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Making OT-IT integration a reality with new data architectures and generative AI

CIO Business Intelligence

The data transformation imperative What Denso and other industry leaders realise is that for IT-OT convergence to be realised, and the benefits of AI unlocked, data transformation is vital. The company can also unify its knowledge base and promote search and information use that better meets its needs.

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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

ElastiCache manages the real-time application data caching, allowing your customers to experience microsecond response times while supporting high-throughput handling of hundreds of millions of operations per second. In the inventory management and forecasting solution, AWS Glue is recommended for data transformation.

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Improve observability across Amazon MWAA tasks

AWS Big Data

To run the scripts, refer to the Amazon MWAA analytics workshop. format(S3_BUCKET_NAME), 's3://{}/data/aggregated/green'.format(S3_BUCKET_NAME), To learn more and get hands-on experience, start with the Amazon MWAA analytics workshop and then use the scripts in the GitHub repo to gain more observability of your DAG run.

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Data Empowerment Fueled by Self-Service

erwin

A key trend proving successful in data empowerment is investing in self-service technology. Self-service done right can enable a new level of productivity and operational efficiency to fuel the next generation of data transformation. What is data empowerment? They are improving the accessibility of available technology.

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Amazon EMR on EKS widens the performance gap: Run Apache Spark workloads 5.37 times faster and at 4.3 times lower cost

AWS Big Data

To learn more and get started with EMR on EKS, try out the EMR on EKS Workshop and visit the EMR on EKS Best Practices Guide page. About the Authors Melody Yang is a Senior Big Data Solution Architect for Amazon EMR at AWS. Her areas of interests are open-source frameworks and automation, data engineering and DataOps.

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Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

With these features, you can now build data pipelines completely in standard SQL that are serverless, more simple to build, and able to operate at scale. Typically, data transformation processes are used to perform this operation, and a final consistent view is stored in an S3 bucket or folder.

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Orchestrate Amazon EMR Serverless jobs with AWS Step functions

AWS Big Data

Prerequisites Before you get started, make sure you have the following prerequisites: An AWS account An IAM user with administrator access An S3 bucket Solution Architecture To automate the complete process, we use the following architecture, which integrates Step Functions for orchestration and Amazon EMR Serverless for data transformations.

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