The Ledger

Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.

5statements match

0resolved → 0open claims → 0supported → 0contradicted →

why aren't all 5 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable

Insight

Why traditional databases fail at time series workloads

“And actually no Regular database is designed with this kind of workload in mind. Databases, for the most part, assume that you want to keep your data around for a very long time. So, these kind of unique aspects of time series make it kind of a degenerate case…”

Paul Dix Jun 12, 2019 ▶ 5:47 What's Next for Open-Source Time Series Data? // Paul Dix, Influx Data (FirstMark's Data Driven NYC)

Insight

DevOps metrics and IoT sensor data spaces look surprisingly similar

“And those, the sensor data and the DevOps data spaces, they look surprisingly similar. Because when you think about DevOps data, and you think about the metrics that you're collecting, the sensors are just software that you have on your servers. Whereas in sen…”

Paul Dix Apr 2, 2015 ▶ 1:52 Paul Dix, InfluxDB // Open-Source Time Series Database // Data Driven NYC (FirstMark Capital)

Insight

Building analytics apps in 2015 resembles web development in 1998

“Building an application with an analytics component today is like building a web application in 1998. You spend months and millions of dollars building infrastructure before you get to the actual thing you want to build that's driving user value, right?”

Paul Dix Apr 2, 2015 ▶ 7:10 Paul Dix, InfluxDB // Open-Source Time Series Database // Data Driven NYC (FirstMark Capital)

Insight

Dix: Ship code to where data lives, not data to code

“This is the key thing that we learned from Hadoop and Google's MapReduce framework, which is You want to ship the code to where the data lives, not the other way around.”

Paul Dix Apr 2, 2015 ▶ 5:16 Paul Dix, InfluxDB // Open-Source Time Series Database // Data Driven NYC (FirstMark Capital)

Insight

Paul Dix: Regular time series data summarizes irregular event data

“The thing that's interesting about irregular time series data is that you can actually induce a regular time series from irregular time series data. Basically, a regular time series is just a summary of an irregular Series.”

Paul Dix Jun 12, 2019 ▶ 2:37 What's Next for Open-Source Time Series Data? // Paul Dix, Influx Data (FirstMark's Data Driven NYC)

The Ledger

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