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Amazon Kinesis is a family of services provided by Amazon Web Services (AWS) for processing and analyzing real-time streaming data at a large scale. Launched in November 2013, it offers developers the ability to build applications that can consume and process data from multiple sources simultaneously. [2]
AWS Glue is an event-driven, serverless computing platform provided by Amazon as a part of Amazon Web Services. It was introduced in August 2017. It was introduced in August 2017. [ 2 ]
The AWS Cloud Development Kit (AWS CDK) is an open-source [1] software development framework developed by Amazon Web Services (AWS) for defining and provisioning cloud infrastructure resources using familiar programming languages. [2]
Amazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services. [1] It is built on top of technology from the massive parallel processing (MPP) data warehouse company ParAccel (later acquired by Actian ), [ 2 ] to handle large scale data sets and database migrations .
Enterprise Integration Patterns is a book by Gregor Hohpe and Bobby Woolf which describes 65 patterns for the use of enterprise application integration and message-oriented middleware in the form of a pattern language.
Learning Tools Interoperability (LTI) is a standard developed by 1EdTech formerly known as IMS Global Learning Consortium at the time of creation. It enables seamless integration between learning systems and external systems. [1] In its current version, v1.3, this is done using OAuth2, OpenID Connect, and JSON Web Tokens.
Enterprise Application Integration is related to middleware technologies such as message-oriented middleware , and data representation technologies such as XML or JSON. Other EAI technologies involve using web services as part of service-oriented architecture as a means of integration. Enterprise Application Integration tends to be data centric.
Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. [1] It can be used to deploy ML models on embedded systems and edge-devices. [2] [3] The platform was launched in November 2017. [4]