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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

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While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. Create dbt models in dbt Cloud.

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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

This blog post with accompanying code presents a solution to experiment with real-time machine translation using foundation models (FMs) available in Amazon Bedrock. To run the project code, make sure that you have fulfilled the AWS CDK prerequisites for Python. For collection_name , use the OpenSearch Serverless collection name.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Communication between the two systems was established through Kerberized Apache Livy (HTTPS) connections over AWS PrivateLink. To promote the success of this migration, we collaborated with the AWS team to create automated and intelligent digital experiences that demonstrated Rockets understanding of its clients and kept them connected.

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Understanding ETL Tools as a Data-Centric Organization

Smart Data Collective

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. Understanding the ETL Process. Before you understand what is ETL tool , you need to understand the ETL Process first. Types of ETL Tools.

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Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

In line with this mission, Talent.com collaborated with AWS to develop a cutting-edge job recommendation engine driven by deep learning, aimed at assisting users in advancing their careers. The solution does not require porting the feature extraction code to use PySpark, as required when using AWS Glue as the ETL solution.

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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. Run the AWS Glue ML transform job.

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Unify structured data in Amazon Aurora and unstructured data in Amazon S3 for insights using Amazon Q

AWS Machine Learning Blog

In this post, we explore how you can use Amazon Q Business , the AWS generative AI-powered assistant, to build a centralized knowledge base for your organization, unifying structured and unstructured datasets from different sources to accelerate decision-making and drive productivity. In this post, we use IAM Identity Center as the SAML 2.0-aligned

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