Modernizing large-scale data on AWS involves patterns like leveraging purpose-built databases and analytics services, robust data ingestion and processing with ETL/ELT/ETLT, and orchestrating complex data pipelines using services like AWS Glue, Amazon EMR, AWS Fargate, AWS Step Functions, and Amazon Managed Workflows for Apache Airflow.

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<p>This article discusses strategies to overcome barriers and modernize large volumes of data using AWS services. It focuses on patterns for data migration, processing, and orchestration.</p>
<p>Specifically, the article covers:</p>
<ul>
<li>Common challenges in data modernization (people, technology, and process)</li>
<li>Data migration using AWS DMS, SCT, and Snowball for petabyte-scale data</li>
<li>Data processing with AWS Glue, EMR, DataBrew, and purpose-built databases</li>
<li>Data orchestration using AWS Glue Workflows, Managed Airflow, and Step Functions</li>
<li>Conclusion highlighting AWS resources for database migration patterns</li>
</ul>
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Overcoming Barriers to Large-scale Data Modernization

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