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Mastering the 10 Vs of big data 

Data Science Dojo

Data types are a defining feature of big data as unstructured data needs to be cleaned and structured before it can be used for data analytics. In fact, the availability of clean data is among the top challenges facing data scientists.

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Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning Blog

You can import data directly through over 50 data connectors such as Amazon Simple Storage Service (Amazon S3), Amazon Athena , Amazon Redshift , Snowflake, and Salesforce. In this walkthrough, we will cover importing your data directly from Snowflake. You can download the dataset loans-part-1.csv csv and loans-part-2.csv.

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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Flipboard

Data Wrangler simplifies the data preparation and feature engineering process, reducing the time it takes from weeks to minutes by providing a single visual interface for data scientists to select and clean data, create features, and automate data preparation in ML workflows without writing any code.

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Present and future of data cubes: an European EO perspective

Mlearning.ai

In the most generic terms, every project starts with raw data, which comes from observations and measurements i.e. it is directly downloaded from instruments. It can be gradually “enriched” so the typical hierarchy of data is thus: Raw dataCleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions.

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