SQL Server - Big Data Clusters

Summary:


SQL Server Big Data Clusters is the multi-cloud, open data platform for analytics at any scale. Big Data Clusters (BDC) unites SQL Server with Apache Spark to deliver the best compute engines available for analytics in a single, easy to use deployment. With these engines, BDC is the ideal data platform for AI, ML, M/R, Streaming, BI, T-SQL, and Spark. Delivered as part of the SQL Server 2019 release, BDC is a cloud-native solution orchestrated by Kubernetes. Our mission is to accelerate, delight and empower our users as they quench their thirst for data driven insights.
 
More information about SQL Server Big Data Clusters is available in the documentation.

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  1. Volume Management for SQL Server Big Data Clusters on AKS

    Currently it is possible to customise storage volumes sizes and storage classes (AKS,Kubernetes) per pool (master, data, storage pool)
    But right now only 1 (one) volume per pod is available. It would be a game changer feature if you could have a volume management feature for Big Data Clusters to add, resize and change storage classes for multiple volumes per pod for all pool types.

    https://docs.microsoft.com/en-us/sql/big-data-cluster/concept-data-persistence?view=sql-server-ver15#customize-storage-configurations-for-each-pool

    https://docs.microsoft.com/en-us/azure/aks/concepts-storage#storage-classes

    7 votes
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  2. BDC Bulk Data Loading utility

    Many customers coming from data warehouse appliances are used to have bulk data loading utility. To speed migration to BDC platform it would be nice feature to have native BDC utility included instead of having to write our own.

    5 votes
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  3. 3 votes
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  4. BDC integration with Azure Machine Learning Service as external compute option

    Some customers have datasets they can't share to the public cloud and they also don't have good compute options on-premise. The marriage of BDC as a compute option to Azure Machine Learning Service would be a good way to distribute training of models that must be trained on-prem and still take advantage of the superior UX in AML. In particular, the Jupyter and AI User Interface on AML are much stronger than Azure Data Studio. Getting out of running tools on workstation and using browser based AI tools is what customers want.

    2 votes
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  5. Access to metadata on spark and hdfs versions

    Customers frequently ask which version of spark and hdfs is being used on the cluster. It would be handy to have a command from azdata to fetch versions of the opensource software distributions in the cluster.

    2 votes
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  6. 1 vote
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  7. 1 vote
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