Having a sticker indicating the usage of AI for some tasks doesn’t mean that the task will be completed successfully: there are many ways to solve a problem. So, if you have a problem to solve with your data, why not use the best set of tools to solve it? That’s the goal of Texter Machine Learning (TML).
Texport is a tool that was built to migrate content between Alfresco repositories, in the most easy and clean way possible. It was developed with a special focus on doing a fast migration even of repositories with large data sets.
As a “deep dive”, the objective of this article is to explain in a more specific and technical way, how it works. Nonetheless, if you want, you can check more generic information about the Texport tool in here!
Content migration has always been one of my favourite topics to explore and during my work practice, I had the opportunity to participate on several successful and not so successful migration projects 🙂. This post represents my view on a content migration strategy that is focused on efficiency, performance and added value for organisations and stakeholders.
Alfresco repository Caches optimisation can have significant impact on the performance of your Alfresco deployment. This post provides an overview on how the repository caches are implemented by Alfresco.
The Alfresco repository leverages and provides in-memory caches. Memory caching (often simply referred to as caching) is a technique in which computer applications temporarily store data in a computer’s main memory (i.e., random access memory, or RAM) to enable fast retrievals of that data.