We use it for data analysis and testing of high volume web user behavioral data.
Sr. Data Quality Analyst at Seek
Can use different technologies to do data analysis and can quickly get data
Pros and Cons
- "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."
- "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."
What is our primary use case?
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I've been using Databricks for a year.
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What do I think about the stability of the solution?
It is a stable and reliable solution. I'd rate stability at eight out of ten.
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
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.
What's my experience with pricing, setup cost, and licensing?
We're charged on what the data throughput is and also what the compute time is.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
Disclosure: My company does not have a business relationship with this vendor other than being a customer.

Executive Manager at Hexagon AB
Excellent data transformation but data-serving performance could be better
Pros and Cons
- "Databricks' most valuable feature is the data transformation through PySpark."
- "Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
What is our primary use case?
We mainly use Databricks to process ingest and do the ELT processes of data to get it ready for analytics and to serve the data to ThoughtSpot, which calls queries and Databricks to get the data.
How has it helped my organization?
We didn't have any good tooling for ELT processing prior to Databricks. We were using Microsoft HD Insight, but it was taking too long to process the data. When we changed our data-processing ELT processes over to Databricks, the amount of time to process the data was reduced to a fraction of what HD Insight used, so we were able to run jobs much faster.
What is most valuable?
Databricks' most valuable feature is the data transformation through PySpark.
What needs improvement?
Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's. In the next release, Databricks should include a better data-sharing platform to facilitate data sharing between companies.
For how long have I used the solution?
I've been using Databricks for three years.
What do I think about the stability of the solution?
Databricks' stability has been great, and I would rate it eight out of ten.
What do I think about the scalability of the solution?
Databricks is very scalable because it's very easy to spin up multiple clusters, but the cost of doing that is tremendous. I'd rate its scalability nine out of ten, but you'll pay for it.
How are customer service and support?
The technical support has been really bad, but that's because we don't have a direct agreement with Databricks.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
I previously used HD Insight from Microsoft, but it took many, many hours to process data, so we switched to Databricks.
How was the initial setup?
The initial setup was pretty complex and required three people.
What about the implementation team?
We used an in-house team with some consulting help.
What was our ROI?
We've had a low ROI from Databricks.
What's my experience with pricing, setup cost, and licensing?
I would rate Databricks' pricing seven out of ten.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
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Databricks
September 2025

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Computer Scientist at Adobe
Pumps up performance and the processing power; comes with helpful Lakehouse and SQL environments
Pros and Cons
- "When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."
- "I believe that this product could be improved by becoming more user-friendly."
What is our primary use case?
Our primary use case is for data analytics. Essentially, we use it for the financial reporting for Adobe.
How has it helped my organization?
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.
What is most valuable?
The features I found most helpful with Databricks are the Lakehouse and SQL environments.
What needs improvement?
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.
For how long have I used the solution?
I have been using Databricks for the last three years.
What do I think about the stability of the solution?
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.
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
Prior to Databricks, we initially used Hadoop. Afterwards, we used HANA, SAP HANA, and the Microsoft SQL Server.
How was the initial setup?
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.
What other advice do I have?
The new version of the Databricks solution requires code maintenance. This is done by the platform team.
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Vice President - Data Engineering and Analytics at a financial services firm with 10,001+ employees
A good, but expensive, web-based platform for automated cluster management with some coding limitations
Pros and Cons
- "We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
- "This solution only supports queries in SQL and Python, which is a bit limiting."
What is our primary use case?
We use this solution for advanced civilization power.
What is most valuable?
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.
What needs improvement?
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.
What do I think about the stability of the solution?
This is a stable solution in our experience.
What do I think about the scalability of the solution?
We have found that part of the beauty of this platform is that it is easy to scale and expand.
How are customer service and support?
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.
How would you rate customer service and support?
Neutral
How was the initial setup?
The initial setup for this solution is very simple.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
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.
What other advice do I have?
We would rate this solution an eight out of ten.
Which deployment model are you using for this solution?
On-premises
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
A stable, scalable solution that simplifies the development process but needs more debuggers and components
Pros and Cons
- "The simplicity of development is the most valuable feature."
- "Databricks has a lack of debuggers, and it would be good to see more components."
What is our primary use case?
We use the solution for data engineering.
How has it helped my organization?
The tool helps us manage large amounts of data.
What is most valuable?
The simplicity of development is the most valuable feature.
What needs improvement?
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.
For how long have I used the solution?
We have been using the solution for three years.
What do I think about the stability of the solution?
The solution's stability is good.
What do I think about the scalability of the solution?
The product is scalable. We're a small organization with 12 users, and we don't currently have any plans to increase our usage.
What was our ROI?
We see an ROI from Databricks.
What other advice do I have?
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.
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Sr Data Engineer at PIMCO
Supports several coding languages, good performance, and facilitates team collaboration
Pros and Cons
- "The load distribution capabilities are good, and you can perform data processing tasks very quickly."
- "In the future, I would like to see Data Lake support. That is something that I'm looking forward to."
What is our primary use case?
Our primary use case is ETL.
How has it helped my organization?
Using Databricks enables us to use the Data Mesh methodology, where every team performs their own ETL.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I worked with Databricks for approximately two years in my previous company.
What do I think about the scalability of the solution?
This is a very scalable solution. We have twenty-five data engineers that use it, and we may grow our usage.
How are customer service and support?
The technical support is okay. I would rate them a seven out of ten.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
We did not use another similar solution prior to Databricks.
How was the initial setup?
The cloud-based deployment is simple.
If you use an on-premises deployment then there is more to do.
What about the implementation team?
We deployed it with our in-house team.
There is no maintenance required.
What was our ROI?
We have seen a return on our investment with Databricks.
What's my experience with pricing, setup cost, and licensing?
Price-wise, I would rate Databricks a three out of five.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
Disclosure: My company does not have a business relationship with this vendor other than being a customer.
Strategic Alliances& Ecosystems Manager at a outsourcing company with 501-1,000 employees
Helps to have a good data presence but needs to incorporate learning aspects
Pros and Cons
- "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."
What is our primary use case?
The product has helped in data fabrication.
How has it helped my organization?
Databricks has helped us have a good presence in data.
What needs improvement?
The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice.
For how long have I used the solution?
I have been using the product for more than six months.
What do I think about the stability of the solution?
I rate Databricks' an eight out of ten.
What do I think about the scalability of the solution?
I rate the tool's scalability an eight out of ten.
How was the initial setup?
The transition to Databricks was smooth.
What's my experience with pricing, setup cost, and licensing?
Databricks' price is high.
What other advice do I have?
I rate the solution a nine out of ten.
Disclosure: My company has a business relationship with this vendor other than being a customer.
Chief Executive Officer at dotFIT, LLC
A powerful solution that is easily integrated into a variety of platforms
Pros and Cons
- "It's very simple to use Databricks Apache Spark."
- "I would like more integration with SQL for using data in different workspaces."
What is our primary use case?
I am a Databricks service partner, and my customers use Azure Databricks and Data Factory.
What is most valuable?
It's very simple to use Databricks Apache Spark. It's really good for parallel execution to scale up the workload. In this context, the usage is more about virtual machines.
Using meta-stores like Hive was optional, and the solution is good for data science use cases. With the Authenticator Log, Databricks is good for data transformation and BI usage. We have a platform.
What needs improvement?
I would like more integration with SQL for using data in different workspaces. We use the user interface for some functionalities, while for others, we have to use SQL to create data sets and grant permissions. For example, when creating a cluster, we have to create it with some API or user interface. Creating a cluster with some properties using SQL grants the possibility of using SQL syntax. Integration with SQL will make Databricks easier to use by people who have experience with databases like Lakehouse, and they would be able to use the data lake and BI. More integration will help have one point of view for everyone using SQL syntax.
Integration with Kubernetes could also be good for minimizing the price because you can use Kubernetes instead of virtual machines. But that won't be easy.
For how long have I used the solution?
I have worked with the solution for four or five years, with some experience since 2016.
What do I think about the stability of the solution?
The solution is stable. The only problem with stability would be that people are not using it efficiently.
What do I think about the scalability of the solution?
The solution is good for scalability.
How was the initial setup?
When we have administration experience, the solution is not difficult to deploy. Technically, however, it's difficult because governance is more complex. For example, I have two warehouses on Databricks, which are clusters in this workspace, and we have to switch from workspace to workspace to have all this information. There is a system table that has all this, but I don't know if everyone can use these tables.
What's my experience with pricing, setup cost, and licensing?
Databricks are not costly when compared with other solutions' prices.
Which other solutions did I evaluate?
Databricks's functionalities are as good as solutions like Snowflake, BigQuery, and Redshift.
What other advice do I have?
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.
Disclosure: My company has a business relationship with this vendor other than being a customer. Partner

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