How financial services companies derive greater value from data with AWS
summary
Claims that financial firms can centralize siloed data on an S3 data lake and run serverless, event driven pipelines that cut cost and accelerate analytics Using Glue EMR Kinesis DMS Direct Connect Snowball Redshift Athena QuickSight and SageMaker she shows a document pipeline that uses Textract → Comprehend → SageMaker to turn PDFs into searchable JSON pushed to Elasticsearch and Neptune with metadata in DynamoDB An example: Intuit shrinks model build from six months to one week a 90% reduction while Broadridge automates PDF extraction at scale
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chapters (17)
- 0:00Intro
- 0:42Data needs to be aggregated at speed and scale to reach business goals
- 5:02AWS simplifies data access, management, and analytics
- 5:17AWS helps financial institutions acquire and manage data cost-effectively
- 5:53Industry-leading financial institutions are deriving greater value from their data with AWS
- 6:20AWS data lakes and analytics solutions provide a secure platform for innovation
- 10:58Essential elements of an analytics solution
- 11:26Data movement
- 14:37Data Lakes are the future of data management
- 15:10Perform analytics
- 17:30Doing more with data by performing agile analytics in the cloud
- 19:26Amazon SageMaker helps you build, train, and deploy models
- 20:53Updated process to extract data from proxy voting forms leveraging machine learning
- 21:42Sample architecture pattern - NLP
- 24:49Metadata services
- 25:06Ingestion module
- 26:55Analytics module
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