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Mastering Data Normalization: A Comprehensive Guide

Data Science Dojo

Thats where data normalization comes in. Its a structured process that organizes data to reduce redundancy and improve efficiency. Whether you’re working with relational databases, data warehouses , or machine learning pipelines, normalization helps maintain clean, accurate, and optimized datasets. Simple, right?

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Is web3 data storage ushering in a new era of privacy?

Dataconomy

The main solutions on the market are decentralized file storage networks (DSFN) like Filecoin and Arweave, and decentralized data warehouses like Space and Time (SxT). A 2024 report by research company Messari pegged the total addressable market for cloud storage at a staggering $80 billion, with 25% annual growth.

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AWS re:Invent 2024 Highlights: Top takeaways from Swami Sivasubramanian to help customers manage generative AI at scale

AWS Machine Learning Blog

We spoke with Dr. Swami Sivasubramanian, Vice President of Data and AI, shortly after AWS re:Invent 2024 to hear his impressionsand to get insights on how the latest AWS innovations help meet the real-world needs of customers as they build and scale transformative generative AI applications. Q: What made this re:Invent different?

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AWS at Databricks Data + AI Summit 2025

databricks

Keep up with us Subscribe Share this post Never miss a Databricks post Subscribe to the categories you care about and get the latest posts delivered to your inbox Sign up What's next? 160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2025.

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Unlock the power of structured data for enterprises using natural language with Amazon Q Business

AWS Machine Learning Blog

Natural language is ambiguous and imprecise, whereas data adheres to rigid schemas. For example, SQL queries can be complex and unintuitive for non-technical users. Handling complex queries involving multiple tables, joins, and aggregations makes it difficult to interpret user intent and translate it into correct SQL operations.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

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How Meta enforces purpose limitation via Privacy Aware Infrastructure at scale

Hacker News

Policy Zones has been built into different Meta systems, including: Function-based systems that load, process, and propagate data through stacks of function calls in different programming languages. Batch-processing systems that process data rows in batch (mainly via SQL ). When data flows across different systems (e.g.,