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How CFM built a well-governed and scalable data-engineering platform using Amazon EMR for financial features generation

AWS Big Data

In this post, we share how we built a well-governed and scalable data engineering platform using Amazon EMR for financial features generation. Joel Farvault is Principal Specialist SA Analytics for AWS with 25 years’ experience working on enterprise architecture, data governance and analytics, mainly in the financial services industry.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

However, you might face significant challenges when planning for a large-scale data warehouse migration. Data engineers are crucial for schema conversion and data transformation, and DBAs can handle cluster configuration and workload monitoring. Platform architects define a well-architected platform.

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Migrate your existing SQL-based ETL workload to an AWS serverless ETL infrastructure using AWS Glue

AWS Big Data

Customers often use many SQL scripts to select and transform the data in relational databases hosted either in an on-premises environment or on AWS and use custom workflows to manage their ETL. AWS Glue is a serverless data integration and ETL service with the ability to scale on demand.

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Expanding data analysis and visualization options: Amazon DataZone now integrates with Tableau, Power BI, and more

AWS Big Data

In the Driver Properties section, enter the parameters that you captured from Amazon DataZone: CredentialsProvider : The credentials provider to authenticate requests to AWS DataZoneDomainId : The ID of your Amazon DataZone domain DataZoneDomainRegion : The AWS Region where your domain is hosted. Lionel Pulickal is Sr.

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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division. Consumer feedback and demand drives creation and maintenance of the data product.