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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. Using Athena and the dbt adapter, you can transform raw data in Amazon S3 into well-structured tables suitable for analytics.

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Oracle Wants to Be the Database for AI

David Menninger's Analyst Perspectives

Oracle recently hosted its annual Database Analyst Summit, sharing the vision and strategy for its data platform. While much of the event was under non-disclosure as product plans and launch schedules are finalized, it still served as a useful recap of the broad portfolio of data platform capabilities that Oracle has to offer.

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Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy

AWS Big Data

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze your data using standard SQL and your existing business intelligence (BI) tools. Data ingestion is the process of getting data to Amazon Redshift.

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Unleash deeper insights with Amazon Redshift data sharing for data lake tables

AWS Big Data

Amazon Redshift has established itself as a highly scalable, fully managed cloud data warehouse trusted by tens of thousands of customers for its superior price-performance and advanced data analytics capabilities. This allows you to maintain a comprehensive view of your data while optimizing for cost-efficiency.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Data architecture has evolved significantly to handle growing data volumes and diverse workloads. Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data.

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

Figure 3 shows an example processing architecture with data flowing in from internal and external sources. Each data source is updated on its own schedule, for example, daily, weekly or monthly. The data scientists and analysts have what they need to build analytics for the user. The new Recipes run, and BOOM! Conclusion.

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United Airlines sets its flight plan for gen AI success

CIO Business Intelligence

As part of its storytelling ethos, the flight-status LLM will specify, for example, which precise weather event may be affecting a delayed flight and provide quick and useful information to customers about next actions.

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