The Future for Informatica - Anil Chakravarthy and Charles Race discuss ...
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Time Series Decomposition
A time series decomposition is a mathematical procedure which transform a original time series into 3 sub-time series. Seasonal: Patterns which repeat with fixed period of time. User page visits to a website increases in the weekends, so a seasonality of 7 days is observed. Trend: Underlying trend of metrics like User page visit count. Random: Residual of time series after allocation into seasonal and trends time series. There are two types of decomposition: Additive and Multiplicative. They have to be chosen correctly for successful decomposition. Additive: The magnitude of seasonality remains constant when time series increases. Time series = Seasonal + Trend + Random Multiplicative: The magnitude of seasonality varies constantly with time series. Time series = Trend * Seasonal *Random
XML Analyzer for Informatica PowerCenter
This is a tool that should help to quickly fetch some useful information from Mapping or Workflow XML file and display them in a business-user friendly format. Features: * Ability to download each source-to-target mapping dependency analysis as tab delimited file (NEW!) * Full Mapplet support * Reporting transformation ports with default names * Transformation-level naming convention * Reporting any implicit datatype conversions * Reporting Precision / Scale mismatches between linked ports * List Workflow variables * List Mapping variables * Checking for variables unused in mapping * Added source and target data type to source-to-target column mappings * Reporting Session level SQL overrides with corresponding Mapping level SQLs * Listing all source-to-target column mappings * Comparing workflow log file with workflow name * Checking if a session is valid * Checking if a mapping is valid * Tracing level * Overridden ...
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