The setup is not easy but also is not too complicated. An infrastructure needs to be set up first. We use Azure storage or SQL S3 and create private end points. 

This is maybe a little more complex or a bit different than other databases in the cloud. For a traditional setup, you need to also think about file systems and disks. Here, you just transform it into the storage and private end point. 

The first setup might be a bit of a struggle until you learn and understand what is necessary. 

The setup is of average difficulty but tougher than Snowflake. 

Deployment is easy and run time is quick. 

The initial setup is easy for me because I access the solution on a web browser. 

Setup was complex. There were some issues with setting up a database and installing the third party component on top of services. I would rate the setup 3 out of 5.

When we have administration experience, the solution is not difficult to deploy. Technically, however, it's difficult because governance is more complex. For example, I have two warehouses on Databricks, which are clusters in this workspace, and we have to switch from workspace to workspace to have all this information. There is a system table that has all this, but I don't know if everyone can use these tables.

The initial setup phase of Databricks was good. You can spin up clusters and integrate those with DevOps as well. Databricks it's quite nice owing to its user-friendly UI, DPP, and workspaces.

The solution is deployed on the cloud.

The time taken for the deployment depends on the workload.

The implementation is not challenging because the solution integrates well with the platforms on which they are established, whether it's Azure, AWS, or GCP. The solution is not difficult to set up, but you'd probably need a technical user to operate it.

It's the same story with maintenance, where you'd need a technically proficient person with programming knowledge to maintain it.

The implementation is very simple to set up. That's why we choose it over many other tools.

Usually, we have two to five data engineers handling the maintenance and running of our solutions.

The initial setup was okay.

The initial setup was easy because the third-party team made the clusters for us. 

The initial setup was straightforward.

The cloud-based deployment is simple.

If you use an on-premises deployment then there is more to do.

The initial setup was pretty complex and required three people.

The initial setup was relatively straightforward. I would rate it nine, on a scale from one to 10, with one being the easiest and 10 being the hardest.

There is no need to worry about the deployment as it can be done quickly. It is relatively automated. We used Terraform for auto-deployment, which happens in Azure. With Terraform, there are two options. As option one, you can deploy manually by creating services. For option two, you use Terraform and automate. Terraform is like infrastructure as a code where you can code the deployment part using it.

There were two or three persons involved in the deployment of this solution.

The initial setup for this solution is very simple.

The situation may have been a bit different for me than for many users or organizations. I've been in this industry for more than 15 or 17 years. I have a lot of experience. I also took the time to do some research and preparation for the setup. It was straightforward for me.

The deployment with Microsoft usually can be done in 20 minutes. However, it can take 40 to 45 minutes to complete. An organization only requires one person to upload the data and have complete access to the account.

I don't have experience setting up Databricks because that's generally taken care of by the IT, data, or software engineering team before the data science team comes in and starts leveraging the platform. I have yet to experience setting up the Databricks environment personally. However, I have had experience setting up clusters, which was pretty straightforward. Still, in the overall environment of an enterprise-wide system, I have yet to gain experience setting Databricks up.

The initial setup is not that difficult. I rate the ease of setup a seven out of ten. The solution is cloud-based. We use native services like Data Factory for orchestration. Sometimes, the customers require us to use Amazon as the cloud provider instead of Azure.

The implementation is quite easy. It's not complex or difficult. The first time, I did it using a tutorial which was quite helpful. Later, I took a course. I know it quite well. 

The deployment only takes a few days. 

You only need to deploy or maintain the solution. 

Setting up Databricks is easy. I set it up at my previous company. That was on Azure as well, but they utilized a third-party team with expertise in Databricks to ensure everything was optimized. 

The transition to Databricks was smooth. 

The initial setup is very easy. It is a managed solution inside Azure so you just need to search for Databricks. There are a couple of pages to follow in the setup wizard and Databricks is up and running.

The initial setup is easy.

The initial setup was fairly okay. It takes about two minutes to deploy this solution. It's all code, so we click a button, and then it's done.

On a scale from one to five, I would give the initial setup a four.

Setting up Databricks is a bit complex, and the initial deployment took a few days—closer to a week. Of course, not everyone is working full-time on this. There are intervals when people are doing other stuff. 

The initial setup was easy to complete and not complex. It may initially be challenging for a new user, but it improves over time. The CICD pipeline works well with the Microsoft Azure platform because the continuous integration, development and deployment come with the Git integration. It makes it easier for Databricks and the CICD. The deployment should be improved from the perspective of auto ML functionality, so it doesn't have intensive automation learning capability.

We don't use Databricks directly because we work on a data science project. It requires an auto ML and inbuilt machine learning capability. We found capabilities like the large language model using NLP and other deep learning models that are not that intensive. It is meant for data engineering purposes rather than data science purposes. It'll be great if Databricks could be intensive for data science.

We used a third-party, Dataiku platform for the deployment, where we connected to Databricks and completed the ML ops. We required about three people for deployment, and it is easy to maintain the solution.

The initial setup is easy. However, I do not know much about the implementation because the company does it.

The setup is quite easy, and Databricks has also partnered with Microsoft, so we get this service on Microsoft Azure.

Deploying Databricks on the cloud is straightforward. It's not like an on-premise solution, where you must create a cluster and all those other prerequisites for big data. 

I don't think it's challenging to maintain, but you need an expert programmer because Databricks isn't GUI-based. With GUI-based tools, building ETLs is drag-and-drop. Databricks entirely relies on coding, so you need skilled programmers to building your code, ETLs, etc. 

The solution is on the cloud and therefore there isn't really an installation process that you need to go through. You only really need to configure the clusters. 

Within the clusters, you configure according to how many platforms you need, or if you want to, you can build a cluster for artificial intelligence. You just configure it as required. 

The initial setup of Databricks is not straightforward. You need to create VLANs, VPNs, and networks. We are two ways of deployment, we are having the legacy PowerShell for the deployment and the template method to deploy the Databricks code to higher levels.

We have not integrated Databricks directly into the DevOps architecture. We are downloading the notebooks manually and we are uploading them.

The initial setup is not very easy, but it's medium in complexity.

The initial setup of Databricks is simple. I did not experience any challenges. The time it takes for the deployment is approximately four hours.

I rate the initial setup of Databricks.

The initial setup of Databricks is more complex. I would rate it a four out of five on the complexity of the setup. It took two days to deploy the solution.

The initial setup of the solution is straightforward, once you understand the UI it is easy to implement. I would rate Databricks a four out of five for ease of setup.

One migration project took two to three months, including writing all the code and implementing end-to-end pipelines. 

We are planning to deploy the solution in stages over the next 15 months to completely implement MLOps for our organization.

The initial setup is pretty simple and requires minimal configuration compared to other technology.

I am not a data engineer because I just started data science at the company, but it was straightforward and clear for the architect to set up. He provided me with that idea because he realized it would take time if we had use cases. You can select and change the data or add some modules or products. You have all the technology to do so.

The initial setup is difficult. 

While I don't know exactly how long the deployment took, I do know that it lasted longer than the one day needed for Alteryx. 

Setup and Support are single-click.

It was relatively simple, we didn't face any challenges. Deployment takes around two days. 

The initial setup was not very complex. We deploy the solution manually and the time required depends on the complexity of the business logic. I rate it an eight out of ten.

There is no installation required. It is easy to use, for example, in Azure it is available, you subscribe, and use it.

The solution is very easy to setup. I would rate its setup a ten out of ten.

There is no installation required.

It was pretty easy to set up. At least, that is my understanding. I'm not the data engineer though. I don't actually do installs and configurations. I explore features and build them in my architecture designs.

The first deployment is difficult. It is not straightforward and you have to think about a lot of stuff. It is not really like a SaaS deployment and there are a lot of steps that you have to take.

The initial setup depends on the readiness of the team working with Databricks. There is no one template saying that it's easy, and it isn't easy. It can be complex to set up if you don't have a really good plan.

You can get in this environment at least for a test. You can do it in the lab, follow it step by step, and that'll take about an hour. The difficulty depends on the business requirements. 

If it's for training purposes, you can do it in about half an hour, and you're good to go. If you need it to support a business, it will be much more rigorous because multiple divisions would be interested in running their own environment, working with their data.

The setup is straightforward, I did it myself. 

The initial setup was not complex at all. The documentation is good. It is clear and not very difficult to understand. Because the documentation is good, the installation is fine.  

We did the implementation by ourselves — within our team and with the help of the documentation. But I would not say that we have already deployed the model yet. This is an ongoing process, as there are certain inputs that changed over time.  

So we have not implemented the product completely, but we have gotten to advance with the product and our understanding of it. It is good, but our company is still trying to get much better data from it. At this point, it is like the data is just junk and more junk. So we are now working toward that goal of improving the result. Whenever the data result gets better, we'll try to implement the workflow to see how it performs. I would say it will probably take two to three months more before we actually get good data.  

The setup was straightforward. It also depends on the projects.

The initial setup of Databricks was not straightforward. We had to do trial and error and we learned as we went along.

I rate the initial setup of Databricks a four out of five.

Setup isn't difficult. We used about 15 people for deployment and maintenance. We have data scientists and statisticians using this solution and doing different analyses.

The initial setup of Databricks is straightforward and simple. It is not complex because they provide a lot of documentation. The deployment was fast, it took less than three days with five people assigned to the task.

The initial set was very straightforward because it's also in our Azure cloud so it was quite easy to set up and configure. Very intuitive.

It is not difficult to deploy this solution because it is well documented. We followed the normal steps that included all of the APIs.

The installation is straightforward, and it took approximately one hour.

I found the initial setup easy because I had previously worked on Spark.

If somebody goes through the training, which is available on the website, then it should be straightforward. I don't think that it is very hard.

When it comes to developing things based on use cases, it can take between three days and two weeks, plus two to three days for testing and deploying it. I would say that for an entire use case, it will take a maximum of three weeks.

The initial setup was not very complex. We had it up and running in no time; it's a quick process.

The initial setup for the solution is a bit complex.

The initial setup was easy.

The initial setup is easy. It's not difficult when you are used to Azure.

The initial setup is straightforward. I wouldn't say that it's complex in any way.

Deployment times vary and really depend on multiple factors. It can take anywhere from a few weeks to a few months to deploy the solution. In our case, it took us about three months to fully deploy it.

It takes two to three people to deploy the solution.

The initial setup is straightforward. With respect to deployment, the development can be done within half an hour and we can use code and deploy from there.

The initial setup is very straightforward. We just use their job functions. To deploy as a spark job is quite straightforward. 

In our use case, we also had some external databases to handle the deployment. For example, we only generated some prediction results. We saved the results into an external database. The solution takes time to deploy to the external database, but the spark job is quite easy.

The installation was straightforward because it is on the cloud. The full deployment took approximately one week.

The initial setup was a little complex because it was a new architecture for the customer, so there was nothing to compare it to in order to accelerate the project. This meant the deployment of the first project using Databricks took almost nine months and the second took almost a year.

The initial setup was straightforward.