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Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease. Key storage solutions include: Data Lakes: Centralised repositories that store raw data in its native format until needed for analysis.
Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease. Key storage solutions include: Data Lakes: Centralised repositories that store raw data in its native format until needed for analysis.
There are a number of tools that can help with streaming data collection and processing, some popular ones include: ApacheKafka : An open-source, distributed event streaming platform that can handle millions of events per second. Azure Stream Analytics : A cloud-based service that can be used to process streaming data in real-time.
Technologies like ApacheKafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. Activity Schema Processing : To capture and process customer activities, you might use a stream processing technology like ApacheKafka or Apache Flink.
According to recent statistics, 56% of healthcare organisations have adopted predictiveanalytics to improve patient outcomes. Real-Time Data Processing The demand for real-time analytics is growing as businesses seek immediate insights to drive decision-making. Additionally, familiarity with cloud platforms (e.g.,
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