A Simplified Guide to Cloud Data Platform Architecture

The world's first hyper-scale public cloud provider, Amazon Web Services (AWS), was launched in 2006, leading to thousands of businesses shifting their on-premise data storage and analytics workloads into the cloud by architecting or adopting a cloud data platform. As enterprise data continues to grow in volume, variety, and velocity, complex cloud data platforms are becoming increasingly time-consuming and costly to manage. To simplify cloud data platform architectures, reduce management costs, and eliminate complexity, data executives are searching for ways to modernize their cloud data platforms. A cloud data platform is an integrated software solution that enables enterprise organizations to aggregate, store, process, and consume data in the cloud. Public cloud services provide cost-effective object storage for enterprise data, along with scalable compute resources and proprietary cloud services. Cloud data warehouses capture data from multiple sources, aggregate it into cloud object storage, normalize and transform it, and store it in a relational format to support business intelligence and analytics use cases. A simplified cloud data platform architecture crushes complexity and democratizes data access across multiple analytics use cases, with features such as batch and stream data ingestion, tiered data storage capabilities, workload orchestration, metadata layer, and ETL tools. Sophisticated cloud data platforms offer additional features that go beyond the four basic layers of platform architecture, including support for real-time analytics use cases, low-latency access, and performant analytics. Examples of sophisticated cloud data platform architectures include Google Cloud Platform's Marketing Analytics Reference Architecture and AWS Data Lakehouse Architecture, which feature a data ingestion layer, data storage layer, data processing layer, and data serving layer. ChaosSearch delivers a simplified cloud data platform architecture that reduces the cost and complexity of cloud data analytics at scale, enabling use cases such as security operations, application troubleshooting, and monitoring cloud services.

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