DoK Talks #132 - Time-series on SQL Server on Kubernetes on ARM64… without SQL Server! // Álvaro Hernández

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3+ y ago 1:05:15

M4A•Thuis aflevering

https://go.dok.community/slack

https://dok.community/

ABSTRACT OF THE TALK

Oh wow. What a weird title. Full of terms that don’t fit together. Or do they?

This talk is for believers, those who believe that the author will deliver what the title says. Here’s the promise on what you will see:

* Time-series specialized storage and functions on SQL Server.
* SQL Server running on Kubernetes.
* Using ARM64 (aarch64) nodes.
* In reality, without using SQL Server, but rather Postgres Babelfish!

The talk will be focused on a demo to show all these components working together. Surely the demo will fail badly, so get ready your rotten tomatoes! The good news is that you will be able to reproduce the demo on your environment too, as all the software used is open source and source code will be provided.

BIO

Álvaro is a passionate database and software developer. Founder of OnGres (one of the founding sponsors of DoK), he has been dedicated to PostgreSQL and R&D in databases for more than two decades.

Álvaro is at heart an open source advocate and developer. He has created software like StackGres, a Platform for running Postgres on Kubernetes. As a well-known member of the PostgreSQL Community, Álvaro founded the non-profit Fundación PostgreSQL and the Spanish PostgreSQL User Group. He has contributed, among others, the SCRAM authentication library to the Postgres JDBC driver.

You can find him frequently speaking at PostgreSQL, database, cloud (becoming an AWS Data Hero in 2019), and Java conferences. In the last 10 years, Álvaro has completed more than 100 tech talks (https://aht.es).

KEY TAKE-AWAYS FROM THE TALK

A key to progress is to combine technologies in novel ways to bring new capabilities to environments that typically lack them. For example, using Postgres Babelfish (a SQL Server-compatible Postgres flavor) and Timescaledb (time-series extension for Postgres) we can deliver open source time-series native capabilities on top of SQL Server protocol, available to SQL Server users.

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