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  • Metadata-Driven Design: Creating an User-Friendly Enterprise DSL

    What if we could create a language that could be easily understood by the layman but yet enforce those rules that apply to our business domain? What if a snippet of this language could then be interpreted and performed at runtime, without the need for recompilation or redeployment of the system? Aaron Kendall shows how to build such a domain-specific language for a saavy but non-technical crowd.

  • Metadata-Driven Design: Building Web APIs for Dynamic Mobile Apps

    More than ten years ago, software architect Kevin Perera invented a design method for architectures that was called "metadata-driven design and development". In this article, Aaron Kendall explains how to use this design method and outlines similarities as well as differences to current techniques like RESTful services or HATEOAS by implementing a metadata-driven mobile application.

  • Metadata-Driven Design: Designing a Flexible Engine for API Data Retrieval

    Bulk data is commonly accessed via files & FTP. As the world moves toward APIs to facilitate collaboration, what are the requirements for data APIs? This article describes a meta-data driven architecture for bulk data ingestion. Two APIs operate in parallel to provide data changes as well as the data records themselves. An example demonstrates how API responses are parameterized using meta-data.

  • Metadata Driven Design - An Agile Bridge Between Design and Development

    Over the years Agile has had a number of high profile successes. However, some have suggested that project efficiency and team creativity are impossible to achieve simultaneously. In this article Aaron Kendall of Barnes & Noble discusses a method they have been using that attempts to address these potential conflicts and combines the design of the overall architecture and implementation.

  • Apache MetaModel – Providing Uniform Data Access Across Various Data Stores

    MetaModel - an Apache Incubator project – is a Java library used to browse, query and update various types of data stores including traditional SQL databases, unusual stores such as CSV or Excel, or the more modern NoSQL stores in a uniform and programmatic way.

  • Hadoop and Metadata (Removing the Impedance Mis-match)

    A new Apache HCatalog project is a table and storage management layer for Hadoop that enables different data processing tools – Pig, MapReduce, and Hive – to more easily inter-operate data. HCatalog’s presents users with a relational view of data and ensures that users need not worry about where or in what format their data is stored – RCFile format, text files, or sequence files.

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