Databricks Reviews

We are using Databricks to receive the data from Data Lake where we are processing it and doing the transformation, and cleansing. Once it is processed, we are sending the data to the Azure SQL database.

Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily.

The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well.

I have been using Databricks for approximately three years.

Databricks is stable.

The salability of Databricks is good. However, if I want to use the higher clusters and high concurrency clusters, you will need to wait more time to spin up the clusters. 

We have different teams. Among them, I'm part of the data analytics where, our team, almost 10 people are using it. But I'm not sure about the rest of the teams.

We are using Databricks extensively. We have a team of 10 using the solution.

The initial setup of Databricks is not straightforward. You need to create VLANs, VPNs, and networks. We are two ways of deployment, we are having the legacy PowerShell for the deployment and the template method to deploy the Databricks code to higher levels.

We have not integrated Databricks directly into the DevOps architecture. We are downloading the notebooks manually and we are uploading them.

The billing of Databricks can be difficult and should improve.

We have evaluated Azure Synapse and SQL. Both Databricks and Azure Synapse are similar, the UI is the only difference. SQL and Databricks are the same, and one of the largest setbacks is the processing of a lot of data takes a long time.

I rate Databricks a seven out of ten.

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 for customer marketing analytics.

Databricks lets you schedule jobs pretty easily, and you can use SQL, Spark SQL, Python, or R. It also allows you to save a table or view. 

I like that you can connect to multiple data sources. Most of our data is stored in the Azure data lake, but my previous company connected to SQL databases or even blob storage. 

They've improved on many features. I don't do data engineering, but I had an issue a couple of years ago at my two companies ago. It took a long time to read and save tables, but I think the new Delta feature helped. 

I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature.

I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data. 

Because I work in analytics and not data engineering, I think that's probably the biggest one. There are better graphical tools, so I don't think Databricks can compete. You can do a simple graph, and it's not that great. However, I don't think they can ever stack up to Tableau, so it's probably not worth it to improve upon that. 

I've been using Databricks for two years.

Databricks is stable.

Databricks is scalable.

Databricks tech support has been great every time I've dealt with them. Their team is highly knowledgeable. 

Setting up Databricks is easy. I set it up at my previous company. That was on Azure as well, but they utilized a third-party team with expertise in Databricks to ensure everything was optimized. 

I rate Databricks 10 out of 10. I recommend taking advantage of Databricks support or a third-party provider to ensure it's set up optimally. I don't know if it's an additional service you must pay for, but we always had access to Databricks support in my last company. 

I think that's worth the money because there are so many different scenarios with distributed computing. Even people who study analytics may not understand the ins and out of Spark. It's worth it to have a service contract for support.

I use Databricks to explore new features and provide the industry visibility and scalability of Databricks to the companies that I work with.

I create proof of concepts for companies. As a consultant, I also create training courses on Databricks. If a company wants to leverage a service provided by Databricks and needs to train people, they use our courses.

Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance.

Databricks has made great strides in terms of performance. 

It is very user friendly. I like the ease of creating a Spark cluster, submitting a job, or creating a notebook.

The UI has also changed for the better compared to what it was two years ago.

If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks.

It's a big ask to have people jump through a lot of hoops to get approval to create a Databricks cluster just to explore it, but if they can try it on their own with a free trial without an underlying cloud account it would be more convenient.

Documentation can be improved as well. There are so many versions of documents. For example, when I tried to create a DBU vault and secrets file, I had to go through multiple versions of documents. This could be improved so that the documentation is easy to use.

I've been using this solution for about two years.

Stability wise, it's quite okay. In my experience, it doesn't crash.

I have not used autoscaling because it consumes a lot of money and because my experience has been alright. In some cases, though, it is tied to the quota of the underlying infrastructure. I have not tested the scalability to its fullest extent, but with the workloads I run, it has been fine.

When I wanted to create an AWS account and contacted technical support via email, I never received a response. Recently, however, I think they have improved their support a little bit, and I did get a call in response to my question. Overall, I've not faced any issues with the person I had to contact directly.

The initial setup is not very easy, but it's medium in complexity.

Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price.

I would rate Databricks at seven on a scale from one to ten. If you compare it to Snowflake, for example, Snowflake doesn't mandate an underlying cloud account. It creates one on its own. That's a subtle convenience that Snowflake has and one that Databricks could also build.

Snowflake's documentation is easy to use in comparison to that of Databricks. 

We use Databricks for video streaming and security purposes.

Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions.

I would like it if Databricks made it easier to set up a project. The use case determines which services we are going to use. You have the application engine, and you generate a potential budget for your workloads, so you can understand what you are going to do, what you are going to use, and what you will invest in.

Because I'm deploying on the Google Cloud Platform, measuring the investment, value, and use case is extremely difficult. So I leave it and move on without the risk. It would be easier if I had one page where you can see three columns: one for the use cases of a specific architecture, a second one for the prices based on the volume of data or machine time, and the third column for the budget. That would make it easier to know if I am using the appropriate architecture for the right solution.

I have seen something like that in Microsoft Azure, but obviously Microsoft Azure costs a lot of money. Amazon has something like that, but it's very complicated to use.

We've been using Databricks for about five years.

Databricks is very stable and powerful.

It was simple to make Databricks scalable. We found that we could set up an alert to tell us if we needed more resources, money, or time from our team. We're alerted when the system detects some trigger for any use of the instance. If you have another alert from your side, that would be extremely useful because it takes a lot of time to develop that kind of trigger. 

Databricks technical support was lovely. We don't need it so much, but the few questions we had were answered immediately.

I am not a data engineer because I just started data science at the company, but it was straightforward and clear for the architect to set up. He provided me with that idea because he realized it would take time if we had use cases. You can select and change the data or add some modules or products. You have all the technology to do so.

I rate Databricks eight out of 10. I like to move my customers into Databricks, but I take care of the internal system infrastructure so they can continue to use familiar software or operating systems and databases. They have a lot of doubts because they don't know the solution. We need to train them, explain things, and show the solution's potential value. 

Generally, companies try to keep the same flavor when they migrate. For example, if they are using many Microsoft products, they want to work with Azure. If they are open to other options, they go with GCP or AWS. However, Databricks doesn't have enough customers here in my market because it's not a visible brand. Azure, GCP, and AWS are highly visible here, so the local teams are friendly with the three brands.

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 were using Databricks to build an AI solution. We are only evaluating it, we have approximately three people that tried it out. Later we choose another solution, we did not fully deploy Databricks.

Before I used Databricks it took me a long time to do some functions and now with Databricks I can do them much quicker. It scales very well.

The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good.

I have used Databricks within the last 12 months.

The solution is stable.

Databricks is a scalable solution. It is the largest advantage of the solution.

We have been in contact with the technical support of Databricks, they were good.

We have used a lot of different solutions, such as Watson and DataIQ.

The initial setup is easy. However, I do not know much about the implementation because the company does it.

We did the implementation of the solution.

If companies want scalability, they should choose Databricks.

I rate Databricks a nine 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. 

We primarily use the solution for retail and manufacturing companies. It allows us to build data lakes.

The solution is very easy to use. 

The storage on offer is very good. 

The solution is perfect for dealing with big data.

The artificial intelligence on offer is very good.

The product is quite flexible.

We have found the solution to be stable. 

The cloud services on offer are very reasonably priced.

Technical support is very good. They also have very good documentation on offer to help you navigate the product and learn about its offerings. 

The solution works very well for us. I can't recall any missing features or anything the solution really lacks. It's very complete. 

It would help if there were different versions of the solution on offer.

The integration of data could be a bit better.

I've worked for about 20 to 25 years in business intelligence analytics and have worked with Databricks for about four years at this point. 

The stability of the solution is very good. It doesn't crash or freeze. There are no bugs or glitches. Its performance is very good.

The scalability is quite good. A company that needs to expand it can do so with ease.

We only have four people on the solution at this time. The front-end users never use the product directly. The companies aren't that big here. If the economy improves, we'll likely have more of a need for the product.

I've dealt with technical support in the past and have found them to be very good. They are helpful and responsive. We are satisfied with their level of service.

I work with  Databricks, Cloudera and Snowflake.

The solution is on the cloud and therefore there isn't really an installation process that you need to go through. You only really need to configure the clusters. 

Within the clusters, you configure according to how many platforms you need, or if you want to, you can build a cluster for artificial intelligence. You just configure it as required. 

The pricing of the product is very reasonable. The fact that it is on the cloud makes it a less expensive option. Other solutions that are on-premises are quite expensive.

We are customers and end-users. 

Databricks is on the could and therefore, we're always on the latest version of the solution. It's constantly updated for us so that we have access to the latest updates and upgrades. 

I'd rate the solution at a nine out of ten. The capability of the product is quite good and we are very satisfied with it overall. 

I'd recommend the solution to other companies and organizations.

We use this solution to build skill and text classification models.

The scalability brings value to this solution.

It can send out large data amounts.

The user experience can be improved. 

It's not easy to use, and they need a better UI.

I have been dealing with Databricks for more than five years.

We used this solution last five months ago and used the most current version during that time.

This solution is quite stable. We have not had any issues with stability.

It's a scalable solution. Very few people are using this solution in our organization. Most don't have the skill.

We were using the free version which did not have a lot of support.

We didn't really need support at the time. I had one conversation with them and they were very nice. They were helpful.

We are using Dataiku for one project and also SageMaker. We have some issues with scalability using SageMaker, which is why we may be going back to Databricks.

SageMaker is a very specific AI tool.

The initial setup was okay.

There are many different versions. 

We used the trial version, which was free.

If you have a lot of data, Databricks is a good choice. 

With the migration of Microsoft and Databricks, they make it easy. It's the direction to go in.

It's a very good tool. I would rate Databricks a nine out of ten.