Stratio is excited to announce our new connector for Tableau. Stratio is now a Tableau Connector Gallery launch partner! This means users can now find and download the connector of Stratio Crossdata directly from the Tableau Extension Gallery. This new approach enables Stratio to continue building and simplifying the way Stratio users connect to Tableau.
The Information Technology industry is sometimes perceived as a mostly-male one, and International Women’s Day (March 8) seems a good occasion for a little fact-checking: are women new to this business? Do we have any female references in IT-related fields? Sure, we all recall Ada Lovelace, considered the first programmer due to her work on Charles Babbage’s Analytical Engine, but… what about others? Let’s meet some women who, from a technical point of view, have patently influenced information technologies.
How serverless works:
Serverless means literally “without server” and it refers to a way to deploy and execute applications where the owner of the application doesn’t have to worry about the server and infrastructure used to serve it. The only responsibility of developers is to write the code needed for the application to work properly and the cloud provider is the one who has to dynamically provision the server, deploy the application in it, and make it work.
Because the code is totally independent of the platform and server on which it will be deployed, the developer doesn’t need to define endpoints or listeners to communicate with the outside world, instead of this, the cloud provider is the one responsible for providing the means to communicate with other services using events that trigger the execution of the application logic.
MLflow, the open-source platform released by Databricks in June 2018, has found a quick and broad acceptance in companies around the world. As stated by Databrick’s co-founder and CTO Matei Zaharia in their presentation post, there were a lot of different tools prior to this, created to track and/or take ML models into production, but they were either proprietary, language-dependent, or able to address only one aspect of the complete Machine Learning lifecycle. MLflow aims to be open in all senses – open source software but also language-agnostic (open interface).
These past months have been filled with uncertainty, we have been living a pandemic caused by Covid-19 that has changed our lives and our daily habits; working every day from home, juggling and balancing our work and personal life. Difficult times often bring out the best in people, and this has been no different in Stratio, Stratians has been giving the best version of themselves.
Talent is that word we say several times a day and what companies fight for, particularly those in industries where – unfortunately – it is scarce.
Talent is something one is born with – but also something that one nurtures and grows, especially in the particular case of work environments. Individual talent in a collaborative culture – something which should be the norm in the technology industry – is however in short supply; in fact, it can even be counterproductive.
Limitations of stateless services
Over the last few years and even now, we usually hear that in order to build a microservices architecture or any distributed system, the services must be stateless. This way if we need, we can scale out adding more instances to face all the traffic that arrives. However, these systems also need to store the state in some way. And how do they do it? Well, because they can not store it in the service itself, it has to send it far away from the logic that must have to process it. Normally this datastore can be either a relational or document database.
This post shows how to solve a recurrent problem when using highly-available virtual routers in AWS: floating IPs.
This approach uses a python script for the new master router to claim an EC2 Secondary private IP in the failovering transition.
Welcome back to our series on Swarm Intelligence Metaheuristics for Optimization! In part 1, we talked about a family of metaheuristic algorithms known generically as Ant Colony Optimization (ACO), which were specially well-suited for combinatorial optimization problems, i.e. finding the best combination of values of many categorical variables. Recall we define Metaheuristics as a class of optimization algorithms which turn out to be very useful when the function being optimized is non-differentiable or does not have an analytical expression at all.
In the previous post about Apache Ignite, we learnt how to set up and create either a simple cache or a sql cache, and share the cached data between different nodes. In this post, we will dig a little deeper. We will see what to do if our app crashes because the cached data has disappeared. How could Ignite help us avoid this problem?