We heavily use Cloudera Data Platform for data science activities. Various departments in the company utilize it as a sandbox for data discovery. We have multiple data pipelines running on a daily and hourly basis, along with some real-time data pipelines.

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The primary usage of Cloudera Data Platform is to offload ETL processes because it's cheaper compared to data warehouse solutions like Teradata or Oracle. Furthermore, basic reporting can be done, and some real-time processes can be managed. View full review »

We use it for multiple domains, including oil & gas, finance (Morgan Stanley), and healthcare. We process around 186 TB of data per day for analytics purposes.

Currently, we use it for healthcare domain. 

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We use Hortonworks Data Platform for data management, significant data ingestion, and analytics.

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There are a lot of use cases for the Hortonworks Data Platform. We use it alongside GPFS, so most of the information we use for operational analytics is primarily on the Hortonworks Data Platform.

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We primarily use the solution for data storage and processing.

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We use Hortonworks as a storage platform and then we create machine learning models and do the execution using Cloudera Data Science Workbench. (Cloudera and Hortonworks merged in January of 2019.)  

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Hortonworks actually provides a complete solution and just one user interface that can manage all the packages. It can monitor all the requirements, all the versions and additionally all the quays and all the hardware-dependent services. What I want is a useful user interface which is the reason why I currently prefer to use Hortonworks.

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We use this solution for the hospitality industry. 

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We use this solution to look at and manage big data. It's mostly historical data that we offload from our data warehouse, as well as from other databases in other platforms.

We have two different installations. The first one is based on IBM POWER CPUs, and the other one is based on Intel CPUs. Our data center is on-premise. There is some thought on moving to a private could, or a private IBM cloud, but we have not proceeded with that as of yet.

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We use it for data science activities.

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