Sql for INSERT else UPDATE

       Select
             Case When E.Empid Is Not Null Then ‘UPDATE’ Else ‘INSERT’ End Flag,
              E1.Empid,   E1.Location
         From
                   Empsource E1
         Left Outer Join
                   Emptarget E
                On E.Empid = E1.Empid

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Data virtualization

Data virtualization is a process of offering a data access interface that hides the technical aspects of stored data, such as location, storage structure, API, access language, and storage technology. Analogous to concept of views in databases Data virtualization tools come with capabilities of data integration, data federation, and data modeling Requires more memory caching Can integrate several data marts or data warehouses through a single data virtualization layer This concept and software is a subset of data integration and is commonly used within business intelligence, service-oriented architecture data services, cloud computing, enterprise search, and master data management. Composite, Denodo, and Informatica are the largest players in the area of data virtualization References for definition: http://www.b-eye-network.com/view/14815

Difference between server jobs and parallel jobs in Datastage

Server job stages do not have in built partitioning and parallelism mechanism for extracting and loading data between different stages. To enhance the speed and performance in server jobs is to - Enable inter process row buffering through the administrator. This helps stages to exchange data as soon as it is available in the link. - Using IPC stage also helps one passive stage read data from another as soon as data is available. In other words, stages do not have to wait for the entire set of records to be read first and then transferred to the next stage. - Link partitioner and link collector stages can be used to achieve a certain degree of partitioning paralellism. All of the above features which have to be explored in server jobs are built in datastage Parallel jobs . The Px engine runs on a multiprocessor sytem and takes full advantage of the processing nodes defined in the configuration file. Both SMP and MMP archi...

Find Changed Data by computing Checksum using MD5 function in Informatica

Introduction: Capturing and preserving the state of data across time is one of the core functions of a data warehouse, but CDC can be utilized in any database or data integration tool. There are many methodologies such as Timestamp, Versioning, Status indicators, Triggers and Transaction logs exists but MD5 function outlines on working with Checksum. Overview: MD5 stands for Message Digest 5 algorithm.It calculates the checksum of the input value using a cryptographic Message-Digest algorithm 5 and returns a128-bit 32 character string of hexadecimal digits (0 - F). Advantage of using MD5 function is that, it will reduce overall ETL run time and also reduces cache memory usage by caching only required fields which are utmost necessary. Implementation Steps : Identify the ports from the source which are subjected to change. Concatenate all the ports and pass them as parameter to MD5 function in expression transformation Map the MD5 function output to a checksum outp...

Datawarehousing Concepts and Tools

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