Databricks Reviews

We use the solution for business engineering.

The solution is very simple and stable.

The tool should improve its integration with other products.

I have been using the solution for around two years.

I would rate the product’s stability a seven out of ten.

I would rate the tool’s scalability a seven out of ten.

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

I would rate the tool’s pricing an eight out of ten.

The tool’s performance is great. I would rate it an eight out of ten.

Our primary use case for the solution is to run batch jobs.

The ease of use and its accessibility are valuable.

The solution can be improved by expanding its integration capabilities and providing the ability to query external vendors directly.

We have been using the solution for a little less than a year, and we deploy it on the Amazon cloud.

The solution is stable.

The solution is scalable, and there are approximately seven developers and two DevOps employees utilizing the solution.

We have had a good experience with customer service and support. I rate them a nine out of ten.

Positive

The initial setup for the solution is a bit complex.

I wouldn't consider it a costly solution. Like all other solutions, it depends on how you use them. If you provision sparked clusters much larger than what you need, it becomes costly. For example, it is not more costly than EMR, the AWS equivalent, and from my perspective, it is much better.

I rate the solution a nine out of ten. The solution is good, but the integration and query capabilities can be improved.

Databricks is very useful and can handle thousands of different use cases. The use cases are all over the place.

Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things.

The most valuable Databricks feature for us is that it does not require us to configure clusters. It automatically configures the clusters to the right size, the right number of clusters, the right number of nodes per cluster, et cetera.

The area in which this product can be improved is optimization. In the next release, I would like to see more optimization features.

I have been using Databricks for a couple of years.

I would say the ROI for this solution is expressed mainly in terms of effort and time.

I would advise that they train themselves before using Databricks. They should figure out which advantages Databricks has over just plain Spark and use it to the best advantage that they can.

I am currently implementing the latest version of Databricks.

The Databricks solution is deployed through Cloud.

I would rate the Databricks solution a nine.

I primarily use Databricks for data pipelines.

Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good.

I've been working with Databricks for around five years.

Databricks is stable.

Databricks is scalable.

Databricks' technical support takes a while to respond and could be improved.

The initial setup was easy.

Databricks' cost could be improved.

I would give Databricks a rating of eight out of ten.

I use this for database machine learning, to construct different models for supermarkets, drug store management, and market involvement to identify business opportunities for clients.

We provide different statistical models and use different algorithms depending on the client.

I was a Lead Data Scientist in different companies. I implement data and build and optimize processes using machine learning techniques, aided by science and advanced analytics.

Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client.

There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable.

It's stable.

It's scalable.

I would rate technical support 4 out of 5.

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.

I would rate this solution 9 out of 10.

My advice is to use the different high analytics methodology, plan for the project, and recognize the different activities for the design.

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.

My company is a customer of Databricks. We use Data Science products for machine learning, engineering, and data preparation.

We have between five and eight people working on coding in Databricks. Indirectly, we have 1500 people consuming the data. We have plans to increase the usage of data bricks by 30% next year.

The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes.

Databricks does not always have clear updates. Often we find an update in the tool but we are not really sure what has changed. We would appreciate better communication from Databricks. It could be in the form of a friendly warning that talks about the updates. 

There would also be benefits if more options were available for workers, or the clusters of the two points.

I have been using Databricks for two years.

Databricks is stable, however, we do find some errors and don't understand what has happened. Usually, they are resolved within a few minutes. I would say it is 95% stable.

Scalability is really good.

I have not had to contact Databrick's support other than through the deployment, which they helped a lot. 

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.

We implemented in-house. It is difficult to find a good consultant or reseller for Databricks in Brazil.

We pay monthly on a pay as you go plan.

With Databricks, you may have a lot of devices. It is important to use each cluster for each kind of process and then not use the small clusters. Using the bigger cluster you will receive better performance and the use is closer and will save you money. 

It is important to code it in parts because if you code it all in full you could find some problems with performance.

I would rate Databricks a 9 out of 10.

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.