Cloudera Distribution for Hadoop and Cloudera Data Platform compete in the data management and analytics category. CDP seems to have the upper hand due to its advanced processing capabilities and hybrid cloud support.
Features: CDH integrates well within the Hadoop ecosystem, offering tools like Hive, Impala, and Cloudera Manager. Sentry and LDAP support are also pivotal security features. CDP offers cutting-edge data engineering tools such as Apache Spark and harnesses Ranger for security. Its hybrid cloud abilities and AI support are distinct advantages.
Room for Improvement: CDH users face stability issues with HBase and challenges in processing speed. Integration complexities and expensive node-based licensing are concerns. CDP would benefit from better documentation, improved integration with existing tech like H2O, and a more appealing pricing strategy. Enhancements in cloud capabilities and security features are anticipated.
Ease of Deployment and Customer Service: CDH is predominantly chosen for on-premises deployment with strong management tools and a positive Cloudera Manager experience. Customer service perception varies; some users praise it while others see inconsistency. CDP offers robust hybrid cloud support and reliable customer service but could improve responsiveness and technical support for complex configurations.
Pricing and ROI: CDH's premium node-based licensing can be steep, aligning with its extensive features for large operations. CDP's pricing is more complex, potentially higher than CDH, but promises substantial ROI through scalability and flexibility. Both products deliver notable returns when utilized effectively within the right contexts and organizational scales.
A specific example of the positive impact of Cloudera Data Platform is the clearly saved time and improved performance, which is the main result of it.
In terms of return on investment, I see great changes in operational effectiveness measured by RTO when comparing on-premises solutions with cloud solutions.
Having a common chat channel between firms and service providers would make communication faster and more efficient.
Cloudera support is timely and responsive, adhering to the SLAs they provide.
I have communicated with technical support, and they are responsive and helpful.
The technical support is quite good and better than IBM.
CDP allows for easy, mostly automated scalability where I can schedule job workflows, fine-tune system resource metrics, and add nodes with just a click.
Integration with other tools works well for us and we successfully scaled the solution after two to three years without any issues.
For scalability, I rate Cloudera Data Platform at an eight out of ten as it is an on-premise solution.
Sometimes a node goes down, but it automatically returns to a healthy state.
Cloudera Data Platform is stable functionality-wise, but it needs some bug fixes for security.
We faced challenges but overcame those challenges successfully.
We aim to address these issues with a Kubernetes-based platform that will simplify the task of upgrading services.
Cloudera Data Platform should include additional capabilities and features similar to those offered by other data management solutions like Azure and Databricks.
Databricks, which are more flexible and in tune with current trends in AI and machine learning.
Integrating with Active Directory, managing security, and configuration are the main concerns.
Initially, CDH had a straightforward pricing model based on nodes, but CDP includes factors like processors, cores, terabytes, and drives, making it difficult to calculate costs.
We find Cloudera Data Platform to be cost-effective.
It can be deployed on-premises, unlike competitors' cloud-only solutions.
What stands out the most in Cloudera Manager are SDX, which provide centralized control for governance, security, and data lineage across multiple sources.
Cloudera Data Platform has positively impacted our organization by reducing overall manual intervention, requiring fewer efforts and resources to build a big data cluster compared to traditional methods.
The Ranger integration makes it more flexible and reliable for me by allowing control over data access, specifying who can access at what level, such as table level, masking, or data layer level.
This is the only solution that is possible to install on-premise.
| Product | Market Share (%) |
|---|---|
| Cloudera Data Platform | 6.2% |
| Palantir Foundry | 25.2% |
| Informatica Intelligent Data Management Cloud (IDMC) | 14.1% |
| Other | 54.5% |
| Product | Market Share (%) |
|---|---|
| Cloudera Distribution for Hadoop | 21.9% |
| Apache Spark | 19.0% |
| HPE Ezmeral Data Fabric | 14.4% |
| Other | 44.7% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 5 |
| Large Enterprise | 22 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 9 |
| Large Enterprise | 31 |
Cloudera Data Platform offers a powerful fusion of Hadoop technology and user-centric tools, enabling seamless scalability and open-source flexibility. It supports large-scale data operations with tools like Ranger and Cloudera Data Science Workbench, offering efficient cluster management and containerization capabilities.
Designed to support extensive data needs, Cloudera Data Platform encompasses a comprehensive Hadoop stack, which includes HDFS, Hive, and Spark. Its integration with Ambari provides user-friendliness in management and configuration. Despite its strengths in scalability and security, Cloudera Data Platform requires enhancements in multi-tenant implementation, governance, and UI, while attribute-level encryption and better HDFS namenode support are also needed. Stability, especially regarding the Hue UI, financial costs, and disaster recovery are notable challenges. Additionally, integration with cloud storage and deployment methods could be more intuitive to enhance user experience, along with more effective support and community engagement.
What are the key features?Cloudera Data Platform is implemented extensively across industries like hospitality for data science activities, including managing historical data. Its adaptability extends to operational analytics for sectors like oil & gas, finance, and healthcare, often enhanced by Hortonworks Data Platform for data ingestion and analytics tasks.
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