Cloudera Reviews & Product Details

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. Review collected by and hosted on G2.com.

The UI can feel a bit clunky at times and doesn’t come across as very modern. Also, because it’s geared toward stability, integrating some cutting-edge tools with the Cloudera stack can be quite difficult. On top of that, a few of our customers have mentioned that the pricing feels on the higher side compared with competitors. Review collected by and hosted on G2.com.

I love its open data storage architecture, and the data preparation for AI is impressive. It gathers data from various sources into a single location, and I really appreciate how well it unifies data from multiple sources. It also provides access to source data for AI, and several aspects of the overall approach are particularly compelling. Review collected by and hosted on G2.com.

Optimizing performance can take some expertise, and troubleshooting may require a fair amount of effort. The interface can feel a bit complex at first, and I think the onboarding process would benefit from more guidance to help new users get started. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Cloudera upgrades can sometimes be complex because of dependencies between different services, parcels, Java versions, and configuration changes. Planning and validation are important to avoid impacting production workloads. Review collected by and hosted on G2.com.

I like that Cloudera allows organizations to leverage modern platforms to run legacy workloads, which helps in getting out of SAS licenses, hardware, and maintenance. I enjoy the idea of 'Cloudera anywhere,' where models can utilize Cloudera data anywhere after migrating from SAS. The initial setup was very easy, which is a big plus for me. Review collected by and hosted on G2.com.

I feel like Cloudera is a bit late in the AI and unified data market. Other tech companies had oriented themselves towards these areas a few years earlier than Cloudera did. Review collected by and hosted on G2.com.

I use Cloudera for our central data platform, and it's fast with standard tools that help in building pipelines quicker. I appreciate the quality of the platform and value its advanced tech stack, particularly Iceberg and Spark, which help in retrieving and computing data faster. The initial setup was easy for our team, and the platform's capabilities make it quite recommendable. Review collected by and hosted on G2.com.

I think Cloudera could improve with faster onboarding of new tech stacks. It would help if they did regular research to keep up with advancing technologies. Review collected by and hosted on G2.com.

I used Cloudera AI Studio to build agents, and it provides guidance and direction to make AI agents while confirming the guardrails. I like the full suite of cloud and AI solutions it offers. With the local LLM, we can maintain privacy, which is important. The AI Studio is the main tool I use, and scalability and price were key factors in switching from AWS. The initial setup was easy. Review collected by and hosted on G2.com.

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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. Review collected by and hosted on G2.com.

Cloudera could be complex and expensive, especially for organizations that do not need a full enterprise-scale data platform. Its steeper learning curve, and the need for skilled teams to manage it. Review collected by and hosted on G2.com.

I like the CM manager, which serves as a central management tool. It provides great visibility of the whole cluster and is easy to maintain and troubleshoot. I also appreciated that the initial setup of Cloudera was comprehensively done by Cloudera Professional Services, ensuring everything was correctly set up from the start. Review collected by and hosted on G2.com.

I have some issues with Cloudera's integration with hardware status. Review collected by and hosted on G2.com.

Professional services, quality product, and reliable after-sales support. Review collected by and hosted on G2.com.

Component integration is complicated and comes with a steep learning curve. On top of that, certification for some components tends to get delayed. Review collected by and hosted on G2.com.

Cloudera stands out for several core capabilities, particularly for enterprises managing complex, large-scale data ecosystems.

Hybrid and multi-cloud flexibility: Cloudera’s “write once, run anywhere” philosophy helps organizations manage workloads smoothly across public clouds, private data centers, and the edge.

Enterprise-grade governance and security: With features like Shared Data Experience (SDX), it offers centralized access control, auditing, and end-to-end data lineage across environments.

Open data lakehouse architecture: Built on open standards like Apache Iceberg, it supports analytics and machine learning directly on large-scale data while avoiding vendor lock-in.

Integrated open-source stack: It combines major big data engines—such as Apache Spark, Kafka, Hive, and NiFi—into a single, managed platform. Review collected by and hosted on G2.com.

The AI’s answers sometimes contain mistakes. Review collected by and hosted on G2.com.