Databricks Reviews

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.

Databricks can be used for large-scale data pre-processing and data transformations.

The solution is easy to use and has a quick start-up time due to being on the cloud.

The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration. There is a lot of code from places, such as GitHub, but it is not tailored for Databricks. It requires a lot of effort to bring the code to a level where it can be used with Databricks capabilities.

I have been using Databricks for two months.

The solution is stable.

Databricks is scalable.

We did not have a need to use technical support.

The installation is straightforward, and it took approximately one hour.

We did the implementation and maintenance of the solution ourselves using approximately three engineers.

The solution requires a subscription.

We are evaluating other solutions.

I would recommend this solution for those wanting to process large data sets, but if it is to be used for smaller data sets, I would not recommend it.

I rate Databricks a five out of ten.

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.

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.

Our primary use case for the solution is to run batch jobs.

The ease of use and its accessibility are valuable.

The solution can be improved by expanding its integration capabilities and providing the ability to query external vendors directly.

We have been using the solution for a little less than a year, and we deploy it on the Amazon cloud.

The solution is stable.

The solution is scalable, and there are approximately seven developers and two DevOps employees utilizing the solution.

We have had a good experience with customer service and support. I rate them a nine out of ten.

Positive

The initial setup for the solution is a bit complex.

I wouldn't consider it a costly solution. Like all other solutions, it depends on how you use them. If you provision sparked clusters much larger than what you need, it becomes costly. For example, it is not more costly than EMR, the AWS equivalent, and from my perspective, it is much better.

I rate the solution a nine out of ten. The solution is good, but the integration and query capabilities can be improved.

I primarily use Databricks for data pipelines.

Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good.

I've been working with Databricks for around five years.

Databricks is stable.

Databricks is scalable.

Databricks' technical support takes a while to respond and could be improved.

The initial setup was easy.

Databricks' cost could be improved.

I would give Databricks a rating of eight out of ten.

We are a consulting house and we employ solutions based on our customers' needs. We don't generally use products internally.

I am a certified data engineer from Microsoft and have worked on the Azure platform, which is why I have experience with Databricks. Now that Microsoft has launched Synapse, I think that there will be more use cases.

You can spin up an Azure Databricks clustered, and integrating with it is seamless.

The integration with Python and the notebooks really helps.

There is definitely room for improvement.

This is the type of solution where you need to have people with technical expertise to use it.  Other products are self-service and can be employed by end-users. Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists. I'm not sure whether Databricks is working towards it, or not.

It would be nice if it were more user-friendly, where you don't have to rely on Power BI or a visualization tool. I know that there is integration in the notebook where you can do it, but still, the relationships and semantics make it more difficult. It would be better to do it right in Databricks. You could put them within the portal and I don't have to log out and bring that into Power BI and then visualize.

We have not done any major implementation yet, although I think it's stable to an extent. I can't comment on it in terms of benchmark and experiencing any issues. It works seamlessly in the places where I've used it.

Our implementations have been small and we haven't needed to scale as of yet. 

Databricks can help you to build a data lake, and it's something that they need to help make more popular. People are slowly understanding it because if you look, there are lots of data lakes that people are trying to create. I'm not intimate with it, but the concept seems complicated. I think they need to write up something where videos can explain it better. What I have seen on YouTube is quite complicated for an end-user to understand.

The initial setup is easy. It's not difficult when you are used to Azure.

I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself. 

The cost is difficult to estimate. I've got customers who went to the cloud and then they realized that the costs were more, compared to what they used to be on-premises. Also, because our exchange rate is so weak, I would always advocate that prices being lower is better, although I don't know how feasible it is.

From a purely technical perspective, I would rate Databricks and eight out of ten. However, there is a failure in terms of user adoption. After I look at other products, including Synapse, those are better. I still feel that Databricks is quite complicated for the average person.

I would rate this solution a five out of ten.

We use the solution for multiple items. We use lots of data crunching, development, and algorithms on it.

The time travel feature is the solution's most valuable aspect.

The management of the solution needs to be modernized. Managing the radius data is hard.

The solution requires modern scoring. There's not a good way of knowing how the models are performing from a data science perspective. The solution needs more model scoring abilities. It doesn't necessarily need more model monitoring, but more model scoring and performance from a data science perspective. 

Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with.

I've been using the solution for one year so far.

The solution is not exactly stable. We've faced a few bugs which have really affected it. There are bugs especially when it comes to connecting with Spark.

It's hard to say how scalable the solution is. The scalability comes into play on the Spark side, not on the Databricks side.

We have about 20 people on the solution right now.

We've never been in touch with technical support, so I don't have any experience in terms of dealing with them.

The initial setup is straightforward. I wouldn't say that it's complex in any way.

Deployment times vary and really depend on multiple factors. It can take anywhere from a few weeks to a few months to deploy the solution. In our case, it took us about three months to fully deploy it.

It takes two to three people to deploy the solution.

I deployed the solution with the help of my team.

I'm not sure what the licensing costs are on the solution.

We did evaluate Amazon PageMaker before ultimately choosing Databricks. It's the only other solution we evaluated at the time.

We're partners with Databricks.

We're using the latest version of the solution, but I can't recall what version number we are on.

I'd advise others considering the solution to look at usage. They shouldn't adopt the solution blindly. How the implementation and usage will go will depend on the skill of the data engineer and what your requirements are.

I'd rate the solution seven out of ten.