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

We also share a Spark benchmark solution that suits all Amazon EMR deployment options, so you can replicate the process in your environment for your own performance test cases. 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.

Testing 101
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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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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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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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Stream real-time data into Apache Iceberg tables in Amazon S3 using Amazon Data Firehose

AWS Big Data

Choose Send data. Querying with Athena: You can query the data you’ve written to your Iceberg tables using different processing engines such as Apache Spark, Apache Flink, or Trino. In Transform records , select Turn on data transformation. For Source , select Direct PUT. For Version select $LATEST.

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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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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Prompt with no metadata For the first test, we used a basic prompt containing just the SQL generating instructions and no table metadata. After getting familiar with generative AI applications, see the GitHub Text-to-SQL workshop to learn more text-to-SQL techniques.

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