We previously used Azure's data lake product and possibly some Hortonworks. 

I previously used Cloud-Bricks. 

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.

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.

We previously used H2O.

We did not use another similar solution prior to Databricks.

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.

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

Prior to Databricks, we initially used Hadoop. Afterwards, we used HANA, SAP HANA, and the Microsoft SQL Server.

We work with multiple clients and this solution is just one of the examples of products we work with. We use several others as well, depending on the client.

It's all wrappers between the same underlying systems. For example, Spark. It's all open-source. We've worked with them as well as the wrappers around it, whether the company was labeled Databrary, IBM insights, Cloudera, etc. These wrappers are all on the same open-source system.

If we with Azure data, we take over Databricks. Otherwise, we have to create a VM separately. Those things are not needed because Azure is already providing those things for us.

I have previously worked with Apache Hadoop, and Databricks is definitely a better product. It's much easier to get data quickly in Databricks. As a result, a lot of the drudgery is taken away. Whereas with Hadoop, it's a bit more tricky to get data together.

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.

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.

We used Cloudera before switching to Databricks.

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.

We have used a lot of different solutions, such as Watson and DataIQ.

I work with  Databricks, Cloudera and Snowflake.

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

I have not used a similar solution to Databricks.

As we are talking about a corporate solution, the deployment of Databricks lasted longer than the one day it took for Alteryx. 

We used Alteryx prior to Databricks and continue to do so, it being the only other solution we have employed. We use the two with different software. 

Previously I used Hive and Livy in Zeppelin on an in-house Hadoop installation. The queries constantly threw exceptions and timeouts and the necessary configuration changes proved time-consuming and problematic. Databricks, on the other hand, simply made all those problems vanish. 

I have not worked with another solution prior to Databricks.

I have not used tools that are similar to Databricks for workload management, but Azure ADFv2, Google BigQuery, SAS are some the most powerful tools in this space, that I have used in the past. I have also heard of Dataiku and other tools but I have not used them. The only things that I have used are tools written in Python or scripting languages.

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.

I have used Snowflake and one of the differences is that Snowflake is much easier to deploy.

I have used different Microsoft solutions before.

We previously used the earlier version of Azure Machine Learning services and we decided to move over because over time it became more difficult to deploy. That was two years ago, but now with the new version, it's much easier to deploy Machine Learning.

We previously used Microsoft stacks. We chose Databricks because the processing power was better and it was a better fit for our use case.

Before using Databricks, we were running our own cluster with a web server that executed our Python queries.

We didn't previously use a different solution, however, we built our own from scratch. This is the first unified platform that we've used.