Amazon EMR and Cloudera Data Platform compete in big data and analytics. Amazon EMR stands out for its scalability and AWS integration, while Cloudera's comprehensive features provide an edge for complex data tasks.
Features: Amazon EMR is renowned for its elasticity, automatic scaling, and seamless integration with AWS services. It supports a wide range of data processing frameworks like Hadoop and Spark, providing flexibility in data management. Cloudera Data Platform offers extensive data management capabilities through its open-source architecture, a powerful Ambari UI for cluster management, and robust security features with tools like Ranger for access control.
Room for Improvement: Amazon EMR can be enhanced through more advanced machine learning capabilities, improved hybrid cloud support, and refined cost management tools. Cloudera Data Platform would benefit from simplified deployment processes, reduced dependency on proprietary updates, and enhanced real-time data processing functionalities.
Ease of Deployment and Customer Service: Amazon EMR offers straightforward deployment within AWS and benefits from reliable and accessible support. Cloudera Data Platform provides extensive enterprise support but might require more initial effort for deployment due to its comprehensive feature set and customization options.
Pricing and ROI: Amazon EMR follows a pay-as-you-go model, allowing cost efficiency as operations scale. Cloudera typically demands a larger initial investment but promises potentially greater ROI with its feature-rich environment and enhanced capabilities for comprehensive data management.
They help with billing, cost determination, IAM properties, security compliance, and deployment and migration activities.
I have communicated with technical support, and they are responsive and helpful.
Scalability can be provisioned using the auto-scaling feature, EC2 instances, on-demand instances, and storage locations like block storage, S3, or file storage.
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.
Regular updates, patch installations, monitoring, logging, alerting, and disaster recovery activities are crucial for maintaining stability.
There is room for improvement with respect to retries, handling the volume of data on S3 buckets, cluster provisioning, scaling, termination, security, and integration between services like S3, Glue, Lake Formation, and DynamoDB.
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.
Cost optimization can be achieved through instance usage, cluster sharing, and auto-scaling.
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.
Amazon EMR helps in scalability, real-time and batch processing of data, handling efficient data sources, and managing data lakes, data stores, and data marts on file systems and in S3 buckets.
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 foremost benefit is offloading data from the warehouse to Cloudera Data Platform, which allows for cheaper storage.
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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