Remove Data Preparation Remove ETL Remove SQL
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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

Data can be generated from databases, sensors, social media platforms, APIs, logs, and web scraping. Data can be in structured (like tables in databases), semi-structured (like XML or JSON), or unstructured (like text, audio, and images) form. How to Choose the Right Data Science Career Path?

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End-to-End model training and deployment with Amazon SageMaker Unified Studio

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Organizations need a unified, streamlined approach that simplifies the entire process from data preparation to model deployment. To address these challenges, AWS has expanded Amazon SageMaker with a comprehensive set of data, analytics, and generative AI capabilities. sql project.athena SELECT * FROM " "."sqad";

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IBM watsonx Platform: Compliance obligations to controls mapping

IBM Journey to AI blog

IBM watsonx.data facilitates scalable analytics and AI endeavors by accommodating data from diverse sources, eliminating the need for migration or cataloging through open formats. This approach enables centralized access and sharing while minimizing extract, transform and load (ETL) processes and data duplication.

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Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

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Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

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How Formula 1® uses generative AI to accelerate race-day issue resolution

AWS Machine Learning Blog

The assistant is connected to internal and external systems, with the capability to query various sources such as SQL databases, Amazon CloudWatch logs, and third-party tools to check the live system health status. To handle the log data efficiently, raw logs were centralized into an Amazon Simple Storage Service (Amazon S3) bucket.

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Ask HN: Who is hiring? (July 2025)

Hacker News

Good at Go, Kubernetes (Understanding how to manage stateful services in a multi-cloud environment) We have a Python service in our Recommendation pipeline, so some ML/Data Science knowledge would be good. Queries everywhere – SQL lives in Slack snippets, BI folders, dusty Git repos, and copy-pasted Notion pages.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

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