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Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, [2] which is used to store and process data for applications such as Azure, AdCenter, Bing, MSN, Skype and Windows Live.
Example of a database that can be used by a data lake (in this case structured data) A data lake is a system or repository of data stored in its natural/raw format, [1] usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., [2] and transformed data ...
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.
Azure Data Explorer is a fully-managed [1] big data analytics cloud platform [2] [3] and data-exploration service, [4] developed by Microsoft, [5] [6] that ingests structured, semi-structured (like JSON) and unstructured data (like free-text).
Alpine Data Labs, an analytics interface working with Apache Hadoop and big data; AvocaData, a two sided marketplace allowing consumers to buy & sell data with ease. Azure Data Lake is a highly scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud
Snowflake Inc. is an American cloud-based data storage company. Headquartered in Bozeman, Montana, it operates a platform that allows for data analysis and simultaneous access of data sets with minimal latency. [1] It operates on Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format. This enables faster loading times.
Azure SQL Database includes built-in intelligence that learns app patterns and adapts them to maximize performance, reliability, and data protection. Key capabilities include: Learning of the host app's data access patterns, adaptive performance tuning, and automatic improvements to reliability and data protection.
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