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How the BMW Group analyses semiconductor demand with AWS Glue

AWS Big Data

We also split the data transformation into several modules (Data Aggregation, Data Filtering, and Data Preparation) to make the system more transparent and easier to maintain. Although each module is specific to a data source or a particular data transformation, we utilize reusable blocks inside of every job.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. Query the data using Athena Athena is a serverless, interactive analytics service built to analyze unstructured, semi-structured, and structured data where it is hosted.

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BMW Cloud Efficiency Analytics powered by Amazon QuickSight and Amazon Athena

AWS Big Data

The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse. In ELT, raw data is loaded into the data warehouse first, then it’s transformed directly within the warehouse.

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How healthcare organizations can analyze and create insights using price transparency data

AWS Big Data

Due to this low complexity, the solution uses AWS serverless services to ingest the data, transform it, and make it available for analytics. Use the Data Catalog and transform the hospital price transparency data. When the data is available in the Data Catalog, you can develop the analytics query using Athena.

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Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

They use various AWS analytics services, such as Amazon EMR, to enable their analysts and data scientists to apply advanced analytics techniques to interactively develop and test new surveillance patterns and improve investor protection. Melody Yang is a Senior Big Data Solutions Architect for Amazon EMR at AWS.

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PODCAST: AI for Digital Enterprise – Episode 5: How Intelligent Operations can become prime advantage for enterprises

bridgei2i

My name is Arjun Shenoy, and I help companies succeed in their AI-enabled digital transformation efforts. I’m a director with BRIDGEi2i, and I focus on building assets and solutions that support execution and quick ROI on client’s digital programs. It’s a real-time interaction. Transcript.

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Exploring the AI and data capabilities of watsonx

IBM Big Data Hub

While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases. offers a Prompt Lab, where users can interact with different prompts using prompt engineering on generative AI models for both zero-shot prompting and few-shot prompting.