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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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Texas Rangers data transformation modernizes stadium operations

CIO Business Intelligence

Resultant recommended a new, on-prem data infrastructure, complete with data lakes to provide stake holders with a better way to manage data reliability, accuracy, and timeliness. In the strategic data assessment, when people were like, ‘Oh, you can show us the ice cream sales?’

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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation

AWS Big Data

The DataFrame code generation now extends beyond AWS Glue DynamicFrame to support a broader range of data processing scenarios. Your generated jobs can use a variety of data transformations, including filters, projections, unions, joins, and aggregations, giving you the flexibility to handle complex data processing requirements.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

“Digitizing was our first stake at the table in our data journey,” he says. That step, primarily undertaken by developers and data architects, established data governance and data integration. For that, he relied on a defensive and offensive metaphor for his data strategy.

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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.

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Connecting the Data Lifecycle

Cloudera

Data transforms businesses. That’s where the data lifecycle comes into play. Managing data and its flow, from the edge to the cloud, is one of the most important tasks in the process of gaining data intelligence. . The firm also worked on creating a solid pipeline from the data warehouse to the data lake.

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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

Overstocking can lead to increased holding costs and waste, while understocking can result in lost sales, reduced customer satisfaction, and damage to the business’s reputation. This is because raw data from various sources can contain inconsistencies, errors, and missing values that may distort the analysis and forecast results.