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Enhancing Data Fabric with SQL Asset Type in IBM Knowledge Catalog

IBM Data Science in Practice

Data fabric is a cohesive architecture designed to simplify data access and management across diverse environments. In this blog, we explore how the introduction of SQL Asset Type enhances the metadata enrichment process within the IBM Knowledge Catalog , enhancing data governance and consumption.

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

Flipboard

For more information about using Athena to query an AWS Glue table, refer to Running SQL queries using Amazon Athena. For example, you can run the following SQL query: SELECT * FROM "c360_workshop_db"."source_auto_address" Under Data classification tools, choose Record Matching. This will open the ML transforms page.

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Upcoming Snowflake Features

phData

The recent Snowflake Summit 2024 brought plenty of exciting upcoming features, GA announcements, strategic partnerships, and many more opportunities for customers on the Snowflake AI Data Cloud to innovate. If you are new to Snowflake Cortex AI, check out this introductory blog.

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

phData

And if you want to apply a masking policy to a variant column, it’s a bit more complicated, especially with nested JSON data. In this blog, we will explore the difficulties of implementing Dynamic Data Masking for external virtual columns when working with simple and nested JSON data files.

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How to Migrate from dbt Core to dbt Cloud: phData’s Simplified Approach

phData

This is due to a number of reasons, which we covered in a previous blog post , but in short, it’s primarily due to a need to accelerate platform adoption outside of central IT teams. When used with the Snowflake Data Cloud , dbt Cloud is the perfect match for our customers looking to scale and grow platform adoption.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

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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. Create a SageMaker Unified Studio domain and three projects using the SQL analytics project profile. See Create a new project to create a project.

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