Operations | Monitoring | ITSM | DevOps | Cloud

The world of FinOps can be pretty complex. Don’t get us wrong, it’s a fantastic way to align IT and finance teams for maximum efficiency in cloud operations. But for newcomers, it may feel a bit overwhelming at first. That’s why following influential leaders in the FinOps space is a must. The tips, insights, and guidance from these top FinOps performers can give you the confidence and motivation to lead FinOps at your company.

Users with real-time and other analytic workloads want or need to keep large volumes of historical data to aid in important activities, such as ad hoc historical trend analysis and training AI models. However, storing this much data in a way that also makes it easily queryable becomes prohibitively expensive. As a result, users must balance data availability and usability with sacrificing data fidelity and storage costs. That is until now.

Are you interested to learn about the characteristics of Elasticsearch for vector search and what the design looks like? As always, design decisions come with pros and cons. This blog aims to break down how we chose to build vector search in Elasticsearch.

In the ever-evolving world of search engines, Elasticsearch, OpenSearch, and Solr have long held the spotlight. However, there are several smaller search platforms that pack a punch and offer compelling alternatives. In this article, we will explore 11 small search platforms, delving into their major features, pros, and cons.

Maybe you came across the term “vector database” and are wondering whether it’s the new kid on the block of data retrieval systems. Maybe you are confused by conflicting claims about vector databases. The truth is, the approach used by vector databases has been around for a few years.