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Manage your data warehouse cost allocations with Amazon Redshift Serverless tagging

AWS Big Data

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. You can define your own key and value for your resource tag, so that you can easily manage and filter your resources. Create cost reports. View and edit tags.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities.

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Accelerate your data warehouse migration to Amazon Redshift – Part 7

AWS Big Data

With Amazon Redshift, you can use standard SQL to query data across your data warehouse, operational data stores, and data lake. Migrating a data warehouse can be complex. You have to migrate terabytes or petabytes of data from your legacy system while not disrupting your production workload.

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. Deploy the solution You can use the following AWS CloudFormation template to deploy the solution.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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Small companies more likely to implement BI tools and data warehouses in the cloud

BI-Survey

S mall companies are more likely than large or mid-sized companies to implement BI tools and data warehouses in the cloud. This makes sense because many small companies may not have a legacy BI/data warehouse environment and internal data center or the IT staff that can build something in-house.

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Data Governance and Metadata Management: You Can’t Have One Without the Other

erwin

When an organization’s data governance and metadata management programs work in harmony, then everything is easier. Data governance is a complex but critical practice. There’s always more data to handle, much of it unstructured; more data sources, like IoT, more points of integration, and more regulatory compliance requirements.

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