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.

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.

My company uses Databricks to process real-time and batch data with its streaming analytics part. We use Databricks' Unified Data Analytics Platform, for which we have Azure as a solution to bring the unified architecture on top of that to handle the streaming load for our platform.

The most valuable feature of the solution stems from the fact that it is quite fast, especially regarding features like its computation and atomicity parts of reading data on any solution. We have a storage account, and we can read the data on the go and use that since we now have the unity catalog in Databricks, which is quite good for giving you an insight into the metadata of the data you're going to process. There are a lot of things that are quite nice with Databricks.

Scalability is an area with certain shortcomings. The solution's scalability needs improvement.

I have been using Databricks for a few years. I use the solution's latest version. Though currently my company is a user of the solution, we are planning to enter into a partnership with Databricks.

It is a stable solution. Stability-wise, I rate the solution an eight to nine out of ten.

It is a scalable solution. Scalability-wise, I rate the solution an eight to nine out of ten.

My company has a team of 50 to 60 people who use the solution.

Sometimes, my company does need support from the technical team of Databricks. The technical team of Databricks has been good and helpful. I rate the technical support an eight out of ten.

The initial setup phase of Databricks was good. You can spin up clusters and integrate those with DevOps as well. Databricks it's quite nice owing to its user-friendly UI, DPP, and workspaces.

The solution is deployed on the cloud.

The time taken for the deployment depends on the workload.

I cannot judge whether the product is expensive or cheap since I am unaware of the prices of the other products, which are competitors of Databricks. The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts.

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.

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're using it to provide a unified development experience for all our data experts, including all data engineers, data scientists, and IT engineers. With the Databrick Platform we allows teams to collaborate easily towards building Data Science models for our clients. The development environment allows us to ingest data from various data sources, scale the data processing and expose them either trough API or through enriched datasets made available to web app or dashboard leveraging the serverless capacities of SQL warehouse endpoints.

Databricks allowed us to offer an homogeneous development environment accross different accounts and domains, and also across different clouds. The upskilling of our employees is far more linear and faster, while removing the complexity of infrastructure management. This lead to an increased collaboration between domain thanks to a better onboarding experience, more performant pipelines and a smoother industrialization process. Overall client satisfaction has increased and the time to first insight has been reduced.

The shared experience of collaborative notebooks is probably the most useful aspect since, as an expert, it allows me to help my juniors debug their books and their code live. I can do some live coding with them or help them find the errors very efficiently.

It has become very simple to set up thanks to its official Terraform provider and the open-source modules made available on GitHub.

I love Databricks due to the fact that we can now deploy it in 15 minutes and it's ready to use. That's very nice since we often help our clients in deploying their first Data Platform with Databricks.

The solution is stable, with LTS Runtimes that have proven to remain stable over the years.

I would love to be able to declare my workflows as-code, in an Airflow-like way. This would help creating more robust ingestion python modules we can test, share and update within the company.

We would also love to have access to cluster metrics in a programmatic way, so that we can analyse hardware logs and identify potential bottlenecks to optimize.

Lastly, the latest VS Code extension has proven to be useful and appreciated by the community, as it allows to develop locally and benefits from traditional software best-practices tools like pre-commits for example.

I've been using the solution for more than four years now, in the context of PoC to full end-to-end Data Platform deployment.

The product is very stable. I've been using it for three years now, and I have projects that have been running for three years without any big issues.

It's very scalable. I have a project that started as a proof of concept on connected cars. We had 100 cars to track at first - just for the proof of concept. Now we have millions of cars that are being tracked. It scales very well. We have terabytes of data every day and it doesn't even flinch.

I've had very good experiences with technical support where they answer me in a couple of hours. Sometimes it takes a bit longer. It's usually a matter of days, so it's very good overall.

Even if it took a bit of time, I got my answer. They never left me without an answer or a solution.

The implementation is very simple to set up. That's why we choose it over many other tools. Its Terraform provider is our way-to-go for the initial setup has we are reusing templates to get a functional workspace in minutes.

Usually, we have two to five data engineers handling the maintenance and running of our solutions.

We deploy it in-house.

The solution is a bit expensive. That said, it's worth it. I see it as an Apple product. For example, the iPhone is very expensive, yet you get what you pay for.

The cost depends on the size of your data. If you have lots of data, it's going to be more expensive since your paper compute units will be more. My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month.

We looked into Azure Synapse as an alternative, as well as Azure ML and Vertex on GCP. Vertex AI would be the main alternative.

Some people consider Snowflake a competitor; however, we can't deploy Snowflake ourselves just like we deploy Databricks ourselves. We use that as an advantage when we sell Databricks to our clients. We say, "If you go with us, we are going to deploy Databricks in your environment in 15 minutes," and they really like it.

Lately Fabric was released and can offer quite a similar product as Databricks. Yet, the user experience, the CI/CD capabilities and the frequent release cycle of Databricks remains a strong advantage.

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.