Databricks Reviews

Our use case is confidential, but I can say we use it for a deep learning model for machine learning. 

The solution is built from Spark and has integration with MLflow, which is important for our use case. 

Databricks is also user-friendly, providing customizable codes and models that allow people to experiment quickly. 

Integration of Delta Lake is another useful feature.

Writing pandas-profiling reports could be easier. 

The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps. 

I have been using this product for one and a half years. 

For now the solution seems stable. 

The solution is easy to scale horizontally and it has a useful auto-scaling feature. For vertical scaling, you need to bring the system down and make some adjustments.

On my current project I have a team of 30 members under me, including data engineers and data science people. Our data science, engineering, and MLOps projects are expanding, so we are planning to do some vertical scaling to increase the team size to over 100 members. In our company, we are trying to certify more and more people in Databricks because it's cloud-agnostic. 

We have never needed to contact customer support, online resources have been sufficient to solve our problems. 

The initial setup of the solution is straightforward, once you understand the UI it is easy to implement. I would rate Databricks a four out of five for ease of setup.

One migration project took two to three months, including writing all the code and implementing end-to-end pipelines. 

We are planning to deploy the solution in stages over the next 15 months to completely implement MLOps for our organization.

I'm not involved in the financing, but I can say that the solution seemed reasonably priced compared to the competitors. Similar products are usually in the same price range. With five being affordable and one being expensive, I would rate Databricks a four out of five. 

I find that deployed systems work out cheaper than having to operate manually, which appeals to our customers. 

I would rate this solution an eight out of ten. 

There is an issue where clusters are automatically deleted after termination or after 100 days of non-usage. This could be more user-friendly, and they could include an enabler to pin the clusters you want to keep, instead of having to go and research why clusters got deleted after implementing the product. That documentation needs to be right in front of the user to avoid issues.

I definitely recommend this product to other users. 

I use Databricks to manage the setting up of data lakes for SaaS.

The biggest problem associated with the product is that it is quite pricey. We cannot find a better solution than Databricks in the market currently.

I have been using Databricks for a year.

It is an expensive tool. The licensing model is a pay-as-you-go one.

The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale.

For my general use cases, I would say that I am not a technical person, so I cannot explain to you how the tool helps with the area of data engineering tasks.

There is another team in my company that is involved in the use of machine learning and AI features in Databricks. My team is mostly into operations. The tool is used in a multi-country project.

For example, in my company, they make some shopping decisions related to solutions based on what is the product chosen by the whole company.

I rate the tool an eight out of ten.

The product has helped in data fabrication. 

Databricks has helped us have a good presence in data. 

The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice. 

I have been using the product for more than six months. 

I rate Databricks' an eight out of ten. 

I rate the tool's scalability an eight out of ten. 

The transition to Databricks was smooth. 

Databricks' price is high. 

I rate the solution a nine out of ten. 

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.

We use this solution for advanced civilization power.

We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time.

This product allows us to write the email models in a way that allows us to take the advantage of the parallel scaling computer window backend on any of the satellite services.

This solution only supports queries in SQL and Python, which is a bit limiting. 

This is a fairly expensive solution for any service outside of the basic package, and costs can add up quite quickly if there are large scaling requirements.

This is a stable solution in our experience.

We have found that part of the beauty of this platform is that it is easy to scale and expand.

The support for this product uses Microsoft as a middle man, and due to this there have been times when we experienced communication delays, as well as misunderstandings of what our issues are.

Neutral

The initial setup for this solution is very simple.

The basic version of this solution is now open-source, so there are no license costs involved. However, there is a charge for any advanced functionality and this can be quite expensive.

We looked at both Snowflake and BigQuery as a comparison with this solution. We choose this product as it offered more scalability and a higher level of security, which is extremely important in our banking environment.

We would rate this solution an eight out of ten.

We use this solution for the Customer Data Platform(CDP). My company works in the MarTech space and usually we implement custom CDP.

The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks. It is the most important element of the solution. Databricks also offers exceptional performance and scalability. 

The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau.

In a future release, we would like to have a better ETL designer tool to assist in the way we move data from one place to another.

We have been using this solution for four years. 

This is a stable solution. 

This is a scalable solution. 

The initial setup is very easy. It is a managed solution inside Azure so you just need to search for Databricks. There are a couple of pages to follow in the setup wizard and Databricks is up and running.

We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data. 

When we first started using Databricks in 2018, there were not many comarable solutions to consider. Right now there are many solutions to consider including Snowflake, Azure Synapse, Redshift and BigQuery.

Databricks continues to be our solution of choice but Snowflake does have a better user interface and is easier to work with the data pipelines and with the overall UI.

I would advise others to first define a strong data strategy and then choose which data platform suits your needs. 

I would rate this solution a nine out of ten. 

Our primary use case for this solution is research for data scientists. The solution is deployed on cloud.

It has allowed our data engineers, data scientists, and analysts to collaborate and work on the same platform. 

We have the ability to scale, collaborate and do machine learning.

The product cannot be integrated with a popular coding IDE.

We have been using this solution for approximately three years.

The solution is stable.

The solution is scalable. There are five people using it in our organization.

I rate my experience with customer service and support an eight out of ten.

We previously used H2O.

The initial setup was straightforward.

Implementation was done in-house.

We have seen a return on investments.

Licensing costs are charged on a yearly basis and costs between 25,000 and 30,000.

We evaluated other options but this solution was the best fit for what we required.

I rate this solution nine out of ten. The solution is good but can be improved by integrating with a popular coding IDE.

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.