Databricks Reviews

Our primary use case is for data analytics. Essentially, we use it for the financial reporting for Adobe.

The way Databricks has improved my organization is definitely through giving us improved performance and the processing power. We are usually never able to achieve it using traditional data warehouses. When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks.

The features I found most helpful with Databricks are the Lakehouse and SQL environments.

I believe that this product could be improved by becoming more user-friendly. 

In the next release, I would like to see more flexibility in the dashboard. It has plenty of features but it can be enhanced so that it matches with other visualization tools, like Power BI and Tableau. Also, the integrations with other tools could be better.

I have been using Databricks for the last three years.

I would rate the stability of Databricks an eight, on a scale from one to 10, with one being the worst and 10 being the best.

I would rate the scalability of this solution a nine, on a scale from one to 10, with one being the worst and 10 being the best. I would say there are around 2,000 to 3,000 users of this solution in our organization.

I've been in contact with the Databricks support team and received timely support from them. I would rate their support an eight, on a scale from one to 10, with one being the worst and 10 being the best.

Positive

Prior to Databricks, we initially used Hadoop. Afterwards, we used HANA, SAP HANA, and the Microsoft SQL Server.

The initial setup was relatively straightforward. I would rate it nine, on a scale from one to 10, with one being the easiest and 10 being the hardest.

There is no need to worry about the deployment as it can be done quickly. It is relatively automated. We used Terraform for auto-deployment, which happens in Azure. With Terraform, there are two options. As option one, you can deploy manually by creating services. For option two, you use Terraform and automate. Terraform is like infrastructure as a code where you can code the deployment part using it.

There were two or three persons involved in the deployment of this solution.

The new version of the Databricks solution requires code maintenance. This is done by the platform team.

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. 

I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast.

I have been using Databricks for three years.

I would rate the stability of this solution a nine out of 10, with one being not stable and 10 being very stable.

I would rate the scalability of this solution an eight out of 10, with one being not scalable and 10 being very scalable.

There are three people using this solution in our organization.

I would rate the available customer service a three. It's worth mentioning that this is Microsoft and not Databricks itself. I haven't spoken to Databricks people directly, but I know the people who have and they have been a lot more pleased.

Negative

I would rate their pricing plan a six (on a scale of one to 10, with one being cheap and 10 being expensive). I think the prices could be lowered a little bit.

Overall, I would rate this solution an eight out of 10, with one being quite poor and 10 being excellent. It is fast, it's scalable, and it does the job it needs to do.

We use Databricks for batch data processing and stream data processing.

Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user.

The flexibility of Databricks is the most valuable feature. It gives us the ability to write analytics code in multiple languages.

There is a single workspace for different data roles like data engineers, machine learning engineers, and the end user, who can connect to the same system. 

Databricks computes separate from storage, so you are not coupled with the underlying data sets, allowing for multiple processes and multiple programs to be written on the same code.

I would like to see improvement with the UI. It is functional and useful, but it's a bit clunky at times. It should be more user-friendly.

In future releases, Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics.

I have been using Databricks for eight months.

Databricks is very stable.

The scalability of this solution is good. In our organization, users include analysts, data engineers, and data scientists.

I would give Databrick service and support a four and a half out of five overall.

Positive

Prior to using Databricks, we used Azure Stream Analytics. We made the switch because of the scalability and integrated platform.

The initial setup of Databricks is more complex. I would rate it a four out of five on the complexity of the setup. It took two days to deploy the solution.

We used a third party for some of the implementations of Databricks. The number of staff required to deploy and maintain this solution depends on the number of processes you have. Due to the cloud nature of the technology, it is easy to deploy and maintain. 

The licensing of Databricks is a tiered licensing regime, so it is flexible. I feel their pricing is a five out of five.

Databricks is a one-stop shop for everything data related, and it can scale with you.

I would rate this solution a 9.5 out of 10 overall.

We use Databricks to define tool data and have many use cases to analyze and distribute the data.

Data is open to everyone; they can access it through many channels, including notebooks or SQL. That on its own democratizes the data.

I like cloud scalability and data access for any type of user.

It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow.

In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them.

I have been using Databricks for roughly one and a half years.

Stability is excellent.

Databricks is scalable. You can use the power of the cloud to scale your cluster size, either CPU or memory. The data doesn't work like a standard database, so you don't have it based on files, and you don't copy the data. It's super scalable. It's only the computing that you have to scale with the data.

We probably have 40 users with roles like developers, business analysts, and data scientists. We have big plans to increase the usage and have more departments using it.

Technical support has helped us.

On a scale from one to ten, I would give technical support a five.

Positive

We used Cloudera before switching to Databricks.

The initial setup was fairly okay. It takes about two minutes to deploy this solution. It's all code, so we click a button, and then it's done.

On a scale from one to five, I would give the initial setup a four.

We set up and deployed this solution.

On a scale from one to five, I would give our ROI a three.

We only pay for the Azure compute behind the solution. If you want to compute, you have to have a database layer and Azure below.

On a scale from one to five, I would give their pricing a two.

We looked at other options such as Snowflake and Cloudera on the cloud,

I would tell potential users that they need proper cloud engineers and a 
cloud infrastructure team to use this solution.

On a scale from one to ten, I would give Databricks a nine.

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 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.

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.