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Starting with the February release (v1.14) of CDE , Apache Iceberg tables are now supported with Spark 3 virtual clusters on AWS. Users can query and work with tables at petabyte scale without impacting query planning, while benefiting from efficient metadata management, snapshotting, and time-travel.

 

 

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Customers and data practitioners can now use a self-service, point and click interface to author multi-step data pipelines with little to no code required.

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Cloudera Employee

Customers with bursty data pipelines with overlapping  schedules can now leverage Cloudera Data Engineering (CDE) new bin-packing auto-scaling policy for more efficient resource allocation and reduce cost.

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Cloudera Data Engineering (CDE) now offers the latest version of Airflow as the default managed scheduling service, which brings with it all the new benefits of Airflow 2.1 including: scheduler speedup of up to 17x, task groups, a new-look UI, and a new way of writing DAGs using the TaskFlow API.

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Users of Cloudera Data Engineering (CDE) on CDP Public Cloud can now deploy, monitor, and operationalize data pipelines with Spark 3 in addition to Spark 2, within the same environment.

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Administrators of Cloudera Data Engineering (CDE) on CDP Public Cloud now have access to real-time as well as historical diagnostic log bundles without the need to access S3 or Kubernetes APIs.

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Users of Cloudera Data Engineering (CDE) on CDP Public Cloud can now deploy, monitor, and operationalize data pipelines with Spark 3 in addition to Spark 2, within the same environment.

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Users of Cloudera Data Engineering (CDE) on CDP Public Cloud can now deploy, monitor, and operationalize data pipelines on Microsoft Azure at scale.

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Cloudera Employee

Supports fault-tolerant access to all cluster web UIs and end-to-end reliability for more workload types. 

 

 

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