Tandem Bank: Processing Transaction Data using AWS Lambda with NLP
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Amazon Web Services782 тыс
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29.04.19 – 66 1190:46Популярные
23 дня – 6420:07
Опубликовано 29 апреля 2019, 21:55
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Tandem wanted to help their customers make sense of bank account statements. In this episode, they share how they built a data pipeline that ingests data from customer bank statements from third-party banks. They use Amazon Kinesis to store the ingested data, then Amazon Simple Queueing Service and AWS Lambda process the data using ANTLR for NLP parser generation. The processed transaction data is then stored in Amazon Relational Database Service so it can be presented to the customer.
Host: Toby Knight, Manager, Solutions Architecture
Customer: Damian Moore, Principal Data Engineer, Tandem
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Tandem wanted to help their customers make sense of bank account statements. In this episode, they share how they built a data pipeline that ingests data from customer bank statements from third-party banks. They use Amazon Kinesis to store the ingested data, then Amazon Simple Queueing Service and AWS Lambda process the data using ANTLR for NLP parser generation. The processed transaction data is then stored in Amazon Relational Database Service so it can be presented to the customer.
Host: Toby Knight, Manager, Solutions Architecture
Customer: Damian Moore, Principal Data Engineer, Tandem
Subscribe:
More AWS videos bit.ly/2O3zS75
More AWS events videos bit.ly/316g9t4
#AWS
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