What is Rundeck?
Rundeck is open source software that helps you automate routine operational procedures in data center or cloud environments. Rundeck provides a number of features that will alleviate time-consuming grunt work and make it easy for you to scale up your automation efforts and create self service for others. Teams can collaborate to share how processes are automated while others are given trust to view operational activity or execute tasks.
Rundeck allows you to run tasks on any number of nodes from a web-based or command-line interface. Rundeck also includes other features that make it easy to scale up your automation efforts including: access control, workflow building, scheduling, logging, and integration with external sources for node and option data. Continue reading
The more we travel in time, towards future, cloud computing keeps on captivating us with its mysterious enticing.The competition is heating up in the public cloud space as vendors regularly drop prices and offer new features.I will shine a light on the competition between the three giants of the cloud:Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft’s Azure. Continue reading
Infra/Dev-ops team do have instance created for POC/Demo/testing purpose which we need to stop daily (office off-hours) or during weekends for cost saving purpose. As this adds an overhead for us to daily stop the instance manually before leaving office and some times we might forget to stop the instance which again will add up the cost.So there was a demand to automate this process in order to save cost. Continue reading
Introduction: Every dataset has two type of variables Continuous(Numerical) and Categorical. Regression based algorithms use continuous and categorical features to build the models. You can’t fit categorical variables into a regression equation in their raw form in most of the ML Libraries. If it is not included in the modeling, then you do not get an accurate model. It’s crucial to learn the methods of dealing with such variables. There are many machine learning libraries that deal with categorical variables in various ways. Approach on how to transform and use those efficiently in model training, varies based on multiple conditions, including the algorithm being used, as well as the relation between the response variable and the categorical variable(s). Here I take the opportunity to demonstrate the various methods prevalent and incorporated in the popular Machine Learning Library in Spark, i.e.Mllib for handling categorical variables. Continue reading
Talentica believes in continuous learning and innovation. We the Talenticians have always been encouraged to undertake learning and experimenting with emerging technologies. With the same objective, we have setup an internal R&D group working on upcoming areas. Software defined networking (SDN) is one the areas where we are developing proficiency.
As a part of SDN group, we are working on a measuring Hadoop Map Reduce shuffle phase network transfer and also possible traffic engineering solutions for optimizing shuffle phase network transfer. While working on the same, we encountered several challenges, this blog is highlighting solution to one the challenges we faced. Continue reading