Cask vs Cloudera Data Platform comparison

Cask Data Application Platform (CDAP) is the first Unified Platform for Big Data. It provides standardization and deep integrations with diverse Hadoop technologies allowing companies to focus on application logic and insights, rather than infrastructure and integration. The platform is 100% open-source, highly extensible, and delivers enterprise-class features to help accelerate time to build, deploy, and manage data-centric applications & data lakes on Hadoop and Spark.

There are 3 extensions packaged with CDAP: Cask Hydrator, Cask Wrangler and Cask Tracker. CDAP Extensions are self-service, purpose-built applications on CDAP designed to solve common and critical big data challenges. Cask Hydrator for data pipelines, Cask Wrangler for data wrangling and Cask Tracker for data discovery and metadata.

CDAP removes barriers to innovation as an extensible and future-proof platform that provides consistency across environments and easily integrates with existing MDM, BI, and security solutions.

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.

• Comprehensive Hadoop Stack: Integrates HDFS, Hive, Spark for large-scale data operations.
• User-Friendly Interface: Managed through Ambari, simplifying configuration.
• Seamless Scalability: Efficiently handles growing data demands with ease.
• Open-Source Flexibility: Offers a customizable platform for specific needs.
• Security Tools: Includes Ranger for advanced data protection measures.
• Data Science Workbench: Provides a robust platform for data modeling.
• Cluster Management: Efficient deployment and governance capabilities.
• Containerization Support: Facilitates modern data processing environments.

• Data Storage Flexibility: Handles diverse data types, enhancing storage solutions.
• Advanced Security: Features tailored for data protection and compliance.
• Scalability: Cost-efficient management of expanding data requirements.
• Operational Efficiency: Streamlined processes through effective tools.
• Data Science Integration: Supports building and deploying models efficiently.
• Industry Versatility: Applicable across finance, healthcare, and more.

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.