Teradata Cloud Analytics Services

In many organizations, the capacity of test and development systems is woefully under-utilized most of the time – but during periods of intense activity, the capacity is often woefully under-sized. It is exactly this type of situation where cloud deployment is ideal. The flexibility to, on-demand, allocate exactly the resources required for a heavy period of test and/or development and then have these resources de-allocated just as quickly when they are no longer needed is very attractive. An added advantage for test and development is the ability to use the very latest versions of software for feature exploitation and/or validation before the software has yet been approved for deployment in a highly governed on-premises environment.

Cloud implementation has two significant advantages for data lab deployment. First is that the requirement for resource elasticity in the projects undertaken by data scientists is typically very high. Scalability in cloud resource allocation provides the perfect working model for a user who cannot easily anticipate resources needed for working with the next new data set or algorithm. The second advantage of cloud deployment for data science work is agility. The internal bureaucracy and timeframes for bringing in a new piece of software on-premises can create friction in data discovery. In a cloud data lab environment, the desired tools are much more likely to be easily accessible without going through onerous IT architecture governance and standards committees.