Databricks Reviews

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.

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.

Our primary use case for the solution is data analysis by providing a Spark cluster environment with a driver to analyze a huge amount of data and gigabytes of data and can create Notebooks in Databricks. We can write SQL commands, Python code, Scala, or Spark with Python. With Databricks, we get a cluster hosted in the public cloud and we adjust it based on how much we use it.

The most valuable features are data engineering and data science because we can create Notebooks on them. We can use any Python library to build data science models, or we can use libraries like Seaborn or Matplotlib to create charts based on data for data analysis. It is a really valuable capability.

Microsoft Azure has its learning environment on the Microsoft website. We can complete certifications, but the Databricks certification is more expensive than Microsoft. It costs between $2,000 and $2,500, and the knowledge is linked. They're also charged based on whether a person doesn't want to analyze large amounts of data. Hence, we want to have the capacity for free student users so that people can learn and build their professional skills.

We have been using the solution for approximately one year.

The solution is stable. Microsoft offers a public service, and we can get it from the Databricks website. Additionally, many companies use it to analyze their data or create a Spark cluster to run Python or SQL scripts based on their data. I rate the stability a nine out of ten.

The setup is quite easy, and Databricks has also partnered with Microsoft, so we get this service on Microsoft Azure.

We have seen a return on investment.

We have a pay-as-you-go subscription and pay for it based on our usage.

We chose this solution because my company uses Microsoft Azure for a project, and my role as a data engineer primarily focuses on data-related services. For storing data, we use Data Lake; similarly, for the data processing engine, we use Spark, which Databricks provides.

I rate the solution an eight out of ten. The solution is good but can be improved by including drag-and-drop features because it can be helpful for users who are unfamiliar with coding. I advise new users to have prior experience with Python or SQL before utilizing this solution if they use it for data science or model building. 

I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast.

I have been using Databricks for three years.

I would rate the stability of this solution a nine out of 10, with one being not stable and 10 being very stable.

I would rate the scalability of this solution an eight out of 10, with one being not scalable and 10 being very scalable.

There are three people using this solution in our organization.

I would rate the available customer service a three. It's worth mentioning that this is Microsoft and not Databricks itself. I haven't spoken to Databricks people directly, but I know the people who have and they have been a lot more pleased.

Negative

I would rate their pricing plan a six (on a scale of one to 10, with one being cheap and 10 being expensive). I think the prices could be lowered a little bit.

Overall, I would rate this solution an eight out of 10, with one being quite poor and 10 being excellent. It is fast, it's scalable, and it does the job it needs to do.

Our company uses the solution for series-based and panel-based migrations. We collect and store user requirements, use apps to fetch data, and provide customers with better data for business reports. There are 30 to 40 users in our company.

The solution is an impressive tool for data migration and integration. 

The run time is very quick.

The solution has some scalability and integration limitations when consolidating legacy systems.

I have been using the solution for two years. 

The solution is stable. 

It is not really scalability but more about the combination of the structure, consolidation, and different formats we can split and merge. We do a lot of things while storing the target operational model. Snowflake is more flexible and scalable in that regard. 

We have contacted technical support a lot about replicating values in PDF files. So far, they have not been able to provide a viable solution. 

The setup is of average difficulty but tougher than Snowflake. 

Deployment is easy and run time is quick. 

We implemented the solution in-house.

One resource manages services for end-to-end monitoring and maintenance activities. 

The solution is based on a licensing model. Updates occur automatically by the task base. 

Snowflake is quite impressive in comparison to the solution because there is flexibility in the way you consolidate. In contrast, the solution has some scalability and integration limitations when consolidating legacy systems. Tool wise, Snowflake is easy from the technical perspective because connectors are included.  

We are evaluating options for one particular use case. The customer wants to replicate values from PDFs and enter them in the data model. We contacted the solution's technical support but do not yet have a viable answer. There are gaps in what we do and how we capture. The only option right now is for the customer to manually upload values that we integrate using Synapse to consolidate report data. We haven't yet found another tool that maps to meet our customer's requirement. 

I rate the solution a seven out of ten. 

We use this solution for the Customer Data Platform(CDP). My company works in the MarTech space and usually we implement custom CDP.

The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks. It is the most important element of the solution. Databricks also offers exceptional performance and scalability. 

The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau.

In a future release, we would like to have a better ETL designer tool to assist in the way we move data from one place to another.

We have been using this solution for four years. 

This is a stable solution. 

This is a scalable solution. 

The initial setup is very easy. It is a managed solution inside Azure so you just need to search for Databricks. There are a couple of pages to follow in the setup wizard and Databricks is up and running.

We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data. 

When we first started using Databricks in 2018, there were not many comarable solutions to consider. Right now there are many solutions to consider including Snowflake, Azure Synapse, Redshift and BigQuery.

Databricks continues to be our solution of choice but Snowflake does have a better user interface and is easier to work with the data pipelines and with the overall UI.

I would advise others to first define a strong data strategy and then choose which data platform suits your needs. 

I would rate this solution a nine out of ten. 

Our primary use case for this solution is research for data scientists. The solution is deployed on cloud.

It has allowed our data engineers, data scientists, and analysts to collaborate and work on the same platform. 

We have the ability to scale, collaborate and do machine learning.

The product cannot be integrated with a popular coding IDE.

We have been using this solution for approximately three years.

The solution is stable.

The solution is scalable. There are five people using it in our organization.

I rate my experience with customer service and support an eight out of ten.

We previously used H2O.

The initial setup was straightforward.

Implementation was done in-house.

We have seen a return on investments.

Licensing costs are charged on a yearly basis and costs between 25,000 and 30,000.

We evaluated other options but this solution was the best fit for what we required.

I rate this solution nine out of ten. The solution is good but can be improved by integrating with a popular coding IDE.

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.

We use Databricks for batch data processing and stream data processing.

Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user.

The flexibility of Databricks is the most valuable feature. It gives us the ability to write analytics code in multiple languages.

There is a single workspace for different data roles like data engineers, machine learning engineers, and the end user, who can connect to the same system. 

Databricks computes separate from storage, so you are not coupled with the underlying data sets, allowing for multiple processes and multiple programs to be written on the same code.

I would like to see improvement with the UI. It is functional and useful, but it's a bit clunky at times. It should be more user-friendly.

In future releases, Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics.

I have been using Databricks for eight months.

Databricks is very stable.

The scalability of this solution is good. In our organization, users include analysts, data engineers, and data scientists.

I would give Databrick service and support a four and a half out of five overall.

Positive

Prior to using Databricks, we used Azure Stream Analytics. We made the switch because of the scalability and integrated platform.

The initial setup of Databricks is more complex. I would rate it a four out of five on the complexity of the setup. It took two days to deploy the solution.

We used a third party for some of the implementations of Databricks. The number of staff required to deploy and maintain this solution depends on the number of processes you have. Due to the cloud nature of the technology, it is easy to deploy and maintain. 

The licensing of Databricks is a tiered licensing regime, so it is flexible. I feel their pricing is a five out of five.

Databricks is a one-stop shop for everything data related, and it can scale with you.

I would rate this solution a 9.5 out of 10 overall.

We use this solution for finding anomalies and applying the rules to the streaming data.

There are around 50 people using this solution in my organization, including data scientists.

The ability to stream data and the windowing feature are valuable. There are a number of targeted integration points, so that is a difference between Stream Analytics and Databricks. The integrations input or output are better in Databricks. It's accessible to use any of the Python or even Java. I can use the third party, deploy it, and use it.

Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing. There should be reliability between these two. Databricks is based on open sources. If it's more synchronous between the Microsoft technology and the programming languages, it'll be better. Python has better languages, but compatibility would be a great help.

I would like to have better support for Microsoft technology and better language components.

With Azure or Cosmo DB, I can store other data links or time series data tables. That would be a great help for analytics in real time.

I have been using Databricks for eight months.

The scalability is fine. We had thousands of devices and were sending data infrequently, so that worked for us. If the amount increases, the windowing function and job schedule may not perform as expected.

I would rate technical support 4 out of 5. We had some issues with setup, and they were finally solved but it was after following up a few times.

Azure Stream Analytics is easy to use and easy to deploy. It's a little bit better. Databricks is still having some stability issues. Azure Stream Analytics has a few input and output sources, and it's scalable to all types of third party or interfaces.

Setup was complex. There were some issues with setting up a database and installing the third party component on top of services. I would rate the setup 3 out of 5.

Implementation was done in-house.

The cost is around $600,000 for 50 users.

I would rate the price 2 out of 5.

I would rate this solution 8 out of 10.