If you were talking to someone whose organization is considering Databricks, what would you say?
How would you rate it and why? Any other tips or advice?
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.
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 rate Databricks a nine 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.
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.
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.
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.
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 would rate Databricks an eight out of ten.
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.
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 would rate this solution an eight out of 10.
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.
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.
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.
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'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.
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.
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.
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.
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.
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