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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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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

Metadata store – We use Spark’s in-memory data catalog to store metadata for TPC-DS databases and tables— spark.sql.catalogImplementation is set to the default value in-memory. 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.

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Build a data lake with Apache Flink on Amazon EMR

AWS Big Data

With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos. Refer to Catalogs for more information.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering.

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The Modern Data Stack Explained: What The Future Holds

Alation

In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. What Is the Modern Data Stack? The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform.