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The architecture is divided into two main categories: data at rest and data in motion. Data at Rest This includes storage solutions such as S3 DataWarehouse and Cassandra. These systems handle the storage costs associated with keeping vast amounts of content and user data.
Log Analysis These are well-suited for analysing log data from various sources, such as web servers, application logs, and sensor data, to gain insights into user behaviour and system performance. Organisations that require low-latency data analysis may find Hadoop insufficient for their needs.
Technologies like ApacheKafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. Here’s how a composable CDP might incorporate the modeling approaches we’ve discussed: Data Storage and Processing : This is your foundation.
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