CDP Public Cloud consists of a number of cloud services designed to address specific enterprise data cloud use cases.

This includes Data Hub powered by Cloudera Runtime, self-service apps (Data Warehouse, Machine Learning, and Data Engineering), the administrative layer (Management Console), and SDX services (Data Lake, Data Catalog, Replication Manager, and Workload Manager).

Administrative layer

Management Console is a general service used by CDP administrators to manage, monitor, and orchestrate all of the CDP services from a single pane of glass across all environments. If you have deployments in your data center as well as in multiple public clouds, you can manage them all in one place - creating, monitoring, provisioning, and destroying services.

Data Hub

Data Hub is a service for launching and managing workload clusters powered by Cloudera Runtime (Cloudera’s new unified open source distribution including the best of CDH and HDP). This includes a set of cloud optimized built-in templates for common workload types as well as a set of options allowing for extensive customization based on your enterprise’s needs.

Data Hub provides a complete workload isolation and full elasticity so that every workload, every application, or every department can have their own cluster with a different version of the software, different configuration, and running on different infrastructure. This enables a more agile development process.

Since Data Hub clusters are easy to launch and their lifecycle can be automated, you can create them on demand and when you don’t need them, you can return the resources to the cloud.

Self-service apps

Data Warehouse is a service for creating and managing self-service data warehouses for teams of data analysts. This service makes it easy for an enterprise to provision a new data warehouse and share a subset of the data with a specific team or department. The service is ephemeral, allowing you to quickly create data warehouses and terminate them once the task at hand is done.

Machine Learning is a service for creating and managing self-service Machine Learning workspaces. This enables teams of data scientists to develop, test, train, and ultimately deploy machine learning models for building predictive applications all on the data under management within the enterprise data cloud.

Data Engineering is a serverless service that allows you to submit Spark jobs to an auto-scaling cluster.

SDX services

Shared Data Experience (SDX) is a suite of technologies that make it possible for enterprises to pull all their data into one place to be able to share it with many different teams and services in a secure and governed manner. There are four discrete services within SDX technologies: Data Lake, Data Catalog, Replication Manager, and Workload Manager.

Data Lake is a set of functionality for creating safe, secure, and governed data lakes which provides a protective ring around the data wherever that’s stored, be that in cloud object storage or HDFS. Data Lake functionality is subsumed by the Management Console service and related Cloudera Runtime functionality (Ranger, Atlas, Hive MetaStore).

Data Catalog is a service for searching, organizing, securing, and governing data within the enterprise data cloud. Data Catalog is used by data stewards to browse, search, and tag the content of a data lake, create and manage authorization policies (by file, table, column, row, and so on), identify what data a user has accessed, and access the lineage of a particular data set.

Replication Manager is a service for copying, migrating, snapshotting, and restoring data between environments within the enterprise data cloud. This service is used by administrators and data stewards to move, copy, backup, replicate, and restore data in or between data lakes. This can be done for backup, disaster recovery, or migration purposes, or to facilitate dev/test in another virtual environment.

Workload Manager is a service for analyzing and optimizing workloads within the enterprise data cloud. This service is used by database and workload administrators to troubleshoot, analyze, and optimize workloads in order to improve performance and/or cost.