Remove Data Classification Remove ETL Remove SQL
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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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Transform raw insurance data into CSV format acceptable to Neptune Bulk Loader , using an AWS Glue extract, transform, and load (ETL) job. When the data is in CSV format, use an Amazon SageMaker Jupyter notebook to run a PySpark script to load the raw data into Neptune and visualize it in a Jupyter notebook.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

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Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

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Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

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