What is CloudxLab?
CloudxLab is a cloud based virtual lab for practicing Big Data (Hadoop, Spark etc), Machine Learning and Deep Learning technologies.
While training students on Big Data technologies at KnowBigData, we realized that our learners were facing a lot of trouble downloading and configuring virtual machines (VM) provided by major Hadoop vendors. Most often, these virtual machines were slow and would not allow for use of any other application on the same computer.
Moreover, working on a VM did not give a real world experience as one is still dealing with only one machine instead of a cluster of machines which is the whole idea of Big Data technologies which are primarily based on distributed computing.
This is how CloudxLab was conceptualized in an effort to resolve these pain points of learners. The video below will help understand how one of our clients – Simplilearn – is using CloudxLab to provide a better learning experience to their course takers.
Practice any where, anytime, using any operating system. You will just need an internet connection and an account with CloudxLab. You can login to CloudxLab via any device, any browser and start practicing Big Data technologies. No more virtual machines. Only practise in real time.
Here is the brief list of technologies one can practice on CloudxLab. We will keep adding more technologies.
1.Ambari- Ambari is used for Provisioning, Managing and Monitoring Hadoop clusters, it can
- Install A fresh cluster
- Add or Remove Nodes
- Add, Remove or Move services between Nodes
2.Hue- Hue is a Open Source Web Interface to analyze data with Hadoop. It provides interface for HDFS, Pig, Hive, Hbase, Oozie, Spark and Zookeeper.
3.Web Console- It is a browser-based application to execute shell commands via SSH, from web console we can run MapReduce, Hive, Pig, HBase, Spark…etc.
“To provide a platform for learning, practicing and innovating with Big Data, Machine Learning and Deep Learning technologies”
We aim to provide a seamless, real time, learning experience. It has opened doors for us to experiment with distributed Machine Learning and Deep Learning technologies.