Databricks Reviews

We use this solution for streaming analytics. We use machine learning functions that output to the API and work directly with the database.

Prior to using Azure Databricks in the cloud, we had Databricks installed in clusters. Since our implementation, the performance has increased and our cost has been reduced.

Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great.

This solution has very good machine learning libraries built-in.

The support for big data is good.

Databricks should have more libraries for predictive analysis and machine learning.

It should have more compatible and more advanced visualization and machine learning libraries. As it is now, I have to try a customer algorithm in order for things to be compatible.

I would like to see more deep learning analytics.

I have been using Databricks for about one year.

This is a cluster-based solution, so it is stable.

We started using Databricks with a small PoC application, and then we developed it into a larger one. It's scalable, and it's a simple process to scale.

We have eight people in our team who are using this solution. We do not plan to increase usage at this time.

I did not contact technical support myself, but when one of our team members contacted them they were given good answers. I would say that the support is good.

It is not difficult to deploy this solution because it is well documented. We followed the normal steps that included all of the APIs.

I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly.

Databricks has been good and I like it. However, it would be improved with the enhancement of the machine learning libraries, and with the inclusion of visualization libraries.

I would rate this solution an eight out of ten.

Databricks can be used for large-scale data pre-processing and data transformations.

The solution is easy to use and has a quick start-up time due to being on the cloud.

The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration. There is a lot of code from places, such as GitHub, but it is not tailored for Databricks. It requires a lot of effort to bring the code to a level where it can be used with Databricks capabilities.

I have been using Databricks for two months.

The solution is stable.

Databricks is scalable.

We did not have a need to use technical support.

The installation is straightforward, and it took approximately one hour.

We did the implementation and maintenance of the solution ourselves using approximately three engineers.

The solution requires a subscription.

We are evaluating other solutions.

I would recommend this solution for those wanting to process large data sets, but if it is to be used for smaller data sets, I would not recommend it.

I rate Databricks a five out of ten.

We use this solution to process data, for example, data validation.

The main features of the solution are efficiency.

We were trying to process 300 million records over 10 years. If you are processing that high number of records through the ADF pipeline with, for example, Azure, it took approximately six hours. In order to reduce the burden on our ADF pipeline, we wrote a simple code in this solution where we can read and write to the file into the temporary Storage Explorer. By going through this solution, we were able to complete the processing of the data in half an hour.

The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view.

The ability to migrate from one environment to another is useful.

There should be better integration with other platforms.

I have been using this solution for two years.

I have approximately 20 users using this solution in my organization. We have plans to increase our usage in the future.

There is no installation required. It is easy to use, for example, in Azure it is available, you subscribe, and use it.

The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable.

I rate Databricks a nine out of ten.

We used Databricks in AWS on top of s3 buckets as data lake. The primary use case was providing consistent, ACID compliant data sets with full history and time series, that could be used for analytics.

Databricks (delta lake) and the underlying files storage (data lake) is in the centre of the organisation's enterprise data hub. Most of our data is structured (csv files), have some semi-structured (json files) but we are beginning to ingest unstructured (pdf files) and use Natural Language Processing (Textract) to obtain insights driven by key words.  

The Databricks notebooks with SQL and Python provide good intuitive development environment. The Delta Lake, the reading of underlying file storage, the delta tables mounted on top of data lake (AWS in our case) are providing full ACID compliance, good connectivity and interoperability.  

The initial setup is fairly straightforward. The stability is good.

The product is quite ambitious. It's trying to become a centralized platform for all data ingestion, transformation, and analytics needs. It may encounter a stiff competition from best of breed solutions powered by open source software. 

Overall it's a good product, however, it might get challenged over time with with individual best-of-breed products. 

For example in the area of Data Science, RStudio seems to be the industry standard at the moment. RStudio IDE is richer, there are a more out of the box functionalities like a push-button publishing, etc. It's more difficult to run R within Databricks. Especially when it comes to synchronizing the R packages, it legs behind. It's not even supporting the latest version of R 1.3. I believe eventually all analytics will converge into data science. The analytics of the future will be data science, because predicting the future will be one of the most prevalent use cases. The stuff we used to do before, slicing and dicing, drilling through, trend analysis, etc. will become redundant operations after the analytics toolsets become powered by AI/ML and fully automated. Unless the organisations acquire these platforms that can cater for machine learning and artificial intelligence, including natural language processing they will have a hard time surviving.

With Databricks I would like to see more integration with and accommodation of  open-source products. This could be controversial, as it could question the whole configuration and the purpose of the product. I'm pretty sure Microsoft is trying to position it in a monopoly market as they did with Windows and MS Office so that they could charge the premium. We are beginning to see the similar product strategy behind Databricks. 

I've been working with Databricks for about two years. 

From what I know and from what I've heard, talking to our data operations team, it is stable and it's quite powerful. 

Obviously running on top of Spark, ensures fast processing and elasticity for coping with big data volumes, up to 2 petabytes. You can spin up the cluster very quickly, and shut it down. It's elastic.

Excellent customer service from Databricks. Very proactive, constantly attuned to customer needs, even connected us with other customers for knowledge exchange. 

I am an IT Consultant and in the past have used different solutions for ETL on top of databases, particularly if we are talking about data warehousing. However, in the last 6 years I have seen large client using a mixture of open source and proprietory technologies, like Informatica stack with data lake in AWS, or Kafka Confluence with MQ Series on top of mainframes and data lake in AWS, Databricks and Azure data lake, etc.

It was pretty easy to set up. At least, that is my understanding. I'm not the data engineer though. I don't actually do installs and configurations. I explore features and build them in my architecture designs.

We implemented Databricks through vendor, and the vendor was pretty good. 

Don't really know.

I can't speak on pricing of the solution. It's not an aspect of the solution I deal with directly.

The options were Talend, EMC Isilon, native AWS services, and others.

In the current capacity as and Architect and the end user of Databricks I would say I do have confidence that Databricks can provide a wealth of functionalities to start with. 

My advice to future adopters of Databricks would be to be careful about the overall architectural roadmap for this application, adopt a flexible, modular, microservices like architecture whose components could be replaced in the future should they deem inadequate to cater for evolving business needs. 

I am a developer and I do a lot of consulting using Databricks.

We have been primarily using this solution for ETL purposes. We also do some migration of on-premises data to the cloud.

The most valuable feature is the ability to switch loads between multiple clusters.

Automation with Databricks is very easy when using the API.

The ability to write code and SQL in the same interface is useful.

It is easy to connect notebooks to a cluster.

There are a large number of inbuilt functions that help to make things easier.

Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems. As it is now, we have to go into the driver logs to identify the error messages properly. 

There is not much information about Databricks available online, such as cost. Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful.

I would like to see integration with Power BI or Tableau for the business users. They may use Databricks to check on things, but it will be a little bit complicated for them. The GUI interfaces for Tableau and Power BI are ones that they are used to, so the integration would help.

I have been using Databricks for about five and a half years.

We have found that in the development environment, Databricks is pretty stable. We have had problems where something works in development but does not work in production, and this can happen when the version is updated and certain features have been deprecated. This means that more testing is required before moving to production, but this is the only drawback that we have seen.

Basically, when we move across version we have found issues, but otherwise, it's pretty stable.

Scalability is one of the main features of Databricks. We have used datasets that are one hundred megabytes in size up to one terabyte, and we can manage, so it's easily scalable.

We have a large company with between 400 and 500 people using this solution.

We have not reached out for technical support on Databricks.

I found the initial setup easy because I had previously worked on Spark.

If somebody goes through the training, which is available on the website, then it should be straightforward. I don't think that it is very hard.

When it comes to developing things based on use cases, it can take between three days and two weeks, plus two to three days for testing and deploying it. I would say that for an entire use case, it will take a maximum of three weeks.

My advice for developers who are interested in working with this solution is to first go through the Spark architecture.

I would rate this solution a nine out of ten.

We specialize in project consulting for our clients. Whenever we get the opportunity, we recommend Databricks to them.

The fast data loading process and data storage capabilities are great.

Based on the data loads and the performance, you can easily scale up the clusters.

Sometimes we experience issues connecting our database to Databricks. There are no direct connectors — they are very limited. This should be addressed and corrected in the next release.  

Reading past data can also be tricky as there is no data spectrum like you would find with Snowflake and other solutions. 

We have been using Databricks for one and a half years.

Both the scalability and the stability of Databricks is good.

Technical support is good but I have not interacted with them directly. We have a point of contact. We used to interact with tech support on a regular basis and they would respond quickly. We would get a response on the same day based on the priority level. Keep in mind, my company is in a partnership with them which could be a factor in their quick response time.

The initial setup was not very complex. We had it up and running in no time; it's a quick process.

We have just one solution architect and one data architect who handle all maintenance-related issues. 

I would recommend purchasing a package that includes technical support. Compared to other companies, they offer great support to their clients.

On a scale from one to ten, I would give Databricks a rating of eight.

We use Databricks for experimentation. For example, we do ML model building and training that is connecting to our data which resides in Azure. It offers very good integration with Azure. We've deployed some of our model inference tools in Databricks.

 Databricks integrates well with other solutions.

Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage.

Along with having connections to different databases for Git tools, adding libraries for easy access would be a benefit. As data scientists, we connect to different databases and different sources of data, having a library would be useful.

I have been using Databricks for approximately one year.

The solution is stable. We did not face any downtime.

Databricks is scalable. It operates three times faster than any of the other ecosystems which we have experimented on.

We have approximately five data scientists using this solution in my organization. We are a small company and as we grow, all our data scientists would be using this platform. We plan to increase usage.

The technical support is good. We didn't need a lot of support. There were a few times we needed some help on how to do certain operations.

The installation was straightforward because it is on the cloud. The full deployment took approximately one week.

We did the implementation of Databricks in-house. It only requires one person for the maintenance of the solution.

My advice to others wanting to implement this solution is to use a cloud environment. For example, we are using Azure with Databricks. It is much better than doing an on-premise implementation.

I rate Databricks an eight out of ten.

We use the product as a data science platform that enables me to handle and analyze large datasets efficiently.

Databricks can switch easily between cloud providers, such as Azure and GCP. It allows seamless integration with various data platforms and cloud providers, facilitating better data handling and analysis.

The product could be improved regarding the delay when switching to higher-performing virtual machines compared to other platforms like Snowflake. The ease and speed of managing clusters can also be enhanced, especially when scaling up resources. They could add more advanced data storage solutions like Iceberg and Delta files.

I have been using Databricks for approximately two years.

I rate the product stability a seven out of ten. 

I rate the product scalability an eight. 

The technical support services are good. 

Positive

The initial setup was straightforward. However, configuring policies could have been simpler.

The product pricing is moderate. 

I evaluated other options, including Snowflake, before choosing Databricks.

Databricks is a robust solution for big data processing, offering flexibility and powerful features. While there are areas for improvement, especially in performance and cluster management, it remains a highly valuable tool in my data science toolkit.

 I rate it a seven.