Optimize Data 360 Ingestion for Cost Efficiency

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Data 360 is a powerful platform for ingesting, processing, unifying, and activating customer data from various sources. However, architecture choices can significantly impact credit consumption. Organizations can incur unnecessary costs through inefficient ingestion, processing, and consumption strategies. In Data 360, many usage types calculate credit consumption based on records processed or accessed, rather than just the number of rows returned. This means inefficient pipelines that scan millions of rows but return only a handful of rows can consume credits without delivering proportionate business value. By aligning ingestion choices with row-processing mechanics, architects can significantly lower credit consumption while maintaining system performance. This post explores high-impact strategies for reducing rows processed across the ingestion pipeline to optimize data volume and manage costs. The choice of ingestion method influences the number of rows processed by Data 360, which serves as the primary driver of  credit consumption. Any data filtered or kept outside the platform incurs zero processing cost. Standard ingestion routes duplicate and register every row within the data lakehouse, whereas zero-copy architectures allow querying without replicating it.

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