It's an open-source product. I don't know much about the licensing aspect. 

It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project.

If I propose using Spark for a project, one of the first questions I get from management is about the cost of Databricks Spark on the cloud platform we're using, whether it's Azure, GCP, or AWS. If we could reduce the collection, system conversion, and transformation network costs by even just 2% to 3%, it would be a significant benefit for us.

They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.

Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera. But in that case, you don't have any support. If you face a problem, you might find something in the community, but you cannot ask Cloudera about it. If you have open source, you don't have support, but you have a community. Cloudera has different packages, which are licensed versions of products like Apache Spark. In this case, you can ask Cloudera for everything.

Apache Spark is an open-source tool. It is not an expensive product.

On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing. Managing costs in a cloud environment can be challenging due to the cumulative expenses associated with running and maintaining Spark. Licensing costs may not be the primary concern, but operational costs in the cloud can add up. For on-premises deployments, maintenance costs include cluster management, job optimization, and upgrades. In the cloud, maintenance costs are relatively lower, especially with managed database clusters, but they still exist and primarily revolve around cluster upkeep.

Considering the product version used in my company, I feel that the tool is not costly since the product is available for free.

The licensing costs for Spark would depend on the specific packages and the needs of the project. Costs can vary based on requirements, affordability, and customer expectations. 

Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. 

If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources. 

The licensing arrangements can differ based on the product and service. Some products might require a license purchase upfront, with subsequent charges based only on usage. 

The availability of hybrid benefits can also influence licensing costs, especially if you're using third-party services like Palo Alto in a VM from the marketplace. If you have an existing license, your costs could be reduced, but purchasing a new license would include licensing fees in the overall cost.

Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises.

The product is expensive, considering the setup. However, from a standalone perspective, it is inexpensive.

The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks.

We are using the free version of the solution.

We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud. 

Licensing costs depend on where you source the solution. 

The solution is moderately priced. 

It is an open-source platform. We do not pay for its subscription.

The solution is affordable and there are no additional licensing costs.

Apache Spark is an expensive solution.

The deployment time of this solution is dependent on the requirements of an organization, and the compatibility of the systems they will be using alongside this solution. We would recommend that these are clearly defined when designing the product for the businesses needs.

Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free.

It is an open-source solution, it is free of charge.

Spark is an open-source solution, so there are no licensing costs. 

I'm unsure as to how much the licensing is for the solution. It's not an aspect of the product I deal with directly.

I would suggest not to try to do everything at once. Identify the area where you want to solve the problem, start small and expand it incrementally, slowly expand your vision. For example, if I have a problem where I need to do streaming, just focus on the streaming and not on the machine learning that Spark offers. It offers a lot of things but you need to focus on one thing so that you can learn. That is what I have learned from the little experience I have with Spark. You need to focus on your objective and let the tools help you rather than the tools drive the work. That is my advice.

Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.

This is an open-source tool, so it can be used free of charge. There is no cost involved.

So far we have no plan to switch to commercial license.

The initial setup is straightforward. It took us around one week to set it up, and then the requirements and creation of the project flow and design needed to be done. The design stage took three to four weeks, so in total, it required between four and five weeks to set up.