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  1. Preface
  2. Part 1: Introduction to Profiles
  3. Part 2: Profiling with Informatica Analyst
  4. Part 3: Profiling with Informatica Developer

Profile Guide

Profile Guide

Sampling Options

Sampling Options

Sampling options determines the number of rows on which the Developer tool runs a profile. You can configure sampling options when you define a profile or run a profile.
The following table describes the sampling options for a profile:
Property
Description
All Rows
Runs a profile on all the rows in the data object.
Supported on Native, Blaze, Spark, and Databricks run-time environment.
Sample First <number> rows
Runs a profile on the sample rows from the beginning of the rows in the data object. You can choose a maximum of 2,147,483,647 rows.
Supported on Native and Blaze run-time environment.
Random Sample <number> rows
Runs a profile on a randomly picked number of the rows in the data object. You can choose a maximum of 2,147,483,647 rows.
Supported on Native and Blaze run-time environment.
Random Sample (Auto)
Runs a profile on the sample rows computed on the basis of the number of rows in the data object.
Supported on Native and Blaze run-time environment.
Limit n <number> rows
Runs a profile based on the number of rows in the data object. When you choose to run a profile in the Hadoop validation environment, Spark engine collects samples from multiple partitions of the data object and pushes the samples to a single node to compute sample size. The Limit n sampling option supports Oracle, SQL Server, and DB2 databases. You cannot apply the Advanced filter with the Limit n sampling option.
Supported on Spark run-time environment.
Random Percentage
Runs a profile on a percentage of rows in the data object.
Supported on Spark and Databricks run-time environment.
Exclude approved data types and data domains from the data type and data domain inference in the subsequent profile runs
Excludes the approved data type or data domain from data type and data domain inference from the next profile run.
After you choose to run the profile on a random sample of rows, the random sample algorithm chooses the rows at random in the data object to run the profile on. When you choose a random sampling option for column profiles, the Developer tool performs drilldown on the staged data. This can impact the drill-down performance. When you choose a random sampling option for data domain discovery profiles, the Developer tool performs drill down on live data.

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