Databricks Reviews

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. 

We have a team that works on Databricks for our clients. We are customers of Databricks. 

Databricks can cut across the entire ecosystem of open source technology which gives an extra level in terms of getting the transformatory process of the data. The solution is primarily open source and they have bolstered its components to make it more fit for purpose for a complete Azure Data platform. The solution is responsible for the core transformatory activities. While Azure Data Factory is very good for pulling in the data, doing the basic standardization and profiling, Databricks is more about making fundamental changes in structure or in size of the data and aligning it for subsequent consumption, or for the final layer on Synapse. It also has the power to complement and work with Spark and elements related to Python. 

In my view, the fundamental approach of implementing Databricks is still very code heavy, more than you find in Azure Data Factory and other technologies like Informatica or SQL Server Integration Service. From my perspective, that could be improved. I'd also like to have the ability to facilitate predictive analytics within the solution. 

I've been using the solution for a year and a half. 

Stability of the product is good, whether it's handling large volumes, diverse elements of data or processing data at speed. It has stood the test of time. It's a solution that really lends itself to that higher level of stability, versatility and diversity in terms of its capability to process different forms of data.

The cost of the solution is slightly on the high side so it's important to use it efficiently.

Use the solution wisely and in tandem with Azure Data Factory. Apply the prism in your overall design of the pipelines of the flow, to utilize to its potential. Databricks offers significant capability to the transformatory and data tranching capabilities in terms of diverse variety to Azure Data Stack per se. In terms of the license, ensure that the customer is getting what they paid for so that the value for money is realized. 

I rate the solution eight out of 10. 

I am a developer and I do a lot of consulting using Databricks.

We have been primarily using this solution for ETL purposes. We also do some migration of on-premises data to the cloud.

The most valuable feature is the ability to switch loads between multiple clusters.

Automation with Databricks is very easy when using the API.

The ability to write code and SQL in the same interface is useful.

It is easy to connect notebooks to a cluster.

There are a large number of inbuilt functions that help to make things easier.

Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems. As it is now, we have to go into the driver logs to identify the error messages properly. 

There is not much information about Databricks available online, such as cost. Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful.

I would like to see integration with Power BI or Tableau for the business users. They may use Databricks to check on things, but it will be a little bit complicated for them. The GUI interfaces for Tableau and Power BI are ones that they are used to, so the integration would help.

I have been using Databricks for about five and a half years.

We have found that in the development environment, Databricks is pretty stable. We have had problems where something works in development but does not work in production, and this can happen when the version is updated and certain features have been deprecated. This means that more testing is required before moving to production, but this is the only drawback that we have seen.

Basically, when we move across version we have found issues, but otherwise, it's pretty stable.

Scalability is one of the main features of Databricks. We have used datasets that are one hundred megabytes in size up to one terabyte, and we can manage, so it's easily scalable.

We have a large company with between 400 and 500 people using this solution.

We have not reached out for technical support on Databricks.

I found the initial setup easy because I had previously worked on Spark.

If somebody goes through the training, which is available on the website, then it should be straightforward. I don't think that it is very hard.

When it comes to developing things based on use cases, it can take between three days and two weeks, plus two to three days for testing and deploying it. I would say that for an entire use case, it will take a maximum of three weeks.

My advice for developers who are interested in working with this solution is to first go through the Spark architecture.

I would rate this solution a nine out of ten.

Our primary use case of Databricks is for advanced analytics. I'm the chief research officer of the company and we're customers of Databricks.  

I think the features I like the most are the scalability of the solution as well as its ability to share. We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.

I'd like to see more licensing options for the solution, the availability of additional pricing tiers. I understand it's not easy to achieve because it's a kind of platform-as-a-service type of solution. If you wanted to be more specific about the parts, and what you might or might not need, then you could save some money, and go for a lower level. Of course, that would then mean you'd have to manage more configurations which, as a user, would make things more complex but it would be good to have that option. The pricing is not the cheapest but it's understandable because it's a very high-end solution and easy to use, there's a lot of complexity masked away.

I would like to see additional monitoring tools and, in general, anything that can improve visualization of data. I know it's not the main point of Databricks and there are other tools that can be used, but anything that facilitates the integration of Databricks with visualization tools could be really useful. Increasing data scalability would also be great. 

I've been using this solution for a year. 

The solution has been very stable. 

Scalability of the solution seems very easy to achieve. 

We haven't had contact with technical support. 

The initial set was very straightforward because it's also in our Azure cloud so it was quite easy to set up and configure. Very intuitive.

I would rate this solution an eight out of 10. 

We use the solution for business engineering.

The solution is very simple and stable.

The tool should improve its integration with other products.

I have been using the solution for around two years.

I would rate the product’s stability a seven out of ten.

I would rate the tool’s scalability a seven out of ten.

The solution is very easy to setup. I would rate its setup a ten out of ten.

I would rate the tool’s pricing an eight out of ten.

The tool’s performance is great. I would rate it an eight out of ten.

I use this for database machine learning, to construct different models for supermarkets, drug store management, and market involvement to identify business opportunities for clients.

We provide different statistical models and use different algorithms depending on the client.

I was a Lead Data Scientist in different companies. I implement data and build and optimize processes using machine learning techniques, aided by science and advanced analytics.

Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client.

There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable.

It's stable.

It's scalable.

I would rate technical support 4 out of 5.

Setup isn't difficult. We used about 15 people for deployment and maintenance. We have data scientists and statisticians using this solution and doing different analyses.

I would rate this solution 9 out of 10.

My advice is to use the different high analytics methodology, plan for the project, and recognize the different activities for the design.

We use this solution for streaming analytics. We use machine learning functions that output to the API and work directly with the database.

Prior to using Azure Databricks in the cloud, we had Databricks installed in clusters. Since our implementation, the performance has increased and our cost has been reduced.

Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great.

This solution has very good machine learning libraries built-in.

The support for big data is good.

Databricks should have more libraries for predictive analysis and machine learning.

It should have more compatible and more advanced visualization and machine learning libraries. As it is now, I have to try a customer algorithm in order for things to be compatible.

I would like to see more deep learning analytics.

I have been using Databricks for about one year.

This is a cluster-based solution, so it is stable.

We started using Databricks with a small PoC application, and then we developed it into a larger one. It's scalable, and it's a simple process to scale.

We have eight people in our team who are using this solution. We do not plan to increase usage at this time.

I did not contact technical support myself, but when one of our team members contacted them they were given good answers. I would say that the support is good.

It is not difficult to deploy this solution because it is well documented. We followed the normal steps that included all of the APIs.

I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly.

Databricks has been good and I like it. However, it would be improved with the enhancement of the machine learning libraries, and with the inclusion of visualization libraries.

I would rate this solution an eight out of ten.

We specialize in project consulting for our clients. Whenever we get the opportunity, we recommend Databricks to them.

The fast data loading process and data storage capabilities are great.

Based on the data loads and the performance, you can easily scale up the clusters.

Sometimes we experience issues connecting our database to Databricks. There are no direct connectors — they are very limited. This should be addressed and corrected in the next release.  

Reading past data can also be tricky as there is no data spectrum like you would find with Snowflake and other solutions. 

We have been using Databricks for one and a half years.

Both the scalability and the stability of Databricks is good.

Technical support is good but I have not interacted with them directly. We have a point of contact. We used to interact with tech support on a regular basis and they would respond quickly. We would get a response on the same day based on the priority level. Keep in mind, my company is in a partnership with them which could be a factor in their quick response time.

The initial setup was not very complex. We had it up and running in no time; it's a quick process.

We have just one solution architect and one data architect who handle all maintenance-related issues. 

I would recommend purchasing a package that includes technical support. Compared to other companies, they offer great support to their clients.

On a scale from one to ten, I would give Databricks a rating of eight.