What advice do you have for others considering Databricks?

Databricks is a robust solution for big data processing, offering flexibility and powerful features. While there are areas for improvement, especially in performance and cluster management, it remains a highly valuable tool in my data science toolkit. I rate it a seven.

The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale. For my general use cases, I would say that I am not a technical person, so I cannot explain to you how the tool helps with the area of data engineering tasks. There is another team in my company that is involved in the use of machine learning and AI features in Databricks. My team is mostly into operations. The tool is used in a multi-country project. For example, in my company, they make some shopping decisions related to solutions based on what is the product chosen by the whole company. I rate the tool an eight 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.

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 rate the solution a nine out of ten.

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

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.

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.

The tool’s performance is great. I would rate it 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 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.

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

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'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.

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 rate Databricks an eight 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 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.

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

I rate Databricks a nine out of ten.

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

I rate Databricks a seven out of ten.

I would rate this solution 8 out of 10.

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 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.

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.

If companies want scalability, they should choose Databricks. I rate Databricks a nine 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.

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.

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.

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.