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Orchestrate Amazon EMR Serverless jobs with AWS Step functions

AWS Big Data

The integration between AWS Step Functions and Amazon EMR Serverless makes it easier to manage and orchestrate big data workflows. Karthik Prabhakar is a Senior Big Data Solutions Architect for Amazon EMR at AWS. Now, with the support for “Run a Job (.sync)” Summarized output is then written to Amazon S3 bucket.

Big Data 103
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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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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

Amazon EMR on EKS provides a deployment option for Amazon EMR that allows organizations to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). 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. Amazon EMR 6.10

Testing 71
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The Modern Data Stack Explained: What The Future Holds

Alation

Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? Reverse ETL tools.

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Extract time series from satellite weather data with AWS Lambda

AWS Big Data

It has not been specifically designed for heavy data transformation tasks. It’s scalable and cost-effective, and can be adapted to other ETL and data processing use cases. You can find hands-on labs to improve your knowledge with AWS Workshops. You also use AWS Glue to consolidate the files produced by the parallel tasks.

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

AWS Big Data

The Orca Platform is powered by a state-of-the-art anomaly detection system that uses cutting-edge ML algorithms and big data capabilities to detect potential security threats and alert customers in real time, ensuring maximum security for their cloud environment. This ensures that the data is suitable for training purposes.