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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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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, dataclassification, organization and tagging.
How much data processing that occurs will depend on the data’s state when ingested and how different the format is from the desired end state. Most data processing tasks are completed using ETL (Extract, Transform, Load) or ELT (Extract, Load Transform) processes.
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An example of Software Defect case is [Customer: "Our data pipeline jobs are failing with a 'memory allocation error' during the aggregation phase. The same ETL workflows were running fine before the upgrade. The same ETL workflows were running fine before the upgrade. The same ETL workflows were running fine before the upgrade.
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