article thumbnail

Why you need Dynatrace on Azure Workloads

Dynatrace

Hopefully, this blog will explain ‘why,’ and how Microsoft’s Azure Monitor is complementary to that of Dynatrace. Do I need more than Azure Monitor? Azure Monitor features. A typical Azure Monitor deployment, and the views associated with each business goal. Available as an agent installer). How does Dynatrace fit in?

Azure 135
article thumbnail

Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources.

Cache 202
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

How multicloud observability boosts cloud performance at Tractor Supply Co.

Dynatrace

shifted most of its ecommerce and enterprise analytics workloads to Kubernetes-managed software containers running in Microsoft Azure. “Caching’s one of the key components of any commerce application,” as it has a major impact on performance, Bollampally said. ” Three years ago, Tractor Supply Co.

Cloud 171
article thumbnail

What is a Distributed Storage System

Scalegrid

Distributed storage technologies use innovative tools such as Hive, Apache Hadoop, and MongoDB, among others, to proficiently deal with processing extensive volumes encountered in multiple-node-based systems. Amazon S3 and Microsoft Azure Blob Storage leverage distributed storage solutions.

Storage 130
article thumbnail

The Amazing Evolution of In-Memory Computing

ScaleOut Software

From Distributed Caches to Real-Time Digital Twins. Emerging in the early 2000s, the first such platforms provided distributed caching on clustered servers with straightforward APIs for storing and retrieving in-memory objects.

article thumbnail

The Amazing Evolution of In-Memory Computing

ScaleOut Software

From Distributed Caches to Real-Time Digital Twins. Emerging in the early 2000s, the first such platforms provided distributed caching on clustered servers with straightforward APIs for storing and retrieving in-memory objects.

article thumbnail

Content Management Systems of the Future: Headless, JAMstack, ADN and Functions at the Edge

Abhishek Tiwari

Most of the CMS vendors dodge questions of evolution by talking about incremental innovation primarily focused on customer experience (CX) such as analytics and personalisation. There is hardly any innovation from traditional CMS vendors. This directory can be uploaded to a server and served using a web server such as Apache or Nginx.

Systems 63