Databricks Reviews

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 Databricks for data science work in projects that create data pipelines, pre-processing, data wrangling, big data cluster management and ML, machine learning and deep learning tasks.

Databricks collaborates very well with the Azure platform, Dataiku, and enterprise AI tool. Databricks is a new connection to pull the data or connect to the Spark cluster. It is helpful for us to instance it or distribute the load through the Spark cluster, and it is very user-friendly.

The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark.

Databricks as a solution is integrated with Azure, but Google Cloud has some restrictions. I'm not sure about AWS Cloud, but it would be great if Databricks could integrate all the cloud platforms. Regarding additional features, we would like to see them mostly on the data engineering side, where we have a Spark cluster and some inbuilt ML. In addition, pre-processing steps will be useful.

We have been using this solution for two years and are using the latest update.

It is a stable solution as long as the Microsoft Azure Platform is stable too.

It is a scalable solution, both vertically and horizontally, which is good. My organization is big, and we have a lot of users. In my department, we have about 15 people using Databricks.

We have not escalated any issues to technical support, but we initially struggled with configuration and the settings of Hive metastore, but we resolved it. I rate the technical support a nine out of ten.

Positive

We were using the looped EMR elastic MapReduce from AWS before using Databricks. We switched to Databricks because the whole platform changed from AWS to Azure platform, and Databricks comes as a package.

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.

We have seen an ROI but cannot differentiate because it also comes with the Azure platform.

I do not have details about the pricing.

I rate this solution a nine out of ten. Regarding advice, Databricks is a very good platform, popular and easy to use daily for data engineers and data scientists who rely on a large dataset to do advanced analytics reporting. It's a very good tool.

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 am using Databricks in my company.

The most valuable feature of Databricks is the notebook, data factory, and ease of use.

I have been using Databricks for approximately nine months.

The performance and stability of Databricks are good. It is quick and I have not had problems.

Databricks is highly scalable.

We have 200 people using the solution in my organization.

When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand.

I have not worked with another solution prior to Databricks.

The price of Databricks is reasonable compared to other solutions.

I rate Databricks an eight out of ten.

Our primary use case of this product is for our customers who are running large systems and looking for an API -- a quick, easy integration with their own system. We use Databricks to create a secure API interface. I'm vice president of data science and we are customers of Databricks. 

Databricks is quite easy to use and requires less coding and customizations than a solution like AWS SageMaker which I'd previously used on a lot of projects. Databricks enables more people to efficiently build and host their ML code. Another great aspect is that MLflow is already integrated with Databricks which makes a big difference. It enables us to track and monitor all our different experiments. We have mostly used the MLflow part and generic notebooks with the ML building machine learning model, as well as using Pytorch for some of our medical imaging. We were able to quickly deploy both these features without requiring anything extra. 

I'm struggling a little because I wanted to do some POC solutions. I present a lot of projects in various forums and seminars and there aren't a lot of credits and trial options with Databricks. Even if we want to explore, we're not able to and that's a challenge. The solution is quite expensive.

I've been using this solution for a year. 

It's currently stable although we have not yet tested it with a huge volume of data. We'll focus on the performance and model serving capability in the near future. We're still carrying out performance testing, developing the models and figuring out the infrastructure.

Scalability is quite good because we just used 128 GB of resources. It's quite easy to scale.

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

We did a PSU in Azure ML Studio which is quite a good solution, easy to deploy and use. It's almost a no-code platform. We've also found Azure ML Studio to be quite cost-effective.

I would recommend trying Databricks because it's cloud agnostic. A lot of customers currently use Azure but want to build something on their own down the track. Databricks makes that easy with its integration with other cloud customers. If somebody wants to build something on their infrastructure or their own virtual cloud, this is a good platform.

I rate the solution eight out of 10 because of the issue I'm having with a lack of trial options.

We are using Databricks to receive the data from Data Lake where we are processing it and doing the transformation, and cleansing. Once it is processed, we are sending the data to the Azure SQL database.

Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily.

The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well.

I have been using Databricks for approximately three years.

Databricks is stable.

The salability of Databricks is good. However, if I want to use the higher clusters and high concurrency clusters, you will need to wait more time to spin up the clusters. 

We have different teams. Among them, I'm part of the data analytics where, our team, almost 10 people are using it. But I'm not sure about the rest of the teams.

We are using Databricks extensively. We have a team of 10 using the solution.

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 billing of Databricks can be difficult and should improve.

We have evaluated Azure Synapse and SQL. Both Databricks and Azure Synapse are similar, the UI is the only difference. SQL and Databricks are the same, and one of the largest setbacks is the processing of a lot of data takes a long time.

I rate Databricks a seven out of ten.

We primarily use Databricks for reporting and machine learning.

Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform.

I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one.

Also, this is an all-in-one platform, but it might be preferable if there were an a la carte model where we could select the best tool in each class for reporting, machine learning, etc. I'm not yet sure if this strategy is the best one. 

We've been using Databricks since the start of the year.

Databricks is quite stable. We haven't had any issues with stability. It's always working perfectly with no downtime.

Databricks is based on Spark, which is based on Scala. These languages aren't easy to handle, and it's challenging to find people who know them well. At the same time, a couple of other vendors that work on top of Databricks are low-code platforms. We have to work around Databrick's lack of scalability by using low-code platforms that work on top of Databricks to give us scalability.

I'll give Databricks support 10 out of 10. They are always prompt even though we didn't buy a support package. They have done an excellent job.

Positive

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. 

It's too soon to tell what kind of return we're getting because we just started using it, and we're still migrating.

The cost of Databricks is in the lower range compared to other solutions. That was one of the main reasons we chose Databricks over other vendors and platforms.  

We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations. 

I give Databricks nine out of 10. The solution has met all our expectations. I'd recommend it to a friend. It's a reasonably priced all-in-one solution that gives us data lake and lakehouse capabilities. Those were the primary reasons we chose Databricks.

I believe we are using the new version.

Our company makes comprehensive use of the solution to consolidate data and do a certain amount of reporting and analytics. All the data consumers use Databricks to develop the information.

Data governance should be addressed. We have some trouble connecting all the governance solutions with Databricks. This means the integrative capabilities are problematic. 

The initial setup is difficult. 

We have been using Databricks for a year-and-a-half.

The solution is stable. 

The solution is scalable. 

The technical support is good. 

As we are talking about a corporate solution, the deployment of Databricks lasted longer than the one day it took for Alteryx. 

We used Alteryx prior to Databricks and continue to do so, it being the only other solution we have employed. We use the two with different software. 

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. 

I believe we used a partner for the deployment, although I cannot say for certain, as this is not within my purview. 

I don't know how many people are needed for maintenance and deployment. 

As the licensing is not within my purview, I am not in a position to comment on this. 

My company makes use of the solution. It is employed by my data team and the technology one. I do not have personal experience using the solution. 

The solution is deployed on base, on data. 

I am not aware of how many people make use of it. 

I rate Databricks as a seven out of ten.