Teradata interview questions, FAQs

Informatica Powercenter Partitioning

Informatica PowerCenter Partitioning Option increases the performance of PowerCenter through parallel data processing. This option provides a thread-based architecture and automatic data partitioning that optimizes parallel processing on multiprocessor and grid-based hardware environments. Introduction: With the Partitioning Option, you can execute optimal parallel sessions by dividing data processing into subsets that are run in parallel and spread among available CPUs in a multiprocessor system. When different processors share the computational load,large data volumes can be processed faster. When sourcing and targeting relational databases, the Partitioning Option enables PowerCenter to automatically align its partitions with database table partitions to improve performance. Unlike approaches that require manual data partitioning, data integrity is automatically guaranteed because the parallel engine of PowerCenter dynamically realigns data partitions for set-oriented trans

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 architecture is supported by datastage Px. Px ta

Datawarehousing Concepts and Tools

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