Summary
This chapter explored the need for, and complexity of, analytics within modern data systems. Businesses increasingly rely on data-driven decisions, requiring efficient analysis and processing of large data volumes. The complexity arose from diverse data sources and varied formats, as well as the requirement to process data in near-real time without disrupting live applications. Effective analytical patterns addressed these challenges, ensuring scalability, reliability, and performance.
We dove into various analytical patterns, discussing methods and tools such as AWS Glue, AWS Step Functions, and SQS for seamless integration with DynamoDB. Highlighting AWS Glue’s native DynamoDB connector, which supports rate-limiting and failure handling, the section explained how to leverage it for robust data operations. Additionally, it examined patterns involving Lambda functions, SQS, and Step Functions for managing workflows and facilitating efficient bulk data processing.
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