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The Northwestern Medical Enterprise Data Warehouse (NMEDW) is a Northwestern University Clinical and Translational Sciences Institute (NUCATS) initiative funded jointly by the Northwestern University Feinberg School of Medicine (FSM), Northwestern Medical Faculty Foundation (NMFF), and Northwestern Memorial Hospital (NMH). Its mission is to create a single, comprehensive, and integrated repository of all medical data sources on the campus to facilitate operational, patient care, and research reporting. The EDW has been developed completely in-house, with no consulting fees.

Recent Posts

  1. ETL Assistant – Getting Error Row Description and Column Information Dynamically

    SSIS does a fine job at letting you manage “garden path” ETL, but many face the challenge of how to manage row failures. Which row failed? Why did it fail? Error row handling is a central part of any development task and usually winds up representing a significant chunk of your time and code. In this article we’ll step you through how to overcome SSIS’s design-time-only availability of error row information by creating a runtime dynamic error row handler using CozyRoc’s tool kit for SSIS. Continue reading

  2. ETL Assistant – Using CozyRoc’s Parallel Loop Task

    In our continuing series on building your own templates, reusable “point-and-click” ETL solution using CozyRoc’s components for Microsoft SSIS, we’ll be stepping into the “Parallel Loop Task”. Microsoft’s default loop task is serial. If you ever have need to parallelize execution of SSIS tasks, overcoming this can be quite a challenge. With the Parallel Loop Task you can, with a few quick clicks, enable parallel execution of tasks and packages to maximize the utilization of your available resources. Continue reading

  3. Introducing ETL Assistant – A Dynamic, Templated Approach to SSIS

    Learn how we approached extraction, transformation, and loading (ETL) of data using CozyRoc’s tool kit for Microsoft SQL Server Integration Services (SSIS). We’ll step you through the basic concepts, building blocks, and stumbling points of constructing your own “point-and-click” loading between a SQL-based source and destination. Cut down the time you spend building and maintaining SSIS packages so you can get back to “real work” at your data warehouse. Continue reading