Cloudera on AWS

My main use case for Cloudera Data Platform  is that I am a certified administrator. I use Cloudera Data Platform  in my daily work by managing it as a whole in a Telco company. I regularly handle tasks by managing Cloudera Data Platform and being responsible for its services, which are currently up and running, and managing daily administrative tasks.

In my experience, the best features Cloudera Data Platform offers are that all the services provided are excellent.

A particular service that stands out to me in Cloudera Data Platform is the performance, which runs very fast. I also find very good features in data security, data reliability, and data lineage.

Cloudera Data Platform's Manager UI and other UIs are very useful and helpful for managing operations.

Cloudera Data Platform has positively impacted my organization as it comes in very handy while performing on big data and handling large files.

I think Cloudera Data Platform is good enough to run now, and I do not see areas for significant improvement.

I wish Cloudera Data Platform would add a service, apart from Ozone , to handle small files with faster performance, so as not to use Ozone  or add extra hard disk capacity to the cluster.

I have been using Cloudera Data Platform for approximately five years.

Cloudera Data Platform is very stable in my experience.

Scalability of Cloudera Data Platform is very good and scalable in public cloud. However, it is not as scalable on on-premises private cloud, which adds considerable cost.

I have interacted with the customer support team extensively, and they are very useful and helpful in resolving issues. I would rate the customer support of Cloudera Data Platform ten out of ten.

Positive

Before choosing Cloudera Data Platform, my organization was using Teradata , and we did not evaluate other options.

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.

We are using Cloudera Data Platform  to migrate and run our ETL processes, transferring data from multiple RDBMS  to a data lake for analysis purposes. The current organization I work for is a top bank with a data lake of more than one petabyte.

Cloudera Data Platform  is a perfect tool to manage such vast amounts of big data, store it properly, query it, and move it from one end to another.

The most useful feature I currently use from Cloudera Data Platform is the Hue tool, which provides a web-based utility. Users don't need network access approval when using on-premises internal access. Additionally, Spark and Impala are the most useful tools that I have used from Cloudera Data Platform.

The current organization I work for is a top bank with a data lake of more than one petabyte. For this specific purpose, Cloudera Data Platform is a perfect tool to manage such vast amounts of big data, store it properly, query it, and move it from one end to another.

Cloudera Data Platform should use fewer tools and remove the complexity between them. It should make it easier for the end user to change the configuration and understand it better.

The UI tool for jobs in Cloudera Data Platform can be improved to provide a proper image of ETL jobs and detailed consolidated graphs to monitor Spark-based Hue jobs.

I have been working on a big data platform for the last five years, starting from 2020. Initially, I worked on Hortonworks platform for the last two to three years. Since Cloudera and Hortonworks merged into a single platform which is Cloudera Data Platform, I have been working on the CDP platform for the last two years.

We face downtime and reliability issues many times a week with Cloudera Data Platform because it is a very complex system and all configurations are managed by the end user. 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.

For scalability, I would rate Cloudera Data Platform nine out of 10. We periodically have requirements to add resources or servers, and we find it very useful from a scalability perspective.

The customer support from Cloudera is good when we receive support from non-Indian representatives. When support comes from Indian representatives, we receive level one support only.

Neutral

Previously we were using Hive  tool for querying engine, but since installing Impala, we witnessed huge performance improvements with Cloudera Data Platform. The majority of users are using Impala instead of Hive  because Impala uses MPP, massively parallel processing technology.

If the only requirement is to have an on-premises system without other options, then Cloudera Data Platform is the best option available. However, if cloud is an option, I would prefer cloud more than on-premises Cloudera system.

We are currently using Cloudera Data Platform with specific tools: Rangers to manage access-related items, HDFS to store files, Hive and Impala to access them, Hue as a query editor, and Spark for ETL execution.

It is a very complex system compared to cloud technology, which is much simpler. Due to this complexity, I rate Cloudera Data Platform six out of 10.

My main use case for Cloudera Data Platform  is for data analytics and AI workload.

We have different data sources where the data is coming in tabular format or CSV, semi-structured or structured, unstructured, and some sort of Kafka streaming messages. We use to store it and then we process and transform, apply the business logic, and then make the data ready for the consumer to consume.

Cloudera Data Platform  offers excellent architectures in terms of decoupling the storage layer from the compute. It is flexible in terms of scaling to your storage account or compute. Additionally, we have different streaming services as part of the ecosystem, and they have added Ranger for security controls, which is a valuable feature.

Decoupling storage from compute has helped my team significantly. Before using Cloudera Data Platform, we were using Cloudera Distribution for Hadoop  (CDH), where we had to have on-premises virtual machines or Linux boxes to add to the cluster, which required lots of effort. We had defined authorized maximum storage per system; for example, one computer can have a maximum of 8 TB, and scaling up to add more compute to the cluster was very challenging. In the current Cloudera Data Platform, the backend storage is a data lake that auto-scales, so we don't have to add more storage. In terms of security, we used to use Sentry  in traditional CDH, but in Cloudera Data Platform, Ranger provides more granular level of security, allowing us to manage who can access data at different levels, maybe at a tabular level or column level.

Streaming services are provided by NiFi, which is one of the best ecosystems for streaming and ETL support.

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. It is also cost-effective and more stable than the traditional ways of handling big data workload.

In terms of resources, we have reduced from ten resources to four or five resources, making it an effective reduction in manual effort. Regarding cost saving, since we are in the cloud, we are saving significant money compared to maintaining infrastructure on-premises.

Cloudera Data Platform could improve by innovating more in terms of full-fledged support for AI workloads, enriching machine learning or LLM, as there haven't been updates in that aspect over the last one and a half years.

I have been using Cloudera Data Platform for almost four years.

Cloudera Data Platform's scalability is very good.

Customer support is good. However, having a common chat channel between firms and service providers would make communication faster and more efficient.

Neutral

My advice to others looking into using Cloudera Data Platform is that if they are looking for big data workloads on the cloud where they can do analysis and achieve cost savings and resource reductions, it is definitely a good use case. It can vary based on business needs, but it is a good option for big data workloads.

I rated Cloudera Data Platform a six out of ten because I wish that it would keep up with market trends and release AI technology and AI-enabled workloads. Sometimes we struggle to get support, and having a common chat channel between firms and service providers would make communication and support more effective, especially in production.

My main use case for Cloudera Data Platform  is measuring HDFS and the SQL queries in Impala to troubleshoot some error in YARN applications based on Spark, and control the reporting data between Informatica and Cloudera for transport data between the DB Oracle, Mongo DB to CDP in Impala, between HDFS.

For measuring HDFS, I use Cloudera Data Platform , specifically Cloudera Manager, to analyze small files in HDFS to reduce our number for the duration of jobs that read this file and the partition date.

I mainly use Cloudera Data Platform as part of a large-scale data processing and analytics pipeline in a hybrid cloud environment, primarily on Azure , which involves managing the YARN cluster, monitoring workloads, troubleshooting performance issues, and integrating data ingestion and transformation processes from various enterprise systems. We leverage CDP for its scalability, security, and strong integration with Looker , Informatica, Hive , and Spark.

Cloudera Data Platform (CDP) has helped our organization improve data management consistency and scalability across multiple environments. The unified control plane and centralized governance have reduced operational overhead and made it easier to manage workloads between on-premise and cloud environments.

We’ve also seen clear benefits in resource optimization — auto-scaling and workload isolation features have allowed better use of infrastructure, while tools like Cloudera Manager and Workload XM improved monitoring and troubleshooting efficiency.

That said, there’s still room for improvement in integration speed and UI responsiveness, especially when managing large clusters or hybrid deployments.

In my opinion, the best features of Cloudera Data Platform are its strong integration, scalability, and unified management capabilities, while what stands out the most in Cloudera Manager are SDX, which provide centralized control for governance, security, and data lineage across multiple sources, simplifying operations significantly. Finally, the YARN and Spark resource management in CDP is robust and efficient, which is essential for handling heavy data transformation workloads at scale.

Cloudera Data Platform has positively impacted my organization by providing a unique storage point for a lot of data from various databases in HDFS. With Hive  or Impala, it is possible to read and integrate data among all the other platforms, making it a great platform for us to have the data and create integrations.

I don't have any challenges or areas I think could use enhancement.

I have been using Cloudera Data Platform for one year, and I have experience with the last version of Cloudera Data Platform for four years.

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. The costs are increasing at the start of the project, but after securing, they are reduced, and the most significant benefit is the availability of data from governance and management.

For the centralized governance of Spark management, we use a dashboard on SAS or Power BI to integrate the data that is stored in HDFS.

My advice to others looking into using Cloudera Data Platform is that it's a great product to save time and reduce costs in the long term.

On a scale of one to ten, I rate Cloudera Data Platform a nine.