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SageMaker Spark is an open-source Spark library for Amazon SageMaker. With SageMaker Spark you construct Spark ML Pipelines using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting. With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale. ...

...We believe that ScalikeJDBC basically works with any other RDBMS (Oracle, SQL Server and so on). If you can access some datastore via JDBC interface, that means you can access them via ScalikeJDBC too. Recently, Amazon Redshift and Facebook Presto support JDBC interface. You can access them via ScalikeJDBC! Of course, you can use c3p0 (or others) instead of commons-dbcp though ConnectionPool implementation for that isn’t provided by default.