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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

Prerequisites For this solution we use MongoDB Atlas to store time series data, Amazon SageMaker Canvas to train a model and produce forecasts, and Amazon S3 to store data extracted from MongoDB Atlas. The following screenshots shows the setup of the data federation. Setup the Database access and Network access.

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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

It lets engineers provide simple data transformation functions, then handles running them at scale on Spark and managing the underlying infrastructure. This enables data scientists and data engineers to focus on the feature engineering logic rather than implementation details.

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Analyzing the history of Tableau innovation

Tableau

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Connecting to data is fundamental to all data work, which is why “get data'' is at the start of the Cycle of Visual Analysis. Nov 2010), which allowed users to drag and drop multiple tables on one sheet.

Tableau 144
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Analyzing the history of Tableau innovation

Tableau

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Connecting to data is fundamental to all data work, which is why “get data'' is at the start of the Cycle of Visual Analysis. Nov 2010), which allowed users to drag and drop multiple tables on one sheet.

Tableau 98