Databricks Reviews

Our team is currently utilizing machine learning for various applications, and a few members are also exploring Databrick's use for ML operations.

In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance.

I have been using Databricks for approximately six months

The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes.

The scalability of Databricks is good as long as you have a data lake, and it's easy to scale.

We have approximately 50 users using this solution in my company.

We have a different team who handles the support. I do not have contact with Databricks support.

I have not used a similar solution to Databricks.

I have seen an ROI using Databricks.

I rate the price of Databricks as eight out of ten.

Having a good understanding of physical security in relation to cybersecurity in an OT (Operational Technology) environment would be beneficial, and utilizing an existing data lake prior to implementing a Databricks initiative would greatly aid in its success.

I rate Databricks an eight out of ten.

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.

I am using Databricks for creating business intelligence solutions.

The most valuable feature of Databricks is the integration with Microsoft Azure.

Databricks can improve by making the documentation better.

I have been using Databricks for approximately one year.

Databricks is stable.

The scalability of Databricks is good.

We have approximately 500 users using this solution in my organization.

I have not used the support from Databricks.

We previously used Microsoft stacks. We chose Databricks because the processing power was better and it was a better fit for our use case.

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.

We did the implementation of Databricks in-house. The solution requires ongoing maintenance.

I would recommend this solution to others.

My advice to others is for them to first do a small proof of concept and then see how it works out and then take it from there.

I rate Databricks an eight out of ten.

This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities. All asset complaints properties are available and this is very useful to ensure the quality of all data.

The connectivity with various BI tools could be improved, specifically the performance and real time integration. There is also some improvement required in the semantic layers to manage the data match as well as the data warehouse features.

In a future release, we would like to have features to better manage all ML development activities.

I have been using this solution for three years. 

This is a stable solution, especially compared to other technology on the market.

It is a scalable solution but this depends on the platform that is being used. If you use a cloud platform such as Azure, it offers scalability. However, some platforms will not support scalability using Databricks.

We have around 20 users in our development team using Databricks. 

The customer service and support for this solution is good. 

Positive

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

I would rate the pricing for this solution a four out of five. This does depend on the environment or the infrastructure that one is using. There is a difference in pricing between using Azure or being on-premises. 

Azure Synapse is a competitor that we evaluated but it is not mature enough to provide better performance than Databricks. We choose Databricks due to the ability to have a lot of data in Data Lakes and the Data Warehouse. We are also able to run data science activities using ML flow.

If you are looking for custom model development and a lot of data management in a cloud agnostic manner, then Databricks is a good solution.

I would rate this solution an eight out of ten. 

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.

I use Databricks to explore new features and provide the industry visibility and scalability of Databricks to the companies that I work with.

I create proof of concepts for companies. As a consultant, I also create training courses on Databricks. If a company wants to leverage a service provided by Databricks and needs to train people, they use our courses.

Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance.

Databricks has made great strides in terms of performance. 

It is very user friendly. I like the ease of creating a Spark cluster, submitting a job, or creating a notebook.

The UI has also changed for the better compared to what it was two years ago.

If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks.

It's a big ask to have people jump through a lot of hoops to get approval to create a Databricks cluster just to explore it, but if they can try it on their own with a free trial without an underlying cloud account it would be more convenient.

Documentation can be improved as well. There are so many versions of documents. For example, when I tried to create a DBU vault and secrets file, I had to go through multiple versions of documents. This could be improved so that the documentation is easy to use.

I've been using this solution for about two years.

Stability wise, it's quite okay. In my experience, it doesn't crash.

I have not used autoscaling because it consumes a lot of money and because my experience has been alright. In some cases, though, it is tied to the quota of the underlying infrastructure. I have not tested the scalability to its fullest extent, but with the workloads I run, it has been fine.

When I wanted to create an AWS account and contacted technical support via email, I never received a response. Recently, however, I think they have improved their support a little bit, and I did get a call in response to my question. Overall, I've not faced any issues with the person I had to contact directly.

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

Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price.

I would rate Databricks at seven on a scale from one to ten. If you compare it to Snowflake, for example, Snowflake doesn't mandate an underlying cloud account. It creates one on its own. That's a subtle convenience that Snowflake has and one that Databricks could also build.

Snowflake's documentation is easy to use in comparison to that of Databricks. 

We use Databricks for video streaming and security purposes.

Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions.

I would like it if Databricks made it easier to set up a project. The use case determines which services we are going to use. You have the application engine, and you generate a potential budget for your workloads, so you can understand what you are going to do, what you are going to use, and what you will invest in.

Because I'm deploying on the Google Cloud Platform, measuring the investment, value, and use case is extremely difficult. So I leave it and move on without the risk. It would be easier if I had one page where you can see three columns: one for the use cases of a specific architecture, a second one for the prices based on the volume of data or machine time, and the third column for the budget. That would make it easier to know if I am using the appropriate architecture for the right solution.

I have seen something like that in Microsoft Azure, but obviously Microsoft Azure costs a lot of money. Amazon has something like that, but it's very complicated to use.

We've been using Databricks for about five years.

Databricks is very stable and powerful.

It was simple to make Databricks scalable. We found that we could set up an alert to tell us if we needed more resources, money, or time from our team. We're alerted when the system detects some trigger for any use of the instance. If you have another alert from your side, that would be extremely useful because it takes a lot of time to develop that kind of trigger. 

Databricks technical support was lovely. We don't need it so much, but the few questions we had were answered immediately.

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.

I rate Databricks eight out of 10. I like to move my customers into Databricks, but I take care of the internal system infrastructure so they can continue to use familiar software or operating systems and databases. They have a lot of doubts because they don't know the solution. We need to train them, explain things, and show the solution's potential value. 

Generally, companies try to keep the same flavor when they migrate. For example, if they are using many Microsoft products, they want to work with Azure. If they are open to other options, they go with GCP or AWS. However, Databricks doesn't have enough customers here in my market because it's not a visible brand. Azure, GCP, and AWS are highly visible here, so the local teams are friendly with the three brands.

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.