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.

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The best features Cloudera Data Platform offers are from the earlier version, and if you see the latest version, there is significant change. It is very much end-user friendly. There are many user interfaces that they have added. A single pane for administration is easy from a data engineering perspective. You can use drag and drop more in the UI features; they are providing good dashboards to understand the performance of your platform. Ready metrics are available. It is very easy administration from a data platform standpoint. There are many other areas such as data principles including lineage and data security, all of which are really coming out of the box of this platform.

The dashboards and drag-and-drop tools have helped my team because the metrics are already available. As an administrator of the platform, certain key metrics are already available as a dropdown. You can select and pick whichever you want, and based on that, you will be able to see memory utilization and disk utilization. Based on that, you can make a decision such as whether you need to do some performance tweaks or add more hardware to your clusters. Those sorts of insights and early alerts help you to do that. That is also another feature available within the platform. From the administration perspective, it is really helpful for the data administrator or a platform administrator.

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The most unique feature I love about Cloudera Data Platform is its integration with Ranger services. Ranger is more flexible compared to Cloudera's previous data distribution component, Sentry, making it more reliable and allowing for access control at a more granular level.

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 crucial for managing all data inside the farm.

In terms of integration, it is very easy with Cloudera Data Platform. We just hook it up since it comes with the package when we install the CDP runtime, allowing us to select the ecosystem we want in our farm depending on our use cases. It is not a standalone installation requirement; it is an easy job. Scalability and flexibility are very good.

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Cloudera Data Platform offers many features, and the best feature is the unified enterprise data platform because it allowed the consolidation of multiple analytical data processing tools into a single coherent platform, reducing fragmentation and technical debt in our case.

Another benefit that we achieved with Cloudera Data Platform was true cloud scalability on AWS. The separation of storage and compute on Amazon S3 enabled elastic scaling of workloads based on demand, optimizing infrastructure cost and performance.

Another key benefit that we achieved with Cloudera Data Platform was strong data governance and security. With native integration with Apache Ranger and Apache Atlas, it ensures fine-grained access control, lineage, and metadata management critical in a regulated, multi-team environment. This was a very useful key benefit for us.

Cloudera Data Platform had a significant positive impact not only on the client but also on us as an IT consulting partner delivering and operating the solution. Cloudera Data Platform provided a stable and predictable enterprise platform on which we could design and deliver the solution with confidence. The maturity of the platform reduced architectural uncertainty and allowed us to focus on value-driven design rather than low-level infrastructure challenges. The strong governance and security capabilities built into Cloudera Data Platform had a direct positive impact on our engagement. Instead of implementing custom client-specific governance frameworks, we were able to rely on native components such as Ranger and Atlas. This significantly reduced customization effort, simplified compliance discussions with stakeholders, and increased trust in the platform from both IT and business teams. The platform supported a long-term partnership approach with the client. By implementing Cloudera Data Platform as a strategic data foundation rather than a point solution, we positioned ourselves as a trusted advisor on data architecture, governance, and advanced analytics, enabling follow-on initiatives and continuous evolution of the platform.

The adoption of Cloudera Data Platform delivered a 30-40% reduction in platform setup and onboarding time and a 25% faster delivery of data pipelines. Related to governance, there was a 30-40% reduction in governance-related custom development.

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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.

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The best features Cloudera Data Platform offers are the processing power with Spark and the distributed data storage, HDFS, which helps us handle massive volumes of data.

Cloudera Data Platform has positively impacted my organization by making it easier to handle such a massive scale of data onto our existing data warehouse systems, allowing us to store heterogeneous data sources.

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Cloudera Data Platform has significantly improved our data management. Distributed computing with Spark has enabled many processing types that were not possible before. 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.

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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.

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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.

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The foremost benefit is offloading data from the warehouse to Cloudera Data Platform, which allows for cheaper storage. We use it to push transformations and run ETL processes, leveraging tools like Spark. Cloudera also supports various functionalities, including AI and Gen AI tools. Basic reporting and some real-time functions are manageable on the platform. View full review »

The most unique thing about my setup with Cloudera Data Platform is having loads and loads of high-volume data, even for specific geographies, and using Cloudera Data Platform has helped us modernize that in a way where the tech is simpler for us.

The best features Cloudera Data Platform offers include their Kafka and Spark offerings, which we are using majorly, along with the Sqoop offering and a bit of Airflow here and there. The standout offering we have used from them is the Spark engine and the Impala engine to query our data, making the Impala cluster the best thing we have used from them.

Using the Spark and Impala engines makes my daily work easier and more efficient because Impala gives us a way to easily do analysis on the data, which simplifies the work of a business analyst as well as a PM when they are doing the initial analysis before the actual development begins for Spark, helping reduce the overall development cycle time.

Cloudera Data Platform has impacted my organization positively by providing cost-saving benefits, which is the North Star because of which we have shifted to it. We had data distributed across many platforms before starting this, and now the entire data strategy is designed around Cloudera Data Platform because it is very simple and very configurable.

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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.

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Distributed computing, secure containerization, and governance capabilities are the most valuable features.

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It is one of the better technology in terms of Hadoop.

The product offers a fairly easy setup process. 

It is quite stable.

The scalability is good. 

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The upgrades and patches must come from Hortonworks. Therefore, if we encounter any problems, they will be responsible for addressing them. This is one of the instances where we have to rely on them for all the upgrades.

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The most valuable part of this product is what Cloudera Data Science Workbench can do as a whole for modeling and analysis.  

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The data platform is pretty neat. The workflow is also really good. 

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One of the most valuable features is that you can configure your data nodes in the big data. Whatever you want. Normally, for example, if you are testing websites and Hash clouds and other sites, in most of them you must manage more than three or four requirements. For example, you must install each feature, you must compile some additional things, and also you must manage more than three configuration files to enable all the nodes to work together.

In the Hortonworks solution, you just need the service, and you just want to install it once to get started on projects easily. You can just click run and it's already installed and you can create and communicate between your services.

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I have no preferences towards any feature.

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A few of them, namely: Hive/Tez, HBase, Ranger, Yarn and Ambari. Ambari helps managing the platform, Hive is very easy to use. Ranger for security; with Ranger we can manager user’s permissions/access controls very easily.

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• Ambari Web UI: user-friendly
• Views for Hive, Tez, Pig
• Spark and Ranger

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We evaluated Cloudera and Hortonworks. Based on our evaluation and actual experience in production of 60 nodes and development of 12 nodes, the most valuable features of Hortonworks are:

• 100% open
• No lock-in like Cloudera
• Fast and accurate support instantly
• Largest number of committers to Hadoop by any means
• Hive is better in performance and ease of use compared to Impala

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• It's the one and only complete open source big data platform
• Ambari-managed admin configuration for HDFS, YARN, Hive, HBase, etc.
• Customized dashboards
• Web-based HDFS browser
• SQL editor for Hive
• Apache Phoenix - OLTP and operational analytics on Hadoop
• Apache Zeppelin - A web-based notebook that enables interactive data analytics

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It has a powerful, user-friendly interface called Ambari which allowed us to administrate our cluster easily.

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Hortonworks is 100% Open Source. Hortonworks does a great job in managing all different components of Hadoop.

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• Ambari
• Hive
• Sqoop
• Flume
• Spark

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The features I've found most valuable are--

• Ambari UI
• Hive
• Pig
• Hive
• Also integrated Tableau with this distribution

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From a product standpoint, their Ambari UI is incredibly valuable for cluster monitoring. It simplifies the deployment and maintenance of hosts, and we can provision, configure and test Hadoop services.

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There are several features that are most valuable for us--

• Hue
• Hive
• Spark
• S3

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Ease of deployment and management of the Hadoop cluster are features we've found most valuable.

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• Open-source
• Big community

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Its flexibility is the most valuable feature because you can leverage any Hadoop component and take full advantage of its open source capabilities.

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Its ability to scale out seamlessly with little to no effort is very valuable to us. All the tools in the stack are built from the ground up to support massive amounts of data.

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The HDFS (Java-based file system) and Hive Utilities are proving to be most useful.

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There’s not only one, the all-stack of Hadoop is valuable, the distributed file system HDFS, Spark, Kafka, HBase, etc. Hortonworks has certainly got the most up-to-date version of each component of Hadoop.

Compared to the other Hadoop distributions, the Ambari server provides the user an easy way to manage, to administrate and to configure their cluster. Ambari also provides a single view that gives you the possibility to use different Hadoop components from the same web interface.

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