Databricks Reviews

The primary use is for data management and managing workloads of data pipelines.

Databricks can also be used for data visualization, as well as to implement machine learning models. Machine learning development can be done using R, Python, and Spark programming.

Databricks gives you the flexibility of using several programming languages independently or in combination to build models.

The quick visualization of the data is very good.

The workload management functionality works well.

Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks.

I have been using Databricks since 2017. I am no longer using it personally, although my team is, and will continue to do so in the future.

Databricks is quite popular these days and it appears to be stable. I have not found any issues with stability.

Databricks is scalable, regardless of which cloud provider is being used. It is supported on Microsoft Azure, AWS, and they have their own cloud as well.

For a small workload, Databricks may not be worth the costs. However, for larger workloads, Databricks is a very good solution.

In my previous organization, there were between 10 and 15 users.

The technical support is handled by Microsoft partners and because we had premium support, it was easy to get. That said, I did not require any support.

I have not used tools that are similar to Databricks for workload management, but Azure ADFv2, Google BigQuery, SAS are some the most powerful tools in this space, that I have used in the past. I have also heard of Dataiku and other tools but I have not used them. The only things that I have used are tools written in Python or scripting languages.

There is no installation required.

Databricks uses pay-per-use model, where you can use as much compute as you need. I think that the cost can be reduced, given that there are more users on the platform, although it is not as expensive as some other solutions like SAS.

As we transition to the Azure cloud, I expect that we will be using Databricks for workloads.

This is a product that I recommend for those who want to scale and have a good budget. It is good for automating a data pipeline and managing workloads. My advice for anybody who is starting to use it is to take the proper training.

Overall, based on my uses, I think that this product is pretty good.

I would rate this solution an eight out of ten.

We work with clients in the insurance space mostly. Insurance companies need to process claims. Their claim systems run under Databricks, where we do multiple transformations of the data. 

The elasticity of the solution is excellent.

The storage, etc., can be scaled up quite easily when we need it to.

It's easy to increase performance as required.

The solution runs on Spark very well.

Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively.

They're currently coming out with a new feature, which is Date Lake. It will come with a new layer of data compliance.

We've been using the solution for two years.

I don't see any issues with stability going down to the cluster. It would certainly be fine if it's maintained. It's highly available even if things are dropped. It will still be up and running. I would describe it as very reliable. We don't have issues with crashing. There aren't bugs and glitches that affect the way it works.

The system is extremely scalable. It's one of its greatest features and a big selling point. If a company needs to scale or expand, they can do so very easily.

We require daily usage from the solution even though we don't directly work with Databricks on a day to day basis. Due to the fact that we schedule everything we need and it will trigger work that needs to be done, it's used often. Do you need to log into the database console every day? No. You just need to configure it one time and that's it. Then it will deliver everything needed in the time required.

We use Microsoft support, so we are enterprise customers for them. We raise a service request for Databricks, however, we use Microsoft. Overall, we've been satisfied with the support we've been given. They're responsive to our needs.

We work with multiple clients and this solution is just one of the examples of products we work with. We use several others as well, depending on the client.

It's all wrappers between the same underlying systems. For example, Spark. It's all open-source. We've worked with them as well as the wrappers around it, whether the company was labeled Databrary, IBM insights, Cloudera, etc. These wrappers are all on the same open-source system.

If we with Azure data, we take over Databricks. Otherwise, we have to create a VM separately. Those things are not needed because Azure is already providing those things for us.

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 deployed the solution myself. I didn't require any assistance, so I didn't enlist any resellers or consultants to help with the process.

The solution is expensive. It's not like a lot of competitors, which are open-source.

There isn't really a version, per se. 

It's a popular service. I'd recommend the solution. The solution is cloud-agnostic right now, so it really can go into any cloud. It's the users who will be leveraging installed environments that can have these services, no matter if they are using Azure or Ubiquiti, or other systems.

I don't think you can find any other tool or any other service that is faster them Databricks. I don't see that right now. It's your best option.

Overall, I'd rate the solution eight out of ten. The reason I'm not giving it full marks is that it's expensive compared to open source alternatives. Also, the configuration is difficult, so sometimes you need to spend a couple of hours to get it right.

I have recently gotten into Databricks and trained on one model. I started using Databricks because of its hardware support and all the other things that it provides, and it is easier to get into. Earlier, when I had to test some part of my code or test if it was working or not, it was not just a fair, not a full production run, but just a fair testing; I had to get a machine, raise a request, get into the whole process. With Databricks, I can just simply create one myself. I could get the resources, whatever they are required, test it out all there, and then go ahead with that, and that is why I have been using it primarily.

The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle.

I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier.

I have experience with Databricks.

I think there's a duration after which our training without any activity would expire, which I think is a fair point, and that is the only place where I think this will stop. I haven't come across a lot of problems with Databricks.

The tool is not used as frequently as PyTorch. I don't know why I am comparing Databricks to PyTorch, but I think around five people use it.

I have not contacted the solution's technical support team.

Before Databricks, I used to use a cloud support platform.

The solution is deployed on the cloud.

I chose Databricks over other products, considering the hardware support it offers.

A little bit of time will be needed to get comfortable with Databricks.

I rate the tool an eight out of ten.

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.