Databricks Reviews

We are building internal tools and custom models for predictive analysis. We are currently building a platform where we can integrate multiple data sources, such as data that is coming from Azure, AWS, or any SQL database. We integrate the data and run our models on top of that.

We primarily use Databricks for data processing and for SQL databases.

I found that PySpark is the most useful tool. It uses in-memory calculation and when you want to run a model it does it very quickly. We used to use Python and when we migrated to PySpark the performance was much better.

It would be very helpful if Databricks could integrate with platforms in addition to Azure.

Having an open-source version or having the option to get a trial version of Databricks would be very helpful.

It would be very useful for beginners if there were tutorials and examples on how to write code for PySpark, R, or Scala. Having examples would give people something to refer to and play with.

We have been using Databricks for the past two or three years.

A couple of times I faced an issue where a long-running process was consuming a lot of time and then stopped abruptly. It necessitated starting the process again.

We are in the prototyping stage so we do not plan on increasing our usage yet.

We have not been in contact with technical support.

Before using Databricks, we were running our own cluster with a web server that executed our Python queries.

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.

We implemented Databricks on our own. We haven't deployed as such, as we are just running our queries and it is not in production yet.

I work in the data science field and I found Databricks to be very useful. If I want to run any models then I can code them in PySpark. If you are coming from a Python background then you can write code in PySpark and it runs quickly. This is a good solution in terms of performance. 

I would rate this solution a nine out of ten.

We primarily use the solution to run current jobs; to run the spark jobs as the current job.

The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient.

The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets.

The support could be improved a bit around the database. When we stream it to Data Lake, some data cannot be loaded. It should be a priority to fix this.

I've been using the solution for half a year.

The solution is stable.

The solution is scalable. However, it still needs us to manually set out the number of nodes in a cluster. It's really dependent on the application. Sometimes, when the tasks are bigger, and it gets a little difficult for us to define the number of nodes in a cluster. If the solution could allow users to set up the clusters, I think that'll be good.

Currently, we have three people using the solution. We may increase usage in the future.

The technical support is quite good. In the beginning, when we had a few POC projects, they were very supportive.

We didn't previously use a different solution, however, we built our own from scratch. This is the first unified platform that we've used.

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.

I'm a software development engineer. I'm working with the latest version.

As long as the developers have an understanding of spark, and understanding technical tricks, it's very fast in terms of using the database.

I'd rate the solution eight out of ten.

We use Databricks for experimentation. For example, we do ML model building and training that is connecting to our data which resides in Azure. It offers very good integration with Azure. We've deployed some of our model inference tools in Databricks.

 Databricks integrates well with other solutions.

Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage.

Along with having connections to different databases for Git tools, adding libraries for easy access would be a benefit. As data scientists, we connect to different databases and different sources of data, having a library would be useful.

I have been using Databricks for approximately one year.

The solution is stable. We did not face any downtime.

Databricks is scalable. It operates three times faster than any of the other ecosystems which we have experimented on.

We have approximately five data scientists using this solution in my organization. We are a small company and as we grow, all our data scientists would be using this platform. We plan to increase usage.

The technical support is good. We didn't need a lot of support. There were a few times we needed some help on how to do certain operations.

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

We did the implementation of Databricks in-house. It only requires one person for the maintenance of the solution.

My advice to others wanting to implement this solution is to use a cloud environment. For example, we are using Azure with Databricks. It is much better than doing an on-premise implementation.

I rate Databricks an eight out of ten.

I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job. So you can create a robust solution by working together with other professionals.

One area for improvement would be that anyone who doesn't know SQL may find the product difficult to work with. It would also be useful to have a remote support team inside Databricks, which would collect and analyze user feedback.

I have been using Databricks since 2018.

I had a little trouble with customer support but this was solved.

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.

We are still exploring the solution. We utilize it much, much better than their star schema models that they are trying to replace it with. We bring in Databricks and then see how they can leverage the additional analytical functionalities around the Databricks cloud. It's more in exploratory ways. We recommend Databricks, especially with the Azure cloud frameworks.

Pricing is one of the things that could be improved.

Also, there could be improvement in the visual analytics space there and on the machine learning functions. I haven't explored so I don't know about the functions and features that are there. If it is not there, then I think that's something which they should consider including.

My team has been exploring Databricks for close to five or six months.

I haven't heard about any major stability issues. At this time I feel like it's stable.

In terms of scalability, I think once we put it across for larger use-cases the scalability question will really arise. So we'll need detailed information. I assume that we will be able to scale up.

I think we do not have more than 10 people working on it now. Because we are in the earlier stages of implementation, it's more like a POC now. I really don't know whether it's been open for the larger audience yet.

The initial setup was straightforward.

It is better to be installed with the help of integrators, or consultants, or with an experienced team.

It's more data scientists using Databricks. I would call them power users trying to see how they can get a hand on it, though they are not data scientists. They try to understand it a little bit better for their future use.

On a scale of one to ten, I would rate it an eight, easy. 

Databricks is the full data analytics platform. It involves end to end data analytics process.

Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution.

Databricks could improve in some of its functionality.

I have been using Databricks for approximately a year and a half.

Databricks is very stable.

The scalability of Databricks is good.

We have 30 to 40 people are using this solution in my company.

I rate Databricks a nine out of ten.