Find out in this report how the two AI Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
There are licensing costs that have been saved when we moved some of the data platforms, decommissioned them, and moved on to this platform.
In terms of return on investment, I see great changes in operational effectiveness measured by RTO when comparing on-premises solutions with cloud solutions.
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.
Using Cohesity DataProtect is easier to manage, and it simplifies various components into one architecture, reducing the need for extensive human resources to manage backups.
I would rate the customer support of Cloudera Data Platform ten out of ten.
I have communicated with technical support, and they are responsive and helpful.
Cloudera support is timely and responsive, adhering to the SLAs they provide.
The support can depend on the region, and for larger customers, I advise having a Technical Account Manager for better assistance.
For the support, I can provide a rating of four only because they initially provide some steps, but later say they are not sure, which is a problem in a production environment.
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.
They have the cloud burst feature available where if the on-premises capacity is not sufficient at a point in time, you can run that Spark job on the cloud itself.
The ability to scale processing capacity on demand for batch jobs without impacting other workloads, and support for a growing number of concurrent users and teams accessing the platform simultaneously are significant advantages.
Cohesity DataProtect is built on a scale-out architecture, which means it can effectively scale to meet various needs.
Sometimes the end user is not experienced or does not have all the expertise related to Cloudera specifically, making it very difficult to manage properly
Sometimes a node goes down, but it automatically returns to a healthy state.
Cloudera Data Platform is pretty stable in my experience; there are not any downtime or reliability issues.
On the whole, any problems were more related to hardware limitations rather than issues with Cohesity DataProtect itself.
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.
Cloudera Data Platform can be improved by addressing the feasibility of using it in the cloud; there are some complexities around the components used in cloud by Cloudera Data Platform that are not really convenient.
The container functionality is very limited at the moment, not covering the whole container.
While there are improvements to be made, such as providing support for older systems like IBM iSeries and tandem systems from HP, the solution overall shifts from older methods to modern practices.
There is room to improve the user interface of Cohesity DataProtect for more intuitive navigation.
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.
So far, I would say that it is competitive pricing that we have received.
I find Cohesity DataProtect to be expensive.
By using the Hadoop File System for distributed storage, we have 1.5 petabytes of physical storage with 500 terabytes of effective storage due to a replication factor of three.
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.
What stands out the most in Cloudera Manager are SDX, which provide centralized control for governance, security, and data lineage across multiple sources.
The platform is based on a scale-out architecture with each node having compute, RAM, SSD, and HDD.
Global deduplication ensures that only unique data blocks are stored, significantly reducing storage consumption.
The option to maintain evidence in Europe for regulatory compliance, the ability to maintain the backup with the same technology and same control plane, along with the same solutions to use backup solutions such as S3 or similar services in AWS, is what we are working with.
| Product | Mindshare (%) |
|---|---|
| Cloudera Data Platform | 0.5% |
| Cohesity DataProtect | 0.4% |
| Other | 99.1% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 7 |
| Large Enterprise | 26 |
| Company Size | Count |
|---|---|
| Small Business | 21 |
| Midsize Enterprise | 22 |
| Large Enterprise | 43 |
Cloudera Data Platform provides efficient data management through features like Hue, Spark, and Impala. It integrates open-source solutions, supports hybrid environments, and enhances data governance while prioritizing security, scalability, and cost-effectiveness.
Cloudera Data Platform addresses data management needs by supporting large-scale analytics, data science, and ETL processes. It facilitates seamless operation with Ambari UI for deployment and monitoring. Users benefit from robust security via Ranger, open-source compatibility, and a flexible eco-system that uses Hadoop components. While it simplifies setup and supports hybrid workloads, improvements in AI, machine learning, stability in Name Node High Availability, and cost management are ongoing needs. Challenges in tool usability, governance maturity, and scalability call for continued innovation, especially in cloud adoption and staying aligned with open-source technologies.
What are the key features of Cloudera Data Platform?Organizations in banking, healthcare, and hospitality leverage Cloudera Data Platform for data management, analytics, and cross-source integration. It handles complex data structures, bolsters AI workloads, and adheres to data compliance standards while integrating with tools like Spark, Kafka, and machine learning models.
Cohesity DataProtect integrates with VMware and cloud services like AWS and Azure, offering rapid VM restores and mass recovery, ransomware protection with immutable snapshots, intuitive UI, and scalability. It also consolidates data management, reducing data fragmentation.
Cohesity DataProtect provides comprehensive data protection and management through a user-friendly platform. It offers seamless integration with existing infrastructure, minimizing downtime and maximizing data security. Intuitive features like automated processes, centralized management, and robust search capabilities enhance operational efficiency. Despite areas needing improvement in reporting, interface usability, and legacy support, the platform remains a reliable choice for data backup, recovery, and ransomware protection. Users benefit from its compatibility with VMware, SQL, and Exchange and its ability to replace outdated tape systems while supporting cloud replication and test environments.
What key features does Cohesity DataProtect offer?Cohesity DataProtect is successfully implemented across industries such as finance, healthcare, and education, optimizing data protection and compliance needs. Organizations leverage its robust backup and recovery capabilities, ensuring data integrity and security while facilitating efficient resource use and operation management.
We monitor all AI Data Analysis reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
