Databricks Reviews

We are still exploring the solution. We utilize it much, much better than their star schema models that they are trying to replace it with. We bring in Databricks and then see how they can leverage the additional analytical functionalities around the Databricks cloud. It's more in exploratory ways. We recommend Databricks, especially with the Azure cloud frameworks.

Pricing is one of the things that could be improved.

Also, there could be improvement in the visual analytics space there and on the machine learning functions. I haven't explored so I don't know about the functions and features that are there. If it is not there, then I think that's something which they should consider including.

My team has been exploring Databricks for close to five or six months.

I haven't heard about any major stability issues. At this time I feel like it's stable.

In terms of scalability, I think once we put it across for larger use-cases the scalability question will really arise. So we'll need detailed information. I assume that we will be able to scale up.

I think we do not have more than 10 people working on it now. Because we are in the earlier stages of implementation, it's more like a POC now. I really don't know whether it's been open for the larger audience yet.

The initial setup was straightforward.

It is better to be installed with the help of integrators, or consultants, or with an experienced team.

It's more data scientists using Databricks. I would call them power users trying to see how they can get a hand on it, though they are not data scientists. They try to understand it a little bit better for their future use.

On a scale of one to ten, I would rate it an eight, easy. 

We primarily use the solution to run current jobs; to run the spark jobs as the current job.

The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient.

The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets.

The support could be improved a bit around the database. When we stream it to Data Lake, some data cannot be loaded. It should be a priority to fix this.

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

The solution is stable.

The solution is scalable. However, it still needs us to manually set out the number of nodes in a cluster. It's really dependent on the application. Sometimes, when the tasks are bigger, and it gets a little difficult for us to define the number of nodes in a cluster. If the solution could allow users to set up the clusters, I think that'll be good.

Currently, we have three people using the solution. We may increase usage in the future.

The technical support is quite good. In the beginning, when we had a few POC projects, they were very supportive.

We didn't previously use a different solution, however, we built our own from scratch. This is the first unified platform that we've used.

The initial setup is very straightforward. We just use their job functions. To deploy as a spark job is quite straightforward. 

In our use case, we also had some external databases to handle the deployment. For example, we only generated some prediction results. We saved the results into an external database. The solution takes time to deploy to the external database, but the spark job is quite easy.

I'm a software development engineer. I'm working with the latest version.

As long as the developers have an understanding of spark, and understanding technical tricks, it's very fast in terms of using the database.

I'd rate the solution eight out of ten.

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.

Our primary use case for this solution is for data ingestion and the DQ rules we are implementing. We deploy the solution on Azure cloud.

Whenever we send data to downstream applications for creating a file, multiple business rules are involved, and this solution assists with quickly computing a considerable amount of historical data.

Its lightweight and fast processing are valuable.

The product could include some UI features to improve the ease of use, like drag and drop for a few aggregated functions. Additionally, the Databricks cluster can be improved.

We have been using Databricks for approximately two years and are currently using the latest version.

The solution is very stable. However, sometimes it intermittently restarts. I rate the stability an eight out of ten.

The solution is scalable, and we are trying to implement more use cases with Databricks in our organization as we advance. I rate the scalability an eight out of ten.

I rate customer service and support a nine out of ten.

Positive

The initial setup was not very complex. We deploy the solution manually and the time required depends on the complexity of the business logic. I rate it an eight out of ten.

We implemented the solution through an in-house team.

I rate the solution an eight out of ten.

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 like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job. So you can create a robust solution by working together with other professionals.

One area for improvement would be that anyone who doesn't know SQL may find the product difficult to work with. It would also be useful to have a remote support team inside Databricks, which would collect and analyze user feedback.

I have been using Databricks since 2018.

I had a little trouble with customer support but this was solved.

The initial setup was a little complex because it was a new architecture for the customer, so there was nothing to compare it to in order to accelerate the project. This meant the deployment of the first project using Databricks took almost nine months and the second took almost a year.

Databricks is the full data analytics platform. It involves end to end data analytics process.

Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution.

Databricks could improve in some of its functionality.

I have been using Databricks for approximately a year and a half.

Databricks is very stable.

The scalability of Databricks is good.

We have 30 to 40 people are using this solution in my company.

I rate Databricks a nine 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.