The main use case for Cloudera Data Platform is to support a multi-source system with a multi-data structure. We have streaming services, Kafka services, RDBMS systems, and semi-structured data in the form of CSV and JSON files where we used to have everything in place and centralized.

Cloudera Data Platform also supports a hybrid data warehouse, which is similar to a relational database management system where business users can do query analytics, similar to a select star. Cloudera Data Platform also supports PySpark, where a user can create a data frame and then do a transformation load to perform and get insights.

The best features of Cloudera Data Platform are that it supports hybrid types of environments, real-time streaming analytics, secure data and governance, machine learning and AI workloads, data warehousing and BI, and edge-to-edge AI use cases.

In the hybrid environment, we can have a private cloud as well as a public cloud, which helps us enable both types of workloads. We have data that keeps coming through a pipeline, and then we just ingest our data. The data engineer transforms and loads it to a data lake, which is Amazon S3. Once the data is ready, it's on the downstream, and it's available for the consumer end to consume the data.

The most important features of Cloudera Data Platform are Rangers, which provide a granular level of security, allowing you to provide column-level security and decide what column you want to expose to the consumer, not just the tabular level.

Cloudera Data Platform has a great impact on my organization as it supports the business demand and business requirements, making me happy with the business use case. It depends on what the business demands and the business use case, which allows for an evaluation of what the business wants. Based on that, they can make a decision on where to go and where to migrate a workload.

I would definitely want to see more on the invention part of Cloudera Data Platform to provide a full-fledged AI and ML workload, as AI is supported currently, but I'm interested in having ML and LLM also supported in a full-fledged manner.

I have been working in the current field for almost six to eight years.

Cloudera Data Platform is stable.

Cloudera Data Platform's scalability is very nice, as you can have multiple workloads and even have multiple clusters with different CDP runtimes. You just have to define the business requirement in the configuration, and based on usage, it automatically scales up and scales down.

Customer support for Cloudera Data Platform is very good.

Positive

We have been using a Cloudera distribution for Hadoop, which is a CDP product, a CDH product. The CDH product provided on-premises only, so we migrated from on-premises to the cloud to opt for cloud compute.

The experience with pricing, setup cost, and licensing is very good. The cloud service provider has an inbuilt tool to analyze what zone and what region to use, as the services have costs associated with that, allowing us to manipulate which region is best suitable and cheaper.

In terms of ROI, we definitely have seen a return on investment. Due to security, we cannot disclose the value, but we have definitely seen an ROI.

The experience with pricing, setup cost, and licensing is very good.

I did not evaluate other options before choosing Cloudera Data Platform.

I would rate Cloudera Data Platform an eight out of ten because it's excellent in terms of the product, its deliverability, its support, and its use cases. It might differ for different industries depending on what each industry wants, but overall, it has a good impression, and I'm happy with the work relationship with Cloudera technical support.

If someone is looking for a hybrid environment or a cloud environment, they can definitely consider reviewing Cloudera Data Platform. They can look at all the aspects, as the Cloudera Data Platform ecosystem provides Apache Hive, HBase, Kafka, NiFi, Solr, and Knox, which they can review based on their business use case.