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2. Azure Data Lake - Wikipedia

   en.wikipedia.org/wiki/Azure_Data_Lake

   azure.microsoft.com /en-us /solutions /data-lake / Azure Data Lake [ 1 ] is a scalable data storage and analytics service. The service is hosted in Azure , Microsoft's public cloud .

3. Microsoft Azure - Wikipedia

   en.wikipedia.org/wiki/Microsoft_Azure

   Azure Data Factory is a data integration service that allows creation of data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. [30] Azure Data Lake is a scalable data storage and analytic service for big data analytics workloads that require developers to run massively parallel queries.

4. Cosmos DB - Wikipedia

   en.wikipedia.org/wiki/Cosmos_DB

   It is designed to provide high availability, scalability, and low-latency access to data for modern applications. Unlike traditional relational databases, Cosmos DB is a NoSQL (meaning "Not only SQL", rather than "zero SQL") and vector database, [ 1 ] which means it can handle unstructured, semi-structured, structured, and vector data types.

5. Distributed data store - Wikipedia

   en.wikipedia.org/wiki/Distributed_data_store

   A distributed data store is a computer network where information is stored on more than one node, often in a replicated fashion. [1] It is usually specifically used to refer to either a distributed database where users store information on a number of nodes , or a computer network in which users store information on a number of peer network nodes .

6. Distributed file system for cloud - Wikipedia

   en.wikipedia.org/wiki/Distributed_file_system...

   GFS is a scalable distributed file system for data-intensive applications. It provides fault-tolerant, high-performance data storage a large number of clients accessing it simultaneously. GFS uses MapReduce, which allows users to create programs and run them on multiple machines without thinking about parallelization and load-balancing issues ...

7. Hyperscale computing - Wikipedia

   en.wikipedia.org/wiki/Hyperscale_computing

   In computing, hyperscale is the ability of an architecture to scale appropriately as increased demand is added to the system. This typically involves the ability to seamlessly provide and add compute, memory, networking, and storage resources to a given node or set of nodes that make up a larger computing, distributed computing, or grid computing environment.

8. Intel Eyes Increased AI Product Demand, Shifts Focus to ... - AOL

   www.aol.com/finance/intel-eyes-increased-ai...

   Crowell noted that demand for traditional data centers might slow as companies seek a balance betwee Intel Eyes Increased AI Product Demand, Shifts Focus to Private Data Storage Solutions Skip to ...

9. Azure Data Explorer - Wikipedia

   en.wikipedia.org/wiki/Azure_Data_Explorer

   In Azure Data Explorer, unlike a typical relational database management systems (RDBMS), there are no constraints like key uniqueness, primary and foreign key. [26] The necessary relationships are established at the query time. [27] The data in Azure Data Explorer generally follows this pattern: [28] Creating Database, Ingesting data, Query the ...