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This approach goes a long way toward explaining how Amazon was able to externalize its computing infrastructure — first to Merchant.com, the company’s ecommerce-as-a-service platform for retailers to build their own online stores, and then to Amazon WebServices, a broader offering that has since taken a life of its own.
Data as a service (DaaS) is a data management approach that uses the cloud to offer storage, integration, processing, and analytics capabilities through a network connection. The DaaS architecture is based on a cloud-based system that supports Webservices and service-oriented architecture (SOA).
This support enables interoperability with leading commercial Hadoop offerings, such as IBM InfoSphere® BigInsights™, Cloudera, Apache, Hortonworks, and Amazon WebServices Elastic Map-Reduce, along with popular analytic data stores, including IBM DB2® BLU and SAP HANA. The Cognos platform supports Big SQL, Apache Hive, ODBC and JDBC.
Evolving technology architecture. Further squeezing the market, custom software, packaged applications, and software as a service (SaaS) are now available to solve business problems once addressed with labor-based services. One important technology transformation involves the construction of information architecture.
To our shareowners: Random forests, naïve Bayesian estimators, RESTful services, gossip protocols, eventual consistency, data sharding, anti-entropy, Byzantine quorum, erasure coding, vector clocks. Look inside a current textbook on software architecture, and youll find few patterns that we dont apply at Amazon.
The first of these has progressed rapidly, while the professional services market is earlier in its transformation. IT executives now refers to SOA (Service Oriented Architecture) rather than webservices, while online delivery of software is usually called SaaS (Software as a Service) instead of the ASP (Application Service Provider) model.
Implementing an Enterprise Data Hub — Technical perspectives for implementing enterprise data hub architectures, converged analytics for workflow optimization, and the essential role of open standards and frameworks to ensure continuous innovation. Closing RemarksWebster Mudge. Senior Director of Technology Solutions, Cloudera.
Implementing an Enterprise Data Hub — Technical perspectives for implementing enterprise data hub architectures, converged analytics for workflow optimization, and the essential role of open standards and frameworks to ensure continuous innovation. Closing RemarksWebster Mudge. Senior Director of Technology Solutions, Cloudera.
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