Databricks Reviews

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 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.

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.

Positive

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.

We use this solution to build skill and text classification models.

The scalability brings value to this solution.

It can send out large data amounts.

The user experience can be improved. 

It's not easy to use, and they need a better UI.

I have been dealing with Databricks for more than five years.

We used this solution last five months ago and used the most current version during that time.

This solution is quite stable. We have not had any issues with stability.

It's a scalable solution. Very few people are using this solution in our organization. Most don't have the skill.

We were using the free version which did not have a lot of support.

We didn't really need support at the time. I had one conversation with them and they were very nice. They were helpful.

We are using Dataiku for one project and also SageMaker. We have some issues with scalability using SageMaker, which is why we may be going back to Databricks.

SageMaker is a very specific AI tool.

The initial setup was okay.

There are many different versions. 

We used the trial version, which was free.

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. 

It's mainly used for data science, data analytics, visualization, and industrial analytics.

Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours.

So that's why it's quite convenient to use for data science, for training machine learning models. By using more computing power, you can make it even faster.

There is room for improvement in visualization.

I used it for two years. I worked with the latest update. 

I would rate the stability a nine out of ten. I didn't face performance drops.

I would rate the scalability an eight out of ten.

Databrick's support is great. If we need any support, they are very quick with it. And they genuinely want you to use Databricks. So, whatever we ask them, they come up with multiple solutions to problem statements. That's really good.

Overall, the customer service and support are very good.

Positive

I personally prefer using Databricks. However, we also considered using Snowflake, but the pricing was different. It's  price per query.

So, as per your storage, a data scientist or a data analytics team needs to query again and again, which does not suit a data-heavy organization.

It's a good return on investment for Databricks from a delivery perspective. Delivered multiple dashboards. So, it's quite a good return on investment. And being a small organization, everyone can use Databricks, and cost-wise, it's also good for small organizations.

If the company is a startup, Databricks might be suitable. If a big company needs a lot of storage, Teradata might be best for them. It depends on the situation.

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

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.

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.

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. In the next release, Databricks should include a better data-sharing platform to facilitate data sharing between companies.

I've been using Databricks for three years.

Databricks' stability has been great, and I would rate it eight out of ten.

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.

The technical support has been really bad, but that's because we don't have a direct agreement with Databricks.

Neutral

I previously used HD Insight from Microsoft, but it took many, many hours to process data, so we switched to Databricks.

The initial setup was pretty complex and required three people.

We used an in-house team with some consulting help.

We've had a low ROI from Databricks.

I would rate Databricks' pricing seven out of 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.