Issue #53: July 18-July 24

SUMMARY: Rocky Linux for Google Cloud, GCP Dataflow new releases; Oracle Azure partnership, Azure Premium SSD v2 Disk Storage now in preview.

Google Cloud recommends Rocky Linux as an alternative to RHEL clone CentOS 7

Now in general availability, Rocky Linux for Google Cloud offers high bandwidth compute engine VMs and day-one support; promises to publish both community and GCP optimized versions for major releases.
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New Google Cloud Dataflow releases now in general availability

Dataflow Prime enables both horizontal and vertical scaling, Dataflow Go brings native support for Go, and Dataflow ML adds OOTB support for PyTorch, scikit-learn and RunInference.
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Oracle launches new database service for Azure

Oracle Database Service for Azure will let Azure customers directly access, provision, and manage Oracle databases running on Oracle Cloud Infrastructure using an Azure-native API and console.
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Azure Premium SSD v2 Disk Storage now in preview

Provision up to 64 TB storage capacity with GB increments at sub-millisecond latencies, with 80k IOPS and 1.2 GBPS throughput on a single disk.
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Microsoft previews Azure Event Hubs Self-Serve Scaling experience

Now scale Dedicated Event Hubs clusters at will. However, migrating from the existing dedicated clusters to self-serve clusters is not possible. See how to use and supported regions.
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AWS Glue Streaming ETL Auto Scaling now in general availability

AWS Glue Streaming ETL jobs can now scale resources up and down based on the input stream, reducing the cost and manual effort required to optimize resources.
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Memory-optimized Amazon EC2 R6a instances available in select regions

Powered by AMD Milan processors, the new instances are 10% cheaper than comparable x86 instances; use for memory-intensive applications. See specifications and comparisons.
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Parallel Steps for Google Cloud Workflows now in preview

Workflows executes steps in sequential order which makes it inefficient for long-running operations; Parallel Steps mitigates the inefficiency by executing steps concurrently using parallel branches and parallel iteration. See how to use it.
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