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Expanding data analysis and visualization options: Amazon DataZone now integrates with Tableau, Power BI, and more

AWS Big Data

Amazon DataZone now launched authentication supports through the Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Plug-and-play integration : A seamless, plug-and-play integration between data producers and consumers should facilitate rapid use of new data sets and enable quick proof of concepts, such as in the data science teams. As part of the required data, CHE data is shared using Amazon DataZone.

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

AWS Big Data

With the ability to browse metadata, you can understand the structure and schema of the data source, identify relevant tables and fields, and discover useful data assets you may not be aware of. About the Authors Chiho Sugimoto is a Cloud Support Engineer on the AWS Big Data Support team. Choose Run all.

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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products. This strategy supports each division’s autonomy to implement their own data catalogs and decide which data products to publish to the group-level catalog.

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Comparison of modern data architectures : Architecture Definition Strengths Weaknesses Best used when Data warehouse Centralized, structured and curated data repository. Inflexible schema, poor for unstructured or real-time data. Data lake Raw storage for all types of structured and unstructured data.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

However, you might face significant challenges when planning for a large-scale data warehouse migration. Data engineers are crucial for schema conversion and data transformation, and DBAs can handle cluster configuration and workload monitoring. Platform architects define a well-architected platform. Vijay Bagur is a Sr.