Remove Business Objectives Remove Data Lake Remove Metadata
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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

However, enterprises often encounter challenges with data silos, insufficient access controls, poor governance, and quality issues. Embracing data as a product is the key to address these challenges and foster a data-driven culture. Amazon Athena is used to query, and explore the data.

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Introducing a new unified data connection experience with Amazon SageMaker Lakehouse unified data connectivity

AWS Big Data

This approach simplifies your data journey and helps you meet your security requirements. The SageMaker Lakehouse data connection testing capability boosts your confidence in established connections. About the Authors Chiho Sugimoto is a Cloud Support Engineer on the AWS Big Data Support team.

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Access Amazon Redshift data from Salesforce Data Cloud with Zero Copy Data Federation

AWS Big Data

This post is co-authored by Vijay Gopalakrishnan, Director of Product, Salesforce Data Cloud. In today’s data-driven business landscape, organizations collect a wealth of data across various touch points and unify it in a central data warehouse or a data lake to deliver business insights.

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Harness Zero Copy data sharing from Salesforce Data Cloud to Amazon Redshift for Unified Analytics – Part 2

AWS Big Data

For Shared database’s region , choose the Data Catalog view source Region. The Shared database and Shared database’s owner ID fields are populated manually from the database metadata. The resource link appears on the Databases page on the Lake Formation console, as shown in the following screenshot.

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Clean up your Excel and CSV files without writing code using AWS Glue DataBrew

AWS Big Data

You can use its built-in transformations, recipes, as well as integrations with the AWS Glue Data Catalog and Amazon Simple Storage Service (Amazon S3) to preprocess the data in your landing zone, clean it up, and send it downstream for analytical processing. For Matching conditions , choose Match all conditions.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

This post also discusses the art of the possible with newer innovations in AWS services around streaming, machine learning (ML), data sharing, and serverless capabilities. A data hub contains data at multiple levels of granularity and is often not integrated. Let’s look at the components of the architecture in more detail.

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Introducing data products in Amazon DataZone: Simplify discovery and subscription with business use case based grouping

AWS Big Data

For example, a marketing analysis data product can bundle various data assets such as marketing campaign data, pipeline data, and customer data. With the grouping capabilities of data products, data producers can manage and control access to the underlying data assets with just a few steps.