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 leverages SQL capabilities to process large datasets, offering high performance, seamless integration with Spark programs, and the ability to run parallel queries. It supports Hive interoperability and facilitates data transformation with DataFrames and Datasets.Spark SQL enables efficient data engineering, transformation, and analytics for organizations dealing with large-scale data processing. It supports big data queries, builds data pipelines and warehouses, and interfaces with...
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.