Remove Big Data Remove Data Transformation Remove Testing Remove Workshop
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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). The solution uses the TPC-DS dataset and unmodified data schema and table relationships, but derives queries from TPC-DS to support the SparkSQL test cases.

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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. To load the time series for a specific point into a pandas data frame, you can use the awswrangler library from your Python code: import awswrangler as wr import pandas as pd # Retrieving the data directly from Amazon S3 df = wr.s3.read_parquet("s3://

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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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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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Use Snowflake with Amazon MWAA to orchestrate data pipelines

AWS Big Data

If you’re testing on a different Amazon MWAA version, update the requirements file accordingly. For testing purposes, you can choose Add permissions and add the managed AmazonS3FullAccess policy to the user instead of providing restricted access. The requirements file is based on Amazon MWAA version 2.6.3.

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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.