We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.
The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive. I would rate the pricing a seven or eight out of ten however, it is easy to run into pricing issues with something like a Databricks cluster if you don't manage usage properly.
Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers...
We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.
We use the open-source version, so we do not have direct support from Apache.
The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive. I would rate the pricing a seven or eight out of ten however, it is easy to run into pricing issues with something like a Databricks cluster if you don't manage usage properly.
The solution is bundled with Palantir Foundry at no extra charge.
The solution is open source but you pay for any extra features.
There is no license or subscription for this solution.
The solution is open-sourced and free.
The pricing of Apache is much more competitive than IBM.