Databricks Reviews

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.

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 the solution for data engineering. 

The tool helps us manage large amounts of data. 

The simplicity of development is the most valuable feature. 

Databricks has a lack of debuggers, and it would be good to see more components. 

Another issue is that the D4 data format keeps changing on our cluster. This doesn't affect me much because I use functions to define it, but it is very frustrating for some more casual users. One day the output will be in a particular format, and then it becomes an object without us changing the cluster configuration. As a small team, we don't have the capacity to dig deeply into the issue, which has been frustrating.

We have been using the solution for three years. 

The solution's stability is good. 

The product is scalable. We're a small organization with 12 users, and we don't currently have any plans to increase our usage.

We see an ROI from Databricks. 

I would rate the solution seven out of ten. 

It's a good solution and more for handling large amounts of data. Databricks is better as a batch processing system than as an interactive system. The performance is a little disappointing because the memory processing is supposed to be excellent, but it's not as competitive as some other solutions out there in this regard. Even classical databases can respond and process faster.

Our primary use case is ETL.

Using Databricks enables us to use the Data Mesh methodology, where every team performs their own ETL.

The most valuable feature is the versatility of the ecosystem. You can write code in SQL, Python, or Java.

The load distribution capabilities are good, and you can perform data processing tasks very quickly.

You can save and share notebooks between different teams.

The interface is easy to use.

The cost of this solution is high, on the expensive side.

In the future, I would like to see Data Lake support. That is something that I'm looking forward to.

I worked with Databricks for approximately two years in my previous company.

This is a very scalable solution. We have twenty-five data engineers that use it, and we may grow our usage.

The technical support is okay. I would rate them a seven out of ten.

Neutral

We did not use another similar solution prior to Databricks.

The cloud-based deployment is simple.

If you use an on-premises deployment then there is more to do.

We deployed it with our in-house team.

There is no maintenance required.

We have seen a return on our investment with Databricks.

Price-wise, I would rate Databricks a three out of five.

When we looked into Databricks, we evaluated Azure Data Factory and some of the others on the market. We found that Databricks was one of the easiest ones to use.

My advice for anybody that is looking into Databricks is not to use the on-premises deployment. Instead, use the cloud-based setup.

In summary, this is a good product.

I would rate this solution an eight out of ten.

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.

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. 

We use this solution for finding anomalies and applying the rules to the streaming data.

There are around 50 people using this solution in my organization, including data scientists.

The ability to stream data and the windowing feature are valuable. There are a number of targeted integration points, so that is a difference between Stream Analytics and Databricks. The integrations input or output are better in Databricks. It's accessible to use any of the Python or even Java. I can use the third party, deploy it, and use it.

Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing. There should be reliability between these two. Databricks is based on open sources. If it's more synchronous between the Microsoft technology and the programming languages, it'll be better. Python has better languages, but compatibility would be a great help.

I would like to have better support for Microsoft technology and better language components.

With Azure or Cosmo DB, I can store other data links or time series data tables. That would be a great help for analytics in real time.

I have been using Databricks for eight months.

The scalability is fine. We had thousands of devices and were sending data infrequently, so that worked for us. If the amount increases, the windowing function and job schedule may not perform as expected.

I would rate technical support 4 out of 5. We had some issues with setup, and they were finally solved but it was after following up a few times.

Azure Stream Analytics is easy to use and easy to deploy. It's a little bit better. Databricks is still having some stability issues. Azure Stream Analytics has a few input and output sources, and it's scalable to all types of third party or interfaces.

Setup was complex. There were some issues with setting up a database and installing the third party component on top of services. I would rate the setup 3 out of 5.

Implementation was done in-house.

The cost is around $600,000 for 50 users.

I would rate the price 2 out of 5.

I would rate this solution 8 out of 10.

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.