CDP Public Cloud: November 2019 Release Summary

Cloudera is pleased to announce the latest feature updates to CDP Public Cloud delivered in November 2019. CDP is an integrated data platform that is easy to deploy, manage, and use. CDP Public Cloud provides secured and governed data lakes (SDX), an environment with a wide range of analytic engines (Data Hub) as well as self-service data marts (Data Warehouse), and machine learning workspaces (Machine Learning).


  • Replication Manager is now available in CDP Public Cloud and supports replication of HDFS & Hive from CDH clusters (version 5.14 and later) to CDP Public Cloud. Additional components & data sources (e.g. HDP clusters) will be supported in future updates.

  • CDP Public Cloud on Azure is now in Tech Preview with support for SDX and Data Hub services, suitable for demos and a limited set of onboarded customers. Cloudera Data Warehouse and Cloudera Machine Learning on Azure are planned for future updates.

Cloudera Data Warehouse

  • Rapid Auto-scaling - For critical workloads with an immediate need for availability and a short term lifespan, Impala VMs can auto-scale to ensure results are delivered as fast as possible. With this update, autoscaling happens significantly faster (seconds vs. minutes) by preparing more of the package in advance of the auto-scale request, saving valuable resource consumption costs.

  • Accelerated Data Warehouse SQL at Massive Scale - For demanding, long running workloads on massive data volumes, typical of an enterprise data warehouse, Hive on LLAP delivers speed without sacrificing scalability. With this update, Hive has been further optimized for even faster SQL performance with improvements in memory handling and accuracy of table statistics for partitioned tables, ensuring all your reports meet or exceed business KPIs. Read more about managing and tuning Hive LLAP Virtual Data Warehouses.

  • Built-in Fuzzy Full Text Search - Finding “close enough” text matches, such as finding customer name matches when mis-spelled, or by approximate address, may require multiple queries into the same data, with different search engines. With this update, Impala streamlines such searches with native support for Jaro-Winkler fuzzy string matching, making it faster and easier to retrieve numerically scored, approximate match results in a single query, making it far easier for line of business users to take advantage of text searching in their analytics. Read more about Impala string functions.

  • Optimized Cloud Data Warehousing - Returning large result sets across long latency connections in the cloud can be slow and expensive, as they tend to be demanding on server-side execution resources. With this update, server-side results spooling dramatically frees up those execution resources, speeding up how users see their query results while also reducing cloud consumption cost. Read more about spooling Impala query results.

Cloudera Machine Learning

  • Analytical Applications: This capability enables data science teams build and host persistent web-based applications and dashboards using frameworks like Flask, Dash, and Shiny to easily share analytics results and insights with business stakeholders. Users can build completely custom functional applications for everything from dashboard reports like Tableau to portable predictive apps that make use of underlying machine learning models in business user UIs. Check this blog to see how it works and don’t forget to share it with your colleagues and customers.

  • Resource Quotas: This feature gives CML workspace site administrators a way to enable CPU, GPU, and memory usage quotas per user. Quotas limit the aggregate resource consumption for data scientists and enable admins to control resource costs more granularly.

  • Diagnostic Bundles: CML now allows site administrators to download diagnostic bundles from the Site Admin panel making it easier to analyze, share, and address issues.

Cloudera Data Hub

  • Operational Database cluster enhancements: The Operational Database cluster definition enables customers to build real-time, high-concurrency, mission-critical business applications in the cloud environment. Key enhancements in this update include real-time monitoring, out-of-the-box resilience, and replication capabilities (Tech Preview) for Disaster Recovery (DR) and for enabling seamless migration of operational applications from on-premises to cloud.

  • Real-Time Data Mart cluster definition: Now available in Data Hub with Apache Kudu, Apache Impala, and Apache Spark for real-time data management and operational analytics use cases. This cluster definition is an excellent choice for real-time analytics on fast changing data, and can be leveraged with Cloudera Data Warehouse (CDW) for a holistic analytics experience.

  • Enhanced SQL Access for DE Clusters: Data Hub clusters built with Data Engineering cluster definition now offer an improved SQL experience for Hive SQL users with smart query authorizing, inbuild performance analysis & recommendations tools available in Data Analytics Studio (DAS). This capability provides an enhanced self-service experience for data analysts and data engineers.

  • Simplified, consistent access to Amazon S3: S3Guard provides strong consistency guarantees when accessing data in S3 across multiple workloads or clusters. S3guard is now easier to use in CDP, allowing customers to either leverage existing DynamoDB infrastructure or have Data Hub clusters automatically manage this infrastructure, resulting in less administration overhead and minimizing costs.

Management Console

  • Wire Encryption: CDP Data Hub and SDX now offer TLS encryption across all endpoints for both data and control traffic, providing an internal Certificate Authority (CA) with commonly trusted certificates and automatic certificate expiry warnings. With auto-TLS, CDP’s default setting, adding new cluster hosts or services to a cluster with auto-TLS enabled automatically creates and deploys the required certificates. Automatic wire encryption makes your complete deployment secure by design and reduces business risk as well as operational overheads associated with enabling and managing TLS encryption on the cluster. Read more about how to use S3 encryption with CDP.

  • Consistent FreeIPA user password across all environments: Users’ FreeIPA passwords are automatically propagated to each newly created environment. Users can still manage their FreeIPA password from the User Management page and reset it if needed. Learn more about how CDP federates identities across clouds.

Data Catalog

  • Ranger Audit Profiler: Adds ability to view access patterns from a forensic audit or compliance perspective. The audit profiler uses the Apache Ranger audit logs to show the most recent raw audit event data as well as summarized views of audits by type of access and access outcomes (allowed/denied). Access pattern visualizations make anomalies stand out and allow for focussed investigation and improved security. Find out more about how any data can be profiled using the Data Catalog Profilers.

The full release documentation including release notes for each service is available here (Service Name → Release Notes → What’s New).