Databricks Reviews

Our primary use case of this product is for our customers who are running large systems and looking for an API -- a quick, easy integration with their own system. We use Databricks to create a secure API interface. I'm vice president of data science and we are customers of Databricks. 

Databricks is quite easy to use and requires less coding and customizations than a solution like AWS SageMaker which I'd previously used on a lot of projects. Databricks enables more people to efficiently build and host their ML code. Another great aspect is that MLflow is already integrated with Databricks which makes a big difference. It enables us to track and monitor all our different experiments. We have mostly used the MLflow part and generic notebooks with the ML building machine learning model, as well as using Pytorch for some of our medical imaging. We were able to quickly deploy both these features without requiring anything extra. 

I'm struggling a little because I wanted to do some POC solutions. I present a lot of projects in various forums and seminars and there aren't a lot of credits and trial options with Databricks. Even if we want to explore, we're not able to and that's a challenge. The solution is quite expensive.

I've been using this solution for a year. 

It's currently stable although we have not yet tested it with a huge volume of data. We'll focus on the performance and model serving capability in the near future. We're still carrying out performance testing, developing the models and figuring out the infrastructure.

Scalability is quite good because we just used 128 GB of resources. It's quite easy to scale.

It was relatively simple, we didn't face any challenges. Deployment takes around two days. 

We did a PSU in Azure ML Studio which is quite a good solution, easy to deploy and use. It's almost a no-code platform. We've also found Azure ML Studio to be quite cost-effective.

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.

We build data solutions for the banking industry. Previously, we worked with AWS, but now we are on Azure. My role is to assess the current legacy applications and provide cloud alternatives based on the customers' requirements and expectations.

Databricks is a unified platform that provides features like streaming and batch processing. All the data scientists, analysts, and engineers can collaborate on a single platform. It has all the features, you need, so you don't need to go for any other tool. 

I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well.

The Unity Catalog provides you with the data links and material capabilities. These are some of the unique features that fulfill all the requirements of the banking domain.

Every tool has room for improvement. Normally what happens, a solution will claim it can do ETL and everything else, but you encounter some limitations when you actually start. Then you keep on interacting with the vendor, and they continue to upgrade it. For example, we haven't fully implemented Databricks Unity Catalog, a newly introduced feature. We need to check how it works and then accordingly, there can be improvements in that also.

Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity.

I have been using Databricks for a year.

Databricks relies on scalability and performance. Every cloud vendor prioritizes scalability, high availability, performance, and security. These are the most important reasons to move to the cloud.

Deploying Databricks on the cloud is straightforward. It's not like an on-premise solution, where you must create a cluster and all those other prerequisites for big data. 

I don't think it's challenging to maintain, but you need an expert programmer because Databricks isn't GUI-based. With GUI-based tools, building ETLs is drag-and-drop. Databricks entirely relies on coding, so you need skilled programmers to building your code, ETLs, etc. 

The price of Databricks is based on the computing volume. You also need to pay storage costs for the cloud where you're hosting Databricks, whether it is AWS, Azure, or Google. 

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

I like the simplicity and ease of use. 

You can deploy the solution to many clouds easily. 

The initial setup is straightforward.

The solution offers a free community version.

The auto models can be improved. 

We can create auto models like Microsoft Azure Machine Learning. In Azure Machine Learning, they have these features, for example, for auto models or code, or by code. They need this in Databricks. 

We need more connectors between on-premises and the cloud. 

We'd like a more visual dashboard for analysis It needs better UI. 

I've used the solution for one and a half months. 

The solution is very stable. There are no bugs or glitches. It doesn't crash or freeze. 

Scalability is no problem. At the beginning, we created a cluster, for example, and if we need more performance in the future, for example, or to accelerate the training, we can change the cluster. It's quite straightforward. 

We have five people using the solution. 

In one or two years, we'd like to promote the solution to clients and increase usage. Right now, the way it is used is limited. I know that some banks and aeronautics companies use it.

In terms of technical support, for now, we use the community. 

We are also aware of KNIME, Azure Machine Learning, and Anaconda. In Anaconda, we use many frameworks, for example.

We started with other platforms, like Azure Machine Learning due to the fact that, with AutoML, it's easy to use. However, now that we have more skills, we need other tools or platforms like Databricks. It's a good platform to deploy and develop machine learning in employees.

The implementation is quite easy. It's not complex or difficult. The first time, I did it using a tutorial which was quite helpful. Later, I took a course. I know it quite well. 

The deployment only takes a few days. 

You only need to deploy or maintain the solution. 

We did not need any outside assistance in terms of setting up the solution. 

For us, this product is free. We use the community version.

I am interested in using the enterprise version, however. Whether we use it or not depends on the projects and customers we get.

I work with a solution provider. We are a Databrick customer.

We are not partners of Databricks. Only we are partnered with Microsoft Azure and Amazon AWS.

We are using the latest version of the solution. However, I do not know the exact version number. 

I still need time with the solution before providing advice to others. I need to prepare the capacity internally. So far, it's been great.

I'd rate the solution eight out of ten. 

We use Databricks to define tool data and have many use cases to analyze and distribute the data.

Data is open to everyone; they can access it through many channels, including notebooks or SQL. That on its own democratizes the data.

I like cloud scalability and data access for any type of user.

It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow.

In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them.

I have been using Databricks for roughly one and a half years.

Stability is excellent.

Databricks is scalable. You can use the power of the cloud to scale your cluster size, either CPU or memory. The data doesn't work like a standard database, so you don't have it based on files, and you don't copy the data. It's super scalable. It's only the computing that you have to scale with the data.

We probably have 40 users with roles like developers, business analysts, and data scientists. We have big plans to increase the usage and have more departments using it.

Technical support has helped us.

On a scale from one to ten, I would give technical support a five.

Positive

We used Cloudera before switching to Databricks.

The initial setup was fairly okay. It takes about two minutes to deploy this solution. It's all code, so we click a button, and then it's done.

On a scale from one to five, I would give the initial setup a four.

We set up and deployed this solution.

On a scale from one to five, I would give our ROI a three.

We only pay for the Azure compute behind the solution. If you want to compute, you have to have a database layer and Azure below.

On a scale from one to five, I would give their pricing a two.

We looked at other options such as Snowflake and Cloudera on the cloud,

I would tell potential users that they need proper cloud engineers and a 
cloud infrastructure team to use this solution.

On a scale from one to ten, I would give Databricks a nine.

We use Databricks for data science work in projects that create data pipelines, pre-processing, data wrangling, big data cluster management and ML, machine learning and deep learning tasks.

Databricks collaborates very well with the Azure platform, Dataiku, and enterprise AI tool. Databricks is a new connection to pull the data or connect to the Spark cluster. It is helpful for us to instance it or distribute the load through the Spark cluster, and it is very user-friendly.

The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark.

Databricks as a solution is integrated with Azure, but Google Cloud has some restrictions. I'm not sure about AWS Cloud, but it would be great if Databricks could integrate all the cloud platforms. Regarding additional features, we would like to see them mostly on the data engineering side, where we have a Spark cluster and some inbuilt ML. In addition, pre-processing steps will be useful.

We have been using this solution for two years and are using the latest update.

It is a stable solution as long as the Microsoft Azure Platform is stable too.

It is a scalable solution, both vertically and horizontally, which is good. My organization is big, and we have a lot of users. In my department, we have about 15 people using Databricks.

We have not escalated any issues to technical support, but we initially struggled with configuration and the settings of Hive metastore, but we resolved it. I rate the technical support a nine out of ten.

Positive

We were using the looped EMR elastic MapReduce from AWS before using Databricks. We switched to Databricks because the whole platform changed from AWS to Azure platform, and Databricks comes as a package.

The initial setup was easy to complete and not complex. It may initially be challenging for a new user, but it improves over time. The CICD pipeline works well with the Microsoft Azure platform because the continuous integration, development and deployment come with the Git integration. It makes it easier for Databricks and the CICD. The deployment should be improved from the perspective of auto ML functionality, so it doesn't have intensive automation learning capability.

We don't use Databricks directly because we work on a data science project. It requires an auto ML and inbuilt machine learning capability. We found capabilities like the large language model using NLP and other deep learning models that are not that intensive. It is meant for data engineering purposes rather than data science purposes. It'll be great if Databricks could be intensive for data science.

We used a third-party, Dataiku platform for the deployment, where we connected to Databricks and completed the ML ops. We required about three people for deployment, and it is easy to maintain the solution.

We have seen an ROI but cannot differentiate because it also comes with the Azure platform.

I do not have details about the pricing.

I rate this solution a nine out of ten. Regarding advice, Databricks is a very good platform, popular and easy to use daily for data engineers and data scientists who rely on a large dataset to do advanced analytics reporting. It's a very good tool.

We are using Databricks for machine learning workloads specifically.

Databricks aligns well with our skillset and overall approach. We sought out their solution specifically for a big data application we are currently working on, as we needed a platform capable of handling large amounts of data and building models. Additionally, the fact that they use open-source software and can integrate data warehouse and data lake systems was particularly appealing, as we have encountered such issues in the past. We determined that Databricks would be an effective solution for our needs.

The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production. 

The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team.

The most important feature other than the Jupyter interface would be to have the RStudio interface inside Databricks. This would be perfect.

We have been using Databricks for approximately one year.

The stability of Databricks is good.

I rate the stability of Databricks a nine out of ten.

Databricks is scalable.

I rate the scalability of Databricks a nine out of ten.

I have been receiving responsive answers from Databricks's support. I have been pleased with the support.

I rate the support from Databricks a ten out of ten.

Positive

The initial setup of Databricks is simple. I did not experience any challenges. The time it takes for the deployment is approximately four hours.

I rate the initial setup of Databricks.

We did the deployment of the solution in-house. There were three people involved in the deployment. A data engineer, data analyst, and machine learning engineer.

We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective.

We only pay standard fees for the solution. 

We use a data engineer, data analyst, and machine learning engineer for the maintenance of the solution.

I rate Databricks a nine out of ten.

Our team is currently utilizing machine learning for various applications, and a few members are also exploring Databrick's use for ML operations.

In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance.

I have been using Databricks for approximately six months

The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes.

The scalability of Databricks is good as long as you have a data lake, and it's easy to scale.

We have approximately 50 users using this solution in my company.

We have a different team who handles the support. I do not have contact with Databricks support.

I have not used a similar solution to Databricks.

I have seen an ROI using Databricks.

I rate the price of Databricks as eight out of ten.

Having a good understanding of physical security in relation to cybersecurity in an OT (Operational Technology) environment would be beneficial, and utilizing an existing data lake prior to implementing a Databricks initiative would greatly aid in its success.

I rate Databricks an eight out of ten.

I am using Databricks in my company.

The most valuable feature of Databricks is the notebook, data factory, and ease of use.

I have been using Databricks for approximately nine months.

The performance and stability of Databricks are good. It is quick and I have not had problems.

Databricks is highly scalable.

We have 200 people using the solution in my organization.

When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand.

I have not worked with another solution prior to Databricks.

The price of Databricks is reasonable compared to other solutions.

I rate Databricks an eight out of ten.