Percona

DECEMBER 27, 2023

of respondents are currently utilizing databases in Kubernetes (k8s). These indicators suggest that the adoption of databases on k8s is in its early stages and is likely to continue growing in the future. It comprises numerous organizations from various sectors, including software, hardware, nonprofit, public, and academic.

Cloud 79

Cloud Open Source Strategy Best Practices 79

Adrian Cockcroft

JUNE 21, 2022

I became the Sun UK local specialist in performance and hardware, and as Sun transitioned from a desktop workstation company to sell high end multiprocessor servers I was helping customers find and fix scalability problems. We had specializations in hardware, operating systems, databases, graphics, etc.

Tuning 52

Tuning Benchmarking Engineering C++ 52

Percona

OCTOBER 18, 2023

Migrating a proprietary database to open source is a major decision that can significantly affect your organization. Today, we’ll be taking a deep dive into the intricacies of database migration, along with specific solutions to help make the process easier.

Open Source 91

Open Source Database Hardware Strategy 91

Percona

MARCH 23, 2023

Hardware Memory The amount of RAM to be provisioned for database servers can vary greatly depending on the size of the database and the specific requirements of the company. Storage The type of storage and disk used for database servers can have a significant impact on performance and reliability.

Performance 124

Performance Hardware Tuning Storage 124

O'Reilly

NOVEMBER 28, 2023

If we asked whether their companies were using databases or web servers, no doubt 100% of the respondents would have said “yes.” Training models and developing complex applications on top of those models is becoming easier. And only 33% report that their companies aren’t using AI at all. We’ll say more about this later.)

Open Source 108

Open Source Artificial Intelligence Google Infrastructure 108

All Things Distributed

MARCH 19, 2018

For example, training on more data means more accurate models. Last re:Invent, to make the problem of authoring, training, and hosting ML models easier, faster, and more reliable, we launched Amazon SageMaker. Machine learning models are usually trained tens or hundreds of times. In machine learning, more is usually more.

Scalability 159

Scalability Hardware AWS Tuning 159

O'Reilly

OCTOBER 11, 2022

However, one thing should be obvious: to fill a prescription, you need to access many different kinds of data, in many different databases. Andrew Ng , Christopher Ré , and others have pointed out that in the past decade, we’ve made a lot of progress with algorithms and hardware for running AI. Was it trained using biased, unfair data?

Games 116

Games Healthcare Processing Retail 116