Computing Infrastructures and Services for the Course
Having enough computing resources for hands-on is tricky for this course because you might need several machines to setup different services and you can also scale big platforms to a large number of machines. It is not possible for this course due to the lack of resources. We learn big data platforms but we will have limited resources to understand practice issues of big data platforms.
Here are best practices that we have learned from dealing with this issue many years:
- Use free cloud resources in a good way: e.g., you get a Google credit which is not much but you can allocate enough number of instances in a limited time to test your idea. In this case, you cannot learn much about the scalability and elasticity of big data platforms but you can see a minimum configuration.
- Try to use existing scalable cloud services offered by others: such services can be free or paid, e.g., Google Big Query, Cloud AMQP, Cloud MQTT, MongoDB Atlas, etc. You get a limited configuration of components in a big data platform but such components are configured for real-world big platforms so you can learn some.
- Using your own resources: your laptop can be powerful enough to run containers and virtual machines. You can have some mini configurations of big data platforms. E.g., we can run ElasticSearch, MongoDB, Hadoop, etc. in laptops and workstations.
- Using university resources: check resources for students from our university.
Even we dont have enough resources, keep in mind that your designs, development and tests are for big data platforms with minimum configurations.