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AWS Machine Learning

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AWS Machine Learning

20.12.2024

Transition from AWS DMS to zero-ETL to simplify real-time data integration with Amazon Redshift | AWS Database Blog

The zero-ETL integrations for Amazon Redshift are designed to automate data movement into Amazon Redshift, eliminating the need for traditional ETL pipelines. With zero-ETL integrations, you can reduce operational overhead, lower costs, and accelerate your data-driven initiatives. This enables organizations to focus more on deriving actionable insights and less on managing the complexities of data integration. In this post, we discuss the best practices for migrating your ETL pipeline from AWS DMS to zero-ETL integrations for Amazon Redshift.

AWS Machine Learning

20.12.2024

Amazon DynamoDB re:Invent 2024 recap | AWS Database Blog

For the Amazon DynamoDB team, AWS re:Invent 2024 was an incredible experience to connect and reconnect with our customers. The key themes this year were “better together” integrations, data modeling, and building globally resilient, scalable applications on DynamoDB. In case you missed some of these sessions, or you wanted to get caught up on why customers like Klarna, Krafton, Vanguard, Fidelity, and JPMorgan Chase are building on DynamoDB, you can read this helpful summary of some of the DynamoDB highlights from re:Invent 2024.

AWS Machine Learning

21.12.2024

Jumia builds a next-generation data platform with metadata-driven specification frameworks | AWS Big Data Blog

Jumia is a technology company born in 2012, present in 14 African countries, with its main headquarters in Lagos, Nigeria. In this post, we share part of the journey that Jumia took with AWS Professional Services to modernize its data platform that ran under a Hadoop distribution to AWS serverless based solutions.

AWS Machine Learning

21.12.2024

Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation | AWS Big Data Blog

Amazon Q data integration, introduced in January 2024, allows you to use natural language to author extract, transform, load (ETL) jobs and operations in AWS Glue specific data abstraction DynamicFrame. This post introduces exciting new capabilities for Amazon Q data integration that work together to make ETL development more efficient and intuitive. We’ve added support for DataFrame-based code generation that works across any Spark environment. We’ve also introduced in-prompt context-aware development that applies details from your conversations, working seamlessly with a new iterative development experience.

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