Matvey Arye
Software Developer at Timescale
Mat has been working on data infrastructure in both academia (Princeton, PhD) and industry. As one of TimescaleDB's core architects he works on performance, scalability, and query power. Previously, he attended Stuyvesant, The Cooper Union, and Princeton.
Upcoming presentations
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Past presentations
Time-series workloads (i.e. data from sensors, IoT devices, finance, or even satellites) are one of the fasting growing segments of the database market, spreading across industries and use cases. Today, many developers working with time-series data turn to NoSQL databases for storage with scale, and relational databases for managing associated metadata and key business data, yet this leads to e...
Jul 19 2019
Gap Filling: Enabling New Analytic Capabilities in Postgres
How Timescale’s open source extension increases the readability, performance, and efficiency of “gap-filling” queries.
One property of real-time data is that it often arrives at irregular intervals. An example of this is minute-by-minute averages of temperature sensors where data arrives from a sensor that is intermittently offline.
When querying such data, we often want results even for minutes that do not have data data reported from the sensor. In such cases, we often want data for such “gaps” reported ...
Mar 21 2019
Using TimescaleDB for time-series storage and analytics in Postgres
An introduction to TimescaleDB, a Postgres extension, and use cases
An Introductory Training on TimescaleDB
TimescaleDB is an open-source time-series database, implemented as a Postgres extension, that improves insert rates by 20x over vanilla Postgres and offers much faster queries, while natively supporting full SQL (including JOINs). TimescaleDB achieves this by storing data on an individual server in a manner more common to...
Oct 15 2018
Time-series databases are one of the fasting growing segments of the database market, spreading across industries and use cases. Common requirements including ingesting high volumes of structured data; answering complex, performant queries for both recent and historical time int...
Oct 16 2018
Building a scalable time-series database on PostgreSQL
TimescaleDB, packaged as a PostgresSQL extension
Today everything is instrumented, generating more and more time-series data streams that need to be monitored and analyzed. When it comes to storing this data, many developers often start with some well-trusted system like PostgreSQL, but when their data hits a certain scale, give up its query power and ecosystem by migrating to some NoSQL or other "modern" time-series architecture. They face t...
Nov 14 2017
Grafana and Prometheus have become a popular duo for collecting, querying and graphing metrics, giving teams greater clarity on their operations. But while Prometheus has its own time-series storage subsystem specifically for metrics monitoring, many have found they require something for long-term, persistent storage that also allows more complex queries to be run across a larger dataset.
In...