Databricks Reviews

I am using Databricks in my company.

The most valuable feature of Databricks is the notebook, data factory, and ease of use.

I have been using Databricks for approximately nine months.

The performance and stability of Databricks are good. It is quick and I have not had problems.

Databricks is highly scalable.

We have 200 people using the solution in my organization.

When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand.

I have not worked with another solution prior to Databricks.

The price of Databricks is reasonable compared to other solutions.

I rate Databricks an eight out of ten.

We build data solutions for the banking industry. Previously, we worked with AWS, but now we are on Azure. My role is to assess the current legacy applications and provide cloud alternatives based on the customers' requirements and expectations.

Databricks is a unified platform that provides features like streaming and batch processing. All the data scientists, analysts, and engineers can collaborate on a single platform. It has all the features, you need, so you don't need to go for any other tool. 

I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well.

The Unity Catalog provides you with the data links and material capabilities. These are some of the unique features that fulfill all the requirements of the banking domain.

Every tool has room for improvement. Normally what happens, a solution will claim it can do ETL and everything else, but you encounter some limitations when you actually start. Then you keep on interacting with the vendor, and they continue to upgrade it. For example, we haven't fully implemented Databricks Unity Catalog, a newly introduced feature. We need to check how it works and then accordingly, there can be improvements in that also.

Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity.

I have been using Databricks for a year.

Databricks relies on scalability and performance. Every cloud vendor prioritizes scalability, high availability, performance, and security. These are the most important reasons to move to the cloud.

Deploying Databricks on the cloud is straightforward. It's not like an on-premise solution, where you must create a cluster and all those other prerequisites for big data. 

I don't think it's challenging to maintain, but you need an expert programmer because Databricks isn't GUI-based. With GUI-based tools, building ETLs is drag-and-drop. Databricks entirely relies on coding, so you need skilled programmers to building your code, ETLs, etc. 

The price of Databricks is based on the computing volume. You also need to pay storage costs for the cloud where you're hosting Databricks, whether it is AWS, Azure, or Google. 

I rate Databricks nine out of 10. Databricks is one of the best tools on the market.

I am using Databricks for creating business intelligence solutions.

The most valuable feature of Databricks is the integration with Microsoft Azure.

Databricks can improve by making the documentation better.

I have been using Databricks for approximately one year.

Databricks is stable.

The scalability of Databricks is good.

We have approximately 500 users using this solution in my organization.

I have not used the support from Databricks.

We previously used Microsoft stacks. We chose Databricks because the processing power was better and it was a better fit for our use case.

The initial setup of Databricks was not straightforward. We had to do trial and error and we learned as we went along.

I rate the initial setup of Databricks a four out of five.

We did the implementation of Databricks in-house. The solution requires ongoing maintenance.

I would recommend this solution to others.

My advice to others is for them to first do a small proof of concept and then see how it works out and then take it from there.

I rate Databricks an eight out of ten.

This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities. All asset complaints properties are available and this is very useful to ensure the quality of all data.

The connectivity with various BI tools could be improved, specifically the performance and real time integration. There is also some improvement required in the semantic layers to manage the data match as well as the data warehouse features.

In a future release, we would like to have features to better manage all ML development activities.

I have been using this solution for three years. 

This is a stable solution, especially compared to other technology on the market.

It is a scalable solution but this depends on the platform that is being used. If you use a cloud platform such as Azure, it offers scalability. However, some platforms will not support scalability using Databricks.

We have around 20 users in our development team using Databricks. 

The customer service and support for this solution is good. 

Positive

The initial setup is pretty simple and requires minimal configuration compared to other technology.

I would rate the pricing for this solution a four out of five. This does depend on the environment or the infrastructure that one is using. There is a difference in pricing between using Azure or being on-premises. 

Azure Synapse is a competitor that we evaluated but it is not mature enough to provide better performance than Databricks. We choose Databricks due to the ability to have a lot of data in Data Lakes and the Data Warehouse. We are also able to run data science activities using ML flow.

If you are looking for custom model development and a lot of data management in a cloud agnostic manner, then Databricks is a good solution.

I would rate this solution an eight 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 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.