The solution is pretty good because it uses Azure's data lake storage. It is basically the tool on top that provides the SQL interface and APIs for Python. I like the solution because it enables people to work with it.

I rate the solution a nine out of ten. 

I rate the solution a seven out of ten. 

It is important to do POCs and run tests to control the meter that also controls the price. The meter can go really high from a computing perspective if POCs and settings are not streamlined. 

I rate the solution an eight out of ten. 

I would rate this solution 8 out of 10.

People sometimes do not use the solution efficiently. They misunderstand databases, the usage of tables, and the performance. Many data engineers are very junior and don't have skills in that. Stability is more a customer problem than a problem with the product itself. One possible problem with the product is that there's no method to pause the usage of something. For example, we have to use the meta server or the data catalog in Synapse. But in Databricks, we have a choice to use a catalog or not, or Hive, which is always integrated, but we have to choose whether to use it or not. Many customers directly use the passes on Databricks, which causes performance and governance problems.

I can offer a lot of advice on Databricks, and one is to use meta stores like Unity Catalog or Hive Metastore. For incoming use cases, it's better to use Unity Catalog.

I rate Databricks a nine out of ten.

It is a state-of-the-art product revolutionizing data analytics and machine learning workspaces. Databricks are a complete solution when it comes to working with data.

I rate the overall product an eight out of ten.

Databricks integrates many enterprise processes because data processing and AIML are a small part of a larger ecosystem. Databricks has been a part of other platforms, and they are trying to establish their platform, which is a good direction.

Most of the capabilities of the underlying platform can be leveraged there. But the setup isn't difficult if the database lacks some capability, you can't find it in the database, or you're not comfortable with a certain feature in the database. It integrates well with the underlying platform. For example, with scheduling, let's say you are uncomfortable with workflow management. You can utilize integrations with EDA for any other tool and probably perform scheduling. Even if what you're trying to do is not easy, it is enabled with integration. Either they build a required feature in their tool later on, like a GUI, or you perform integrations to make the features possible.

We did evaluate licensing costs, but it had more to do with the Azure ecosystem pricing since whatever we are doing has more to do with Azure Databricks. Many optimizations are recommended, but we haven't exercised those for now. But considering that the processing is a bit more efficient, the overall price won't be much different from what it could be for any other similar component or technology. We haven't had specific discussions with Databricks' folks on pricing.

My advice to users who would like to start working with Databricks is that it is a good solution to work with for data integration and machine learning. Databricks is maturing for other use cases, so there are two points to be considered. One is that you need to evaluate how they will mature, which will be on a case-to-case basis. Second, how will it align with the overall platform story? There will be many overlapping aspects over there as Databricks expands its capabilities. In that case, it must be considered that if those capabilities overlap, how will the underlying platform vendors handle it? How would that interplay happen if many of Databricks' new capabilities align with Microsoft Fabric? That has to be very carefully considered. Otherwise, if you utilize those new capabilities, there might be a discontinuity where you cannot use Databricks because the platform does not support that.

If I specifically talk about Spark-based processing transformations, the data integration story, and advanced stability, I would rate Databricks around eight out of ten. However, with respect to new capabilities like cataloging, data governance, and security integration, I rate Databricks around five because it has to establish these features. And since Databricks integrates with platforms, we must see the interplay with the platforms' capabilities.

I overall rate Databricks a seven out of ten.

We're a partner.

We use the solution on various clouds. Mostly it is Aure. However, we also have Google and AWS as well. 

One of the big advantages is that it works across domains. I'm responsible for a data engineering team. However, I work on the same platform with data scientists, and I'm very close to my IT team, who is in charge of the data access and data access control, and they can manage all the accesses from one point to all the data assets. It's very useful for me as a data engineer. I'm sure that my IT director would say it's very useful for him too. They managed to build a solution that can very easily cross responsibilities. It unifies all the challenges in one place and solves them all mostly.

I'd rate the solution nine out of ten.

I would rate the solution seven out of ten. 

It's a good solution and more for handling large amounts of data. Databricks is better as a batch processing system than as an interactive system. The performance is a little disappointing because the memory processing is supposed to be excellent, but it's not as competitive as some other solutions out there in this regard. Even classical databases can respond and process faster.

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. 

I would advise using it based on the use case because it easily handles big data. It is your go-to tool if you are dealing with massive data. 

Overall, I would rate the solution a nine out of ten. The tool performs well in various use cases, availability of documentation online, and compatibility with big data systems like GCP, Azure, or AWS.

I rate this solution nine out of ten. The solution is good but can be improved by integrating with a popular coding IDE.

My advice for anybody that is looking into Databricks is not to use the on-premises deployment. Instead, use the cloud-based setup.

In summary, this is a good product.

I would rate this solution an eight out of ten.

Overall, I would rate the solution a eight out of ten. I would definitely recommend this solution for small organizations. 

I would advise anyone thinking of implementing Databricks to know their use case. For example, if you're looking for a big data repository to query data and do ELT processing, I recommend looking at other platforms, like Snowflake. However, if you're going to do AI and machine learning, then Databricks is probably stronger in that area. Overall, I would rate Databricks seven out of ten.

The new version of the Databricks solution requires code maintenance. This is done by the platform team.

We would rate this solution an eight out of ten.

There isn't really a version, per se. 

It's a popular service. I'd recommend the solution. The solution is cloud-agnostic right now, so it really can go into any cloud. It's the users who will be leveraging installed environments that can have these services, no matter if they are using Azure or Ubiquiti, or other systems.

I don't think you can find any other tool or any other service that is faster them Databricks. I don't see that right now. It's your best option.

Overall, I'd rate the solution eight out of ten. The reason I'm not giving it full marks is that it's expensive compared to open source alternatives. Also, the configuration is difficult, so sometimes you need to spend a couple of hours to get it right.

I'm a data scientist, so I frequently use Databricks and Domino Data Science Platform.

I'm a consultant, so every client has a different version or a different runtime in Databricks, so the versions used would vary per client.

The deployment for the solution is on the cloud, predominantly on AWS or Azure.

My clients adopted Databricks as the platform of choice, and with different use cases and more teams coming on board, the usage of Databricks will increase. I don't see that going down. It can only go up.

My advice to anyone looking into implementing Databricks is that it should be one of your top choices, especially if you're looking to focus on data processing, standard ETL operations, advanced analytics, or the ML type of work.

I'd rate the solution as nine out of ten. It checks almost all the boxes that modern applications need to have.

My organization is an active partner and implementer of Databricks, but it doesn't resell the solution.

There are many services which are coming up. They are still in the preview stage. Overall, I rate the product an eight out of ten.

I'd strongly recommend giving Databricks a try. We have found it to be a fantastic tool that has accelerated some of our solutions. We're an AI-heavy shop, and there are a lot of data scientists using the MLflow capabilities. I hear a lot of good things from that side as well. From a data analysis point of view, Databricks has been fantastic, and I would rate it at eight on a scale from one to ten.

I work with a solution provider. We are a Databrick customer.

We are not partners of Databricks. Only we are partnered with Microsoft Azure and Amazon AWS.

We are using the latest version of the solution. However, I do not know the exact version number. 

I still need time with the solution before providing advice to others. I need to prepare the capacity internally. So far, it's been great.

I'd rate the solution eight out of ten. 

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 rate the solution a nine out of ten. 

I would advise others to first define a strong data strategy and then choose which data platform suits your needs. 

I would rate this solution a nine out of ten. 

Delta Lake is a free system. We practically work on the data that we get from Snowflake. Databricks are returned to the model outputs that are returned to Delta Lake. It is easy for us to collaborate using Delta Lake, and the computation speed is also quite high for Delta Lake.

The learning curve for Databricks is not very steep. It's pretty easy, and you will find a lot of materials online. So, if you are comfortable coding in Python, it's very straightforward. There is nothing to worry about when using Databricks.

Overall, I rate the solution a ten out of ten.

I would tell potential users that they need proper cloud engineers and a 
cloud infrastructure team to use this solution.

On a scale from one to ten, I would give Databricks a nine.

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 rate this solution a nine out of ten. Regarding advice, Databricks is a very good platform, popular and easy to use daily for data engineers and data scientists who rely on a large dataset to do advanced analytics reporting. It's a very good tool.

If companies want scalability, they should choose Databricks.

I rate Databricks a nine out of ten.

I rate the solution an eight out of ten. The solution is good but can be improved by including drag-and-drop features because it can be helpful for users who are unfamiliar with coding. I advise new users to have prior experience with Python or SQL before utilizing this solution if they use it for data science or model building. 

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

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.

I rate Databricks a seven out of ten.

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 a data engineer, data analyst, and machine learning engineer for the maintenance of the solution.

I rate Databricks a nine out of ten.

Overall, I would rate this solution an eight out of 10, with one being quite poor and 10 being excellent. It is fast, it's scalable, and it does the job it needs to do.

Databricks is a one-stop shop for everything data related, and it can scale with you.

I would rate this solution a 9.5 out of 10 overall.

I would rate this solution an eight out of ten. 

There is an issue where clusters are automatically deleted after termination or after 100 days of non-usage. This could be more user-friendly, and they could include an enabler to pin the clusters you want to keep, instead of having to go and research why clusters got deleted after implementing the product. That documentation needs to be right in front of the user to avoid issues.

I definitely recommend this product to other users. 

Having a good understanding of physical security in relation to cybersecurity in an OT (Operational Technology) environment would be beneficial, and utilizing an existing data lake prior to implementing a Databricks initiative would greatly aid in its success.

I rate Databricks an eight out of ten.

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. 

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.

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. 

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. 

By investing in people skilled in data querying, Python coding, and even basic Data Science, a Databricks setup will reward the business. 

Once the Databricks data flows are established, it is a matter of a few incremental steps to opening up streaming and running up-to-the-minute queries, allowing the business to build its data-driven processes. 

Databricks continues to advance the state-of-the-art and will be my go-to choice for mission-critical PySpark and ML workflows. 

I am currently implementing the latest version of Databricks.

The Databricks solution is deployed through Cloud.

I would rate the Databricks solution a nine.

I would recommend trying Databricks because it's cloud agnostic. A lot of customers currently use Azure but want to build something on their own down the track. Databricks makes that easy with its integration with other cloud customers. If somebody wants to build something on their infrastructure or their own virtual cloud, this is a good platform.

I rate the solution eight out of 10 because of the issue I'm having with a lack of trial options.

I rate the solution an eight out of ten.

I rate Databricks a nine out of ten.

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

I rate Databricks an eight out of ten.

As we transition to the Azure cloud, I expect that we will be using Databricks for workloads.

This is a product that I recommend for those who want to scale and have a good budget. It is good for automating a data pipeline and managing workloads. My advice for anybody who is starting to use it is to take the proper training.

Overall, based on my uses, I think that this product is pretty good.

I would rate this solution an eight out of ten.

In the current capacity as and Architect and the end user of Databricks I would say I do have confidence that Databricks can provide a wealth of functionalities to start with. 

My advice to future adopters of Databricks would be to be careful about the overall architectural roadmap for this application, adopt a flexible, modular, microservices like architecture whose components could be replaced in the future should they deem inadequate to cater for evolving business needs. 

Our client is a bank and some of the information can be shared outside of the organization, whereas some of the data is confidential and private. Using a purely on-premises solution would have made it more difficult to share information with the outside, which is one of the reasons that they wanted a cloud-based deployment. 

My advice for anybody who is considering this solution is that it is very good for unstructured or semi-structured data. If, however, you have structured data then I would recommend a columnar database like Snowflake or Vertica. These solutions are easier to deploy.

This is a good solution that is working well, but I don't think that it is really a SaaS.

I would rate this solution a seven out of ten.

If you're thinking of implementing Databricks, I would recommend working with professionals. It'll help you save time. Also, plan the work and work the plan. Otherwise, it'll be a waste of time and money.

On a scale from one to ten, I would give Databricks a nine.

I think the point is that because we'll be working collaboratively in the future, internally and externally, we should compare experiences and exchange knowledge. 

I would rate this solution an eight out of 10. 

The product has improved and I'm sure this will continue in the next versions. We are completely satisfied with it, the ease of connecting to different sources of data or pocket files in the search. 

I think it could be very interesting for users looking for a framework to use Databricks. I would, however, recommend a more complicated architecture for using Databricks and achieving a great result for end-users. 

I would rate this product an eight out of 10. 

I would rate Databricks an eight out of ten.

On a scale from one to ten where one is the worst and ten is the best, I would rate Databricks overall as around a 7 or 7.5. If we had more experience with it and could be sure we had a solid understanding of what it could do and the reliability, I might recommend it with a better score. I do not think I should give it more than a seven for now.  

I rate Databricks a seven out of ten.

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.

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.

With Databricks, you may have a lot of devices. It is important to use each cluster for each kind of process and then not use the small clusters. Using the bigger cluster you will receive better performance and the use is closer and will save you money. 

It is important to code it in parts because if you code it all in full you could find some problems with performance.

I would rate Databricks a 9 out of 10.

I would rate this solution an eight out of 10. 

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.

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.

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.

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.

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

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

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'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.

I work in the data science field and I found Databricks to be very useful. If I want to run any models then I can code them in PySpark. If you are coming from a Python background then you can write code in PySpark and it runs quickly. This is a good solution in terms of performance. 

I would rate this solution a nine out of ten.

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.

My advice to others wanting to implement this solution is to use a cloud environment. For example, we are using Azure with Databricks. It is much better than doing an on-premise implementation.

I rate Databricks an eight out of ten.

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. 

I rate Databricks a nine out of ten.