Summary
In this chapter, we expanded our understanding of NoSQL, emphasizing the distinctive design patterns between RDBMS and NoSQL, with a specific focus on DynamoDB. We explored strategies such as duplication and denormalization, which optimize for compute over storage. This approach minimizes expensive runtime joins and prioritizes efficient data retrieval.
While discussing denormalization, we delved into strategies for efficient data modeling in DynamoDB. We highlighted scenarios favoring a single DynamoDB table for related entities versus multiple tables, emphasizing that there is no one-size-fits-all approach. Sometimes, segregating data into separate DynamoDB tables proves more efficient.
Furthermore, we examined the intricacies of breaking down data in NoSQL databases, emphasizing smart partitioning strategies. This section also covered handling LOBs in DynamoDB, detailing strategies based on factors such as latency, update frequency, and application dependencies.
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