A hybrid data platform approach that enables organizations to manage, govern, and analyze data across on-premises systems, public clouds, and edge environments in a more unified and consistent way. Its main strength is the unified platform for data engineering, analytics, and AI.

The support team we get with Cloudera is amazing. I mean, they’re great people, and honestly they helped us out a bunch when we were knee-deep in trouble, so I really appreciate the support team.

What I value most is having access to a stable, up-to-date big data stack that I can run on premises. I also like the flexibility to deploy the platform on premises, in a public cloud, or in a hybrid setup—whatever best fits the environment. Cloudera data services, in particular, feels like a game changer. It enables users to quickly deploy warehouses, run data engineering jobs, build end-to-end machine learning pipelines, deploy ML models as API/web applications, and roll out LLMs as inference services for enterprise AI. On top of that, Cloudera support has been very helpful and generally responds quickly. Cloudera is also easy to integrate with our customers existing stack, whether BI tools like Qlik and Tableau, or ETL like Talend and IBM DataStage. It's easy to setup connections from Hive/Impala to Talend for ETL purpose, and then connect them also to Qlik for dashboards.