It is a processing of single input into a multiple output What is a Normalizer transformation? Follow Following. Sign me up. Already have a WordPress. Log in now. Rename fields Step 4.
Transformations Transformations Back Next. Normalizer transformation. The Normalizer transformation is an active transformation that transforms one incoming row into multiple output rows. When the Normalizer transformation receives a row that contains multiple-occurring data, it returns a row for each instance of the multiple-occurring data.
For example, a relational source includes four fields with quarterly sales data. You can configure a Normalizer transformation to generate a separate output row for each quarter. When the Normalizer transformation returns multiple rows from an incoming row, it returns duplicate data for single-occurring incoming columns. The terms normalisation and standardisation are sometimes used interchangeably, but they usually refer to different things.
The goal of applying Feature Scaling is to make sure features are on almost the same scale so that each feature is equally important and make it easier to process by most ML algorithms. This is a dataset that contains an independent variable Purchased and 3 dependent variables Country, Age, and Salary. We can easily notice that the variables are not on the same scale because the range of Age is from 27 to 50, while the range of Salary going from 48 K to 83 K. The range of Salary is much wider than the range of Age.
This will cause some issues in our models since a lot of machine learning models such as k-means clustering and nearest neighbour classification are based on the Euclidean Distance. Focusing on age and salary. The difference in Age contributes less to the overall difference. Therefore, we should use Feature Scaling to bring all values to the same magnitudes and, thus, solve this issue. To do this, there are primarily two methods called Standardisation and Normalisation.
The result of standardization or Z-score normalization is that the features will be rescaled to ensure the mean and the standard deviation to be 0 and 1, respectively. The equation is shown below:. It also returns an index - called GCID we will know later in detail - that identifies the quarter number:. Now that you know the concept of a normalizer, let's see how we can implement this concept using Normalizer transformation. We will take a different data set for our example this time.
Suppose we have the following data in source:. Now below is the screen-shot of a complete mapping which shows how to achieve this result using Informatica PowerCenter Designer. Please click on the above image to enlarge it.
You can see after the Source Qualifier, we have placed the Normalizer transformation. In the next section, I will explain how to set up the properties of the normalizer. First we need to set the number of occurrences property of the Expense head as 3 in the Normalizer tab of the Normalizer transformation.
This is because we have 3 different types of expenses in the given data - Food, Houserent and Transportation. As soon as we set the occurrences to 3, Normalizer will in turn automatically create 3 corresponding input ports in the ports tab along with the other fields e.
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