Databricks Reviews

Our primary use case is for data analytics. Essentially, we use it for the financial reporting for Adobe.

The way Databricks has improved my organization is definitely through giving us improved performance and the processing power. We are usually never able to achieve it using traditional data warehouses. When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks.

The features I found most helpful with Databricks are the Lakehouse and SQL environments.

I believe that this product could be improved by becoming more user-friendly. 

In the next release, I would like to see more flexibility in the dashboard. It has plenty of features but it can be enhanced so that it matches with other visualization tools, like Power BI and Tableau. Also, the integrations with other tools could be better.

I have been using Databricks for the last three years.

I would rate the stability of Databricks an eight, on a scale from one to 10, with one being the worst and 10 being the best.

I would rate the scalability of this solution a nine, on a scale from one to 10, with one being the worst and 10 being the best. I would say there are around 2,000 to 3,000 users of this solution in our organization.

I've been in contact with the Databricks support team and received timely support from them. I would rate their support an eight, on a scale from one to 10, with one being the worst and 10 being the best.

Prior to Databricks, we initially used Hadoop. Afterwards, we used HANA, SAP HANA, and the Microsoft SQL Server.

The initial setup was relatively straightforward. I would rate it nine, on a scale from one to 10, with one being the easiest and 10 being the hardest.

There is no need to worry about the deployment as it can be done quickly. It is relatively automated. We used Terraform for auto-deployment, which happens in Azure. With Terraform, there are two options. As option one, you can deploy manually by creating services. For option two, you use Terraform and automate. Terraform is like infrastructure as a code where you can code the deployment part using it.

There were two or three persons involved in the deployment of this solution.

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

I've worked with Databricks primarily in the pharmaceuticals and life sciences space, which means a lot of work on patient-level data and the predictive analytics around that.

Another use case for Databricks is in the manufacturing industry. I'm a consultant, so the use cases for the product vary, but my primary use case for it is in the pharma space.

From a data science and applied analytics perspective, what I like about Databricks is that it's probably one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that, and then go ahead and make that available for dissemination of insights. For example, you can save all data and build out endpoints, so business analysts and users can access that data through a dashboard.

During the process, I also like that Databricks allows you to do portion control to keep track of your operations on the data and maintain that lineage to create reproducible results. 

The most significant Databricks advantage is that you can do everything within the platform. You don't need to exit the platform because it's a one-stop shop that can help you do all processes.

The solution is top-notch from a data science, applied ML, or advanced analytics perspective.

I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement. Still, I am generally unaware of any super-critical issues.

My experience with Databricks is two and a half years.

Databricks stability is an eight out of ten because I never had issues with its stability.

Databricks has high scalability. Most of my work on the solution has been in the pharma space, which has massive data sets, so it's a nine out of ten, scalability-wise.

I've never dealt with the Databricks technical support team.

I don't have experience setting up Databricks because that's generally taken care of by the IT, data, or software engineering team before the data science team comes in and starts leveraging the platform. I have yet to experience setting up the Databricks environment personally. However, I have had experience setting up clusters, which was pretty straightforward. Still, in the overall environment of an enterprise-wide system, I have yet to gain experience setting Databricks up.

The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is. If it's a global organization, that cost varies versus a smaller organization that has just adopted the platform and is trying to onboard a small team of five people. It depends.

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.

We use this solution for advanced civilization power.

We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time.

This product allows us to write the email models in a way that allows us to take the advantage of the parallel scaling computer window backend on any of the satellite services.

This solution only supports queries in SQL and Python, which is a bit limiting. 

This is a fairly expensive solution for any service outside of the basic package, and costs can add up quite quickly if there are large scaling requirements.

This is a stable solution in our experience.

We have found that part of the beauty of this platform is that it is easy to scale and expand.

The support for this product uses Microsoft as a middle man, and due to this there have been times when we experienced communication delays, as well as misunderstandings of what our issues are.

Neutral

The initial setup for this solution is very simple.

The basic version of this solution is now open-source, so there are no license costs involved. However, there is a charge for any advanced functionality and this can be quite expensive.

We looked at both Snowflake and BigQuery as a comparison with this solution. We choose this product as it offered more scalability and a higher level of security, which is extremely important in our banking environment.

We would rate this solution an eight out of ten.

We use the solution for data engineering. 

The tool helps us manage large amounts of data. 

The simplicity of development is the most valuable feature. 

Databricks has a lack of debuggers, and it would be good to see more components. 

Another issue is that the D4 data format keeps changing on our cluster. This doesn't affect me much because I use functions to define it, but it is very frustrating for some more casual users. One day the output will be in a particular format, and then it becomes an object without us changing the cluster configuration. As a small team, we don't have the capacity to dig deeply into the issue, which has been frustrating.

We have been using the solution for three years. 

The solution's stability is good. 

The product is scalable. We're a small organization with 12 users, and we don't currently have any plans to increase our usage.

We see an ROI from Databricks. 

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.

Our primary use case is ETL.

Using Databricks enables us to use the Data Mesh methodology, where every team performs their own ETL.

The most valuable feature is the versatility of the ecosystem. You can write code in SQL, Python, or Java.

The load distribution capabilities are good, and you can perform data processing tasks very quickly.

You can save and share notebooks between different teams.

The interface is easy to use.

The cost of this solution is high, on the expensive side.

In the future, I would like to see Data Lake support. That is something that I'm looking forward to.

I worked with Databricks for approximately two years in my previous company.

This is a very scalable solution. We have twenty-five data engineers that use it, and we may grow our usage.

The technical support is okay. I would rate them a seven out of ten.

Neutral

We did not use another similar solution prior to Databricks.

The cloud-based deployment is simple.

If you use an on-premises deployment then there is more to do.

We deployed it with our in-house team.

There is no maintenance required.

We have seen a return on our investment with Databricks.

Price-wise, I would rate Databricks a three out of five.

When we looked into Databricks, we evaluated Azure Data Factory and some of the others on the market. We found that Databricks was one of the easiest ones to use.

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.

We use the solution for reliability engineering, where we apply ML and Deep Learning models to identify the fear failure patterns across different geographies and products.

Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy.

Databricks would have more collaborative features than it has. It should have some more customization for the jobs. Also, it has an average dashboarding tool. They can bring advanced features so we don't depend on other BI tools to build a dashboard. We are using Tableau to create a dashboard. If Databricks has more advanced features, we can entirely use Databricks.

I have been using Databricks for one year.

The product is stable. It has been giving consistent outputs without any major issues.

The solution is hosted on the cloud. It supports high scalability features.

10-20 users are using this solution.

There was a training session from Databricks where they explained how to use it. We never had to contact them because they had already given us proper training on the platform.

I have used Alteryx before. We switched to Databricks because it can compute and turn your code into production-ready code in very few seconds. Also, the stability is relatively high.

The initial setup is easy.

We have a dedicated team for the deployment.

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.

The product has helped in data fabrication. 

Databricks has helped us have a good presence in data. 

The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice. 

I have been using the product for more than six months. 

I rate Databricks' an eight out of ten. 

I rate the tool's scalability an eight out of ten. 

The transition to Databricks was smooth. 

Databricks' price is high. 

I rate the solution a nine out of ten. 

I worked with Databricks pretty recently. The particular design processes involved in Databricks were also a part of that specific design/architectural process.

We have used the solution for the overall data foundation ecosystem for processing and storage on a Delta format. We have also seen use cases where we were trying to establish advanced analytics models and data sharing where we leverage the Delta Sharing capabilities from Databricks.

A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem.

There are some aspects of Databricks, like generative AI, where they are positioning things like DALL-E. They're a little bit late to the game, but I think there are some things that they are working on. Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster, and even though they are fast, I'm not sure how they'll catch up and get adopted because there are strong players in the market.

Databricks is coming up with a few good things in terms of integration. But I have to put one point forward that covers multiple aspects, which is the ease of use for the end user while operating this particular tool. For example, a tool like ADS gives you a GUI-based development, which is good for the end user who does development or maintenance. Looking at the complexities of data integration, a GUI might not be easy, but Databricks should embrace something on the graphical user development front because it is currently notebook-driven. Also, in terms of accessing the data for the end user, Databricks has an SQL interface, similar to earlier tools like SQL Management Studio. Since people are mostly comfortable with SSMS already or not, Databricks can build integration to known tools for data access, and that also helps, apart from what they're doing. I would like to see improvements with respect to user enablement, which is a good part of enterprise strategy. I would like to see their integration with a broader ecosystem of products. If you have to do data governance in tools like Microsoft Purview, it's manual and difficult. Now, I'm unsure if that momentum must be from Databricks or Microsoft. But it would be good if Databricks had some open interfaces to share metadata, which could be viewed in tools enabling data governance like Collibra, Purview, or Informatica. The improvement has to do with user and metadata integration for tools.

I've worked with Databricks for over five or six years, but it's been on and off.

The solution is scalable. In this particular ecosystem, there is no one else who can catch up with Databricks for now.

Databricks' customer support is very good. They have a lot of ways in which they interact with vendors and service partners across the globe. They have periodic touch-up sessions with vendors, where their engineers answer your questions.

The implementation is not challenging because the solution integrates well with the platforms on which they are established, whether it's Azure, AWS, or GCP. The solution is not difficult to set up, but you'd probably need a technical user to operate it.

It's the same story with maintenance, where you'd need a technically proficient person with programming knowledge to maintain it.

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.

Our primary use case is in our project; we are dealing with Duo Special Data, where we need a lot of computing resources. Here, the traditional warehouse cannot handle the amount of data we are using, and this is where Databricks comes into the picture. 

The processing capacity is tremendous in the database. We are dealing with Azure as storage, so we have not faced any challenges. And also the connectors to different data sources. Moreover, it is not a language-dependent tool. Therefore, development also takes place faster. It is one of the best features of Databricks.

There is room for improvement in the documentation of processes and how it works. I was trying to get one of the certifications, so I saw an area of improvement there. 

I have been using Databricks for eight to nine months.

It is a stable product for us. We didn't see any challenges. 

There are around 30 to 35 users in our organization. 

The initial setup was easy because the third-party team made the clusters for us. 

A third-party team enabled the cluster to make the setup easy for us. 

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.

We use it for data analysis and testing of high volume web user behavioral data.

Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes.

I'm starting to build a solution using Delta Live Tables and Delta Live pipelines, and it is proving to be exceptionally easy to use. I have also been able to quickly implement a pipeline.

Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present.

I've been using Databricks for a year.

It is a stable and reliable solution. I'd rate stability at eight out of ten.

Databricks is absolutely scalable, and I'd rate scalability at eight out of ten. We probably have between 60 and 100 users in our organization, and we hope to increase usage in the future.

The technical support staff we have worked with have been amazing. They helped us initially with our Delta Live pipelines. I would give them a rating of ten out of ten.

Positive

I have previously worked with Apache Hadoop, and Databricks is definitely a better product. It's much easier to get data quickly in Databricks. As a result, a lot of the drudgery is taken away. Whereas with Hadoop, it's a bit more tricky to get data together.

We're charged on what the data throughput is and also what the compute time is.

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.