Near real-time lakehouse data processing - Azure Architecture . . . This article describes an end-to-end solution for near real-time data processing to keep lakehouse data in sync The solution uses Azure Event Hubs, Azure Synapse Analytics, and Azure Data Lake Storage for data processing and analytics
Real-Time Data Processing in Hybrid Cloud Systems . . . Real-time data processing enables organizations to derive insights and make decisions based on live data, enhancing the responsiveness of applications and services This paper
What Is A Real-Time Data Lake: Purpose, Example Use Cases A real-time data lake plays a big role in how we use data today They offer fast access to data which helps businesses make quick and smart choices You can use a real-time data lake to manage different kinds of data, generate immediate insights, and ultimately save money, and grow your business
Implementing Multi-Cloud and Hybrid Data Lakes: A Tech. Guide A hybrid data lake allows companies to store sensitive or regulated data on-premises while leveraging the cloud for analytics and big data processing Multi-cloud strategies offer region-based storage options, ensuring compliance with data sovereignty laws
8 Reasons Why Data Lakes Are Shifting to a Hybrid Cloud - Atlan The data can be ingested in real-time or batch processes and can be stored as-is without the need to convert it to a specific schema Data lakes in the cloud offer several benefits which include: Scalability: You can quickly scale your storage capacity up or down based on your needs
Hybrid Data Lakehouse: Benefits and Architecture Overview By leveraging the cloud’s scalability for processing large volumes of streaming data and the low-latency capabilities of on-premises infrastructure, a hybrid data lakehouse ensures that real-time analytics are efficient and effective