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Data Classification: Does Python still have a need for class without dataclass?

Hacker News

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Choosing the Right Programming Language for A Corporate Database

Smart Data Collective

SQL uses a straightforward system of data classification with tables and columns that make it relatively easy for people to navigate and use. Python is a high-level, general-purpose programming language that has a wide variety of uses and applications. R Programming Language.

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How the UNDP Independent Evaluation Office is using AWS AI/ML services to enhance the use of evaluation to support progress toward the Sustainable Development Goals

AWS Machine Learning Blog

Data ingestion and extraction Evaluation reports are prepared and submitted by UNDP program units across the globe—there is no standard report layout template or format. The postprocessing component is capable of extracting data from complex, multi-format, multi-page PDF files with varying headers, footers, footnotes, and multi-column data.

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Upcoming Snowflake Features

phData

Snowflake announced many new features that will enhance development and collaboration: Snowflake Notebooks: Currently available in public preview, Snowflake notebooks provide a notebook interface that enables data teams to collaborate with Python and SQL in one place. Furthermore, Snowflake Notebooks can also be run on a schedule.

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How To Use Dynamic Data Masking for Virtual Columns in Snowflake External Tables

phData

Limitation of Data Classification Table Function Tagging is a feature in Snowflake, represented as a schema-level object. It empowers a data governance team to mark and label PII and sensitive information, followed by associating a policy object to safeguard these fields.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning Blog

Prerequisites To follow along with this post, set up Amazon SageMaker Studio to run Python in a notebook and interact with Amazon Bedrock. The Python code invokes the Amazon Bedrock Runtime service: import boto3 import json from datetime import datetime import time # Create an Amazon Bedrock Runtime client in the AWS Region of your choice.

AWS 115
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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

Flipboard

Load and visualize the raw data in Neptune This section uses SageMaker Jupyter notebooks to load, query, explore, and visualize the raw property and auto insurance data in Neptune. We use Python scripts to analyze the data in a Jupyter notebook. Under Data classification tools, choose Record Matching.

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