.0:00–3:36 · Matt pushing back 0/10 Data Driven NYC Event Title Sequence Ajay presents an overview of TimescaleDB and introduces his presentation on the future of data. This segment is a solo presentation monologue with no host presence.3:36–6:57 · Matt pushing back 0/10 Key Trends: Rise of Machines and Data Resolution Ajay outlines trends in machine-generated data and the evolution of data resolution. The host does not participate in this monologue segment.6:57–9:41 · Matt pushing back 0/10 Summary: Entering the Time Series Era Ajay highlights time series database adoption and presents performance benchmarks against MongoDB and Cassandra. As this is a keynote presentation segment, there is no host interaction.9:41–13:33 · Matt pushing back 0/10 Expansion from Niche Market to Ubiquitous Use Cases Ajay argues that all data is fundamentally time series data and shares user case studies. The segment is a solo talk without host participation.13:33–21:36 · Matt pushing back 1/10 TimescaleDB Open Source Community & Resources Matt Turck moderates Q&A on open source tactics, followed by audience questions on database architecture. The dynamic is supportive and collaborative throughout.

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

0:00 · Matt 0% · guest 100%0:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%12:00 · Matt 10.6% · guest 89.4%12:00 · Matt 10.6% · guest 89.4%15:00 · Matt 7.9% · guest 92.1%15:00 · Matt 7.9% · guest 92.1%18:00 · Matt 0.6% · guest 99.4%18:00 · Matt 0.6% · guest 99.4%21:00 · Matt 13.9% · guest 86.1%21:00 · Matt 13.9% · guest 86.1%
Sharpest disagreement ▶ 2:30 Declaring the big data era over

Ajay forcefully challenges the prevailing industry narrative by declaring the big data era over and introducing time series as the new paradigm.

 Hardest push from Matt ▶ 15:49 Host pressing on post-launch traction

Matt pushes Ajay to explain how open source creators avoid silence and build momentum after making a project public.

 Biggest teaching moment ▶ 18:57 Explaining KDB limitations versus SQL

Ajay educates the room on why financial firms struggle to scale KDB across departments due to its proprietary Q language, compared to standard SQL.

 Matt holds his own ▶ 14:05 Host framing open source distribution dynamics

Matt demonstrates industry knowledge by highlighting TimescaleDB's open-source trajectory and directing the discussion toward tactical growth advice.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Data Driven NYC Event Title Sequence 0010 Ajay presents an overview of TimescaleDB and introduces his presentation on the future of data. This segment is a solo presentation monologue with no host presence.
Key Trends: Rise of Machines and Data Resolution 0010 Ajay outlines trends in machine-generated data and the evolution of data resolution. The host does not participate in this monologue segment.
Summary: Entering the Time Series Era 0020 Ajay highlights time series database adoption and presents performance benchmarks against MongoDB and Cassandra. As this is a keynote presentation segment, there is no host interaction.
Expansion from Niche Market to Ubiquitous Use Cases 0010 Ajay argues that all data is fundamentally time series data and shares user case studies. The segment is a solo talk without host participation.
TimescaleDB Open Source Community & Resources 2211 Matt Turck moderates Q&A on open source tactics, followed by audience questions on database architecture. The dynamic is supportive and collaborative throughout.

Statements from this episode (14)

Assertion Not checkable as stated
TimescaleDB surpassed 1 million downloads in its first 18 months
“We've passed a million downloads in 18 months,”
Ajay Kulkarni Sep 17, 2018 ▶ 1:15
Assertion Supported
Connected internet devices outnumbered humans in 2017, excluding phones and PCs
“The number of connected machines, these are machines connected to the internet, not including smartphones and computers last year outnumbered the number of human beings on this planet for the first time.”
Ajay Kulkarni Sep 17, 2018 ▶ 3:38
Assertion Supported
Time-series databases became the fastest-growing database category from 2016 to 2018
“So over the past 24 months, Time Series has emerged as the largest growing, fastest growing category of databases, ah, in the industry.”
Ajay Kulkarni Sep 17, 2018 ▶ 6:34
Opinion
Kulkarni: The big data era is over and the time series era has begun
“The big data era is over, and in fact, we're now entering the time series era for analyzing the past, understanding the present, and predicting the future.”
Ajay Kulkarni Sep 17, 2018 ▶ 6:55
Assertion Not checkable as stated
Kulkarni: TimescaleDB delivers query speeds 100x-1000x faster than MongoDB
“Given the same hardware, we're seeing not just higher insert performance, but hundreds to thousands of times faster queries.”
Ajay Kulkarni Sep 17, 2018 ▶ 9:10
Assertion Not checkable as stated
Kulkarni: 3 TimescaleDB nodes outperformed 30 Cassandra nodes at 1/10th cost
“For time series data, three time scale nodes was able to outperform a 30 node Cassandra cluster at one 10th the cost.”
Ajay Kulkarni Sep 17, 2018 ▶ 9:23
Assertion Supported
Kulkarni: Web eventing and machine learning inferences are time-series data
“Web and mobile eventing data, again, is, is time series data, and even machine learning inferences are a source of time series data.”
Ajay Kulkarni Sep 17, 2018 ▶ 10:08
Insight
Ajay Kulkarni: All data is fundamentally time-series data
“It's actually because we believe that all data is fundamentally time series data.”
Ajay Kulkarni Sep 17, 2018 ▶ 10:42
Insight
Ajay Kulkarni: Non-time-series data storage discards valuable state information
“And in fact, I would argue that not storing your data as time series is throwing away valuable information, because then you're losing how your data changed in the past, how it would be changing today, and how it might change in the future.”
Ajay Kulkarni Sep 17, 2018 ▶ 11:05
Insight
Kulkarni: Open source is the dominant way to distribute software infrastructure
“I firmly believe that, you know, open source has become the, I think, the dominant way to distribute, you know, software infrastructure, you know, whether it's you know, something like Kubernetes, or it's a database.”
Ajay Kulkarni Sep 17, 2018 ▶ 14:26
Disclosure
Timescale pivoted from an IoT platform into a time-series database
“We have close to a 100,000 devices on our platform, and we need a place to store them, and, you know, and we tried a bunch of databases. It didn't work. Long story short, we just built our own time series database but we're still in the IoT business, and we fo…”
Ajay Kulkarni Sep 17, 2018 ▶ 15:01
Assertion Supported
Kulkarni: Elastic filed for IPO making $160M in revenue, more than MongoDB
“I think Elastic, which is Filed Furnace for a IPO, is an example of that. I think they're already making a hundred and sixty million In revenue, which is more than Mongo's making already, which is crazy.”
Ajay Kulkarni Sep 17, 2018 ▶ 16:46
Opinion
Kulkarni argues KDB fails to scale due to its proprietary Q language
“Where the problem with KDB is that, well, number one, it's not open source. But number two it, it's not really, it's not a sequel database, right? There's a really arcane language called Q that's built in APL, which is, you know, looks like Greek, right? And o…”
Ajay Kulkarni Sep 17, 2018 ▶ 19:13
Insight
Kulkarni: Popularity of Kafka will not replace traditional databases
“Like, I don't think the popularity of Kafka means that, that, that databases, you know, go away.”
Ajay Kulkarni Sep 17, 2018 ▶ 21:06
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