Astera Data Stack
Version 10
Version 10
  • Welcome to Astera Data Stack Documentation
  • RELEASE NOTES
    • Astera 10.5 - Release Notes
    • Astera 10.4 - Release Notes
    • Astera 10.3 - Release Notes
    • Astera 10.2 – Release Notes
    • Astera 10.1 - Additional Notes
    • Astera 10.1 - Release Notes
    • Astera 10.0 - Release Notes
  • SETTING UP
    • System Requirements
    • Product Architecture
    • Migrating from Astera 9 to Astera 10
    • Migrating from Astera 7.x to Astera 10
    • Installing Client and Server Applications
    • Connecting to an Astera Server using the Client
    • How to Connect to a Different Astera Server from the Client
    • How to Build a Cluster Database and Create Repository
    • Repository Upgrade Utility in Astera
    • How to Login from the Client
    • How to Verify Admin Email
    • Licensing in Astera
    • How to Supply a License Key Without Prompting the User
    • Install Manager
    • User Roles and Access Control
      • Windows Authentication
      • Azure Authentication
    • Offline Activation of Astera
    • Setting Up R in Astera
    • Silent Installation
  • DATAFLOWS
    • What are Dataflows?
    • Sources
      • Data Providers and File Formats Supported in Astera Data Stack
      • Setting Up Sources
      • Excel Workbook Source
      • COBOL File Source
      • Database Table Source
      • Delimited File Source
      • File System Items Source
      • Fixed Length File Source
      • Email Source
      • Report Source
      • SQL Query Source
      • XML/JSON File Source
      • PDF Form Source
      • Parquet File Source (Beta)
      • MongoDB Source (Beta)
      • Data Model Query
    • Transformations
      • Introducing Transformations
      • Aggregate Transformation
      • Constant Value Transformation
      • Denormalize Transformation
      • Distinct Transformation
      • Expression Transformation
      • Filter Transformation
      • Join Transformation
      • List Lookup Transformation
      • Merge Transformation
      • Normalize Transformation
      • Passthru Transformation
      • Reconcile Transformation
      • Route Transformation
      • Sequence Generator
      • Sort Transformation
      • Sources as Transformations
      • Subflow Transformation
      • Switch Transformation
      • Tree Join Transformation
      • Tree Transform
      • Union Transformation
      • Data Cleanse Transformation
      • File Lookup Transformation
      • SQL Statement Lookup
      • Database Lookup
      • AI Match Transformation
    • Destinations
      • Setting Up Destinations
      • Database Table Destination
      • Delimited File Destination
      • Excel Workbook Destination
      • Fixed Length File Destination
      • SQL Statement Destination
      • XML File Destination
      • Parquet File Destination (Beta)
      • Excel Workbook Report
      • MongoDB Destination
    • Data Logging and Profiling
      • Creating Data Profile
      • Creating Field Profile
      • Data Quality Mode
      • Using Data Quality Rules in Astera
      • Record Level Log
      • Quick Profile
    • Database Write Strategies
      • Data Driven
      • Source Diff Processor
      • Database Diff Processor
    • Text Processors
      • Delimited Parser
      • Delimited Serializer
      • Language Parser
      • Fixed Length Parser
      • Fixed Length Serializer
      • XML/JSON Parser
      • XML/JSON Serializer
    • Data Warehouse
      • Fact Table Loader
      • Dimension Loader
      • Data Vault Loader
    • Testing and Diagnostics
      • Correlation Analysis
    • Visualization
      • Basic Plots
      • Distribution Plots
    • EDI
      • EDI Source File
      • EDI Message Parser
      • EDI Message Serializer
      • EDI Destination File
  • WORKFLOWS
    • What are Workflows?
    • Creating Workflows in Astera
    • Decision
    • EDI Acknowledgment
    • File System
    • File Transfer
      • FTP
      • SFTP
    • Or
    • Run Dataflow
    • Run Program
    • Run SQL File
    • Run SQL Script
    • Run Workflow
    • Send Mail
    • Workflows with a Dynamic Destination Path
    • Customizing Workflows With Parameters
    • GPG-Integrated File Decryption in Astera
    • AS2
      • Setting up an AS2 Server
      • Adding an AS2 Partner
      • AS2 Workflow Task
  • Subflows
    • Using Subflows in Astera
  • DATA MODEL
    • Creating a Data Warehousing Project
    • Data Models
      • Introducing Data Models
      • Opening a New Data Model
      • Data Modeler - UI Walkthrough
      • Reverse Engineering an Existing Database
      • Creating a Data Model from Scratch
      • General Entity Properties
      • Creating and Editing Relationships
      • Relationship Manager
      • Virtual Primary Key
      • Virtual Relationship
      • Change Field Properties
      • Forward Engineering
      • Verifying a Data Model
    • Dimensional Modelling
      • Introducing Dimensional Models
      • Converting a Data Model to a Dimensional Model
      • Build Dimensional Model
      • Fact Entities
      • Dimension Entities
      • Placeholder Dimension for Early Arriving Facts and Late Arriving Dimensions
      • Date and Time Dimension
      • Aggregates in Dimensional Modeling
      • Verifying a Dimensional Model
    • Data Vaults
      • Introducing Data Vaults
      • Data Vault Automation
      • Raw Vault Entities
      • Bridge Tables
      • Point-In-Time Tables
    • Documentation
      • Generating Technical and Business Documentation for Data Models
      • Lineage and Impact Analysis
    • Deployment and Usage
      • Deploying a Data Model
      • View Based Deployment
      • Validate Metadata and Data Integrity
      • Using Astera Data Models in ETL Pipelines
      • Connecting an Astera Data Model to a Third-Party Visualization Tool
  • REPORT MODEL
    • User Guide
      • Report Model Tutorial
    • Report Model Interface
      • Report Options
      • Report Browser
      • Data Regions in Report Models
      • Region Properties Panel
      • Pattern Properties
      • Field Properties Panel
    • Use Cases
      • Auto-Creating Data Regions and Fields
      • Line Count
      • Auto-Parsing
      • Pattern Count
      • Applying Pattern to Line
      • Regular Expression
      • Floating Patterns and Floating Fields
      • Creating Multi-Column Data Regions
      • Defining the Start Position of Data Fields
      • Data Field Verification
      • Using Comma Separated Values to Define Start Position
      • Defining Region End Type as Specific Text and Regular Expression
      • How To Work With PDF Scaling Factor in a Report Model
      • Connecting to Cloud Storage
    • Auto Generate Layout
      • Setting Up AGL in Astera
      • UI Walkthrough - Auto Generation of Layout, Fields and Table
      • Using Auto Generation Layout, Auto Create Fields and Auto Create Table (Preview)
    • AI Powered Data Extraction
      • AI Powered Data Extraction Using Astera North Star
      • Best Practices for AI-Powered Template Creation in Astera
    • Optical Character Recognition
      • Loading PDFs with OCR
      • Best Practices for OCR Usage
    • Exporting Options
      • Exporting a Report Model
      • Exporting Report Model to a Dataflow
    • Miscellaneous
      • Importing Monarch Models
      • Microsoft Word and Rich Text Format Support
      • Working With Problematic PDF Files
  • API Flow
    • API Publishing
      • Develop
        • Designing an API Flow
        • Request Context Parameters
        • Configuring Sorting and Filtering in API Flows
        • Enable Pagination
        • Asynchronous API Request
        • Multiple Responses using Conditional Route
        • Workflow Tasks in an API Flow
        • Enable File Download-Upload Through APIs
        • Database CRUD APIs Auto-Generation
        • Pre-deployment Testing and Verification of API flows
        • Multipart/Form-Data
        • Certificate Store
      • Publish
        • API Deployment
        • Test Flow Generation
      • Manage
        • Server Browser Functionalities for API Publishing
          • Swagger UI for API Deployments
        • API Monitoring
        • Logging and Tracing
    • API Consumption
      • Consume
        • API Connection
        • Making API Calls with the API Client
        • API Browser
          • Type 1 – JSON/XML File
          • Type 2 – JSON/XML URL
          • Type 3 – Import Postman API Collections
          • Type 4 - Create or customize API collection
          • Pre-built Custom Connectors
        • Request Service Options - eTags
        • HTTP Redirect Calls
        • Method Operations
        • Pagination
        • Raw Preview And Copy Curl Command
        • Support for text/XML and SOAP Protocol
        • API Logging
        • Making Multipart/Form-Data API Calls
      • Authorize
        • Open APIs - Configuration Details
        • Authorizing Facebook APIs
        • Authorizing Astera’s Server APIs
        • Authorizing Avaza APIs
        • Authorizing the Square API
        • Authorizing the ActiveCampaign API
        • Authorizing the QuickBooks’ API
        • Astera’s Server API Documentation
        • NTLM Authentication
        • AWS Signature Authentication
        • Accessing Astera’s Server APIs Through a Third-Party Tool
          • Workflow Use Case
  • Project Management and Scheduling
    • Project Management
      • Deployment
      • Server Monitoring and Job Management
      • Cluster Monitor and Settings
      • Connecting to Source Control
      • Astera Project and Project Explorer
      • CAR Convert Utility Guide
    • Job Scheduling
      • Scheduling Jobs on the Server
      • Job Monitor
    • Configuring Multiple Servers to the Same Repository (Load Balancing)
    • Purging the Database Repository
  • Data Governance
    • Deployment of Assets in Astera Data Stack
    • Logging In
    • Tags
    • Modifying Asset Details
    • Data Discoverability
    • Data Profile
    • Data Quality
    • Scheduler
    • Access Management
  • Functions
    • Introducing Function Transformations
    • Custom Functions
    • Logical
      • Coalesce (Any value1, Any value2)
      • IsNotNull (AnyValue)
      • IsRealNumber (AnyValue)
      • IsValidSqlDate (Date)
      • IsDate (AnyValue)
      • If (Boolean)
      • If (DateTime)
      • If (Double)
      • Exists
      • If (Int64)
      • If (String)
      • IsDate (str, strformat)
      • IsInteger (AnyValue)
      • IsNullOrWhitespace (StringValue)
      • IsNullorEmpty (StringValue)
      • IsNull (AnyValue)
      • IsNumeric (AnyValue)
    • Conversion
      • GetDateComponents (DateWithOffset)
      • ParseDate (Formats, Str)
      • GetDateComponents (Date)
      • HexToInteger (Any Value)
      • ToInteger (Any value)
      • ToDecimal (Any value)
      • ToReal (Any value)
      • ToDate (String dateStr)
      • TryParseDate (String, UnknownDate)
      • ToString (Any value)
      • ToString (DateValue)
      • ToString (Any data, String format)
    • Math
      • Abs (Double)
      • Abs (Decimal)
      • Ceiling (Real)
      • Ceiling(Decimal)
      • Floor (Decimal)
      • Floor (Real)
      • Max (Decimal)
      • Max (Date)
      • Min (Decimal)
      • Min (Date)
      • Max (Real)
      • Max (Integer)
      • Min (Real)
      • Pow (BaseExponent)
      • Min (Integer)
      • RandomReal (Int)
      • Round (Real)
      • Round (Real Integer)
      • Round (Decimal Integer)
      • Round (Decimal)
    • Financial
      • DDB
      • FV
      • IPmt
      • IPmt (FV)
      • Pmt
      • Pmt (FV)
      • PPmt
      • PPmt (FV)
      • PV (FV)
      • Rate
      • Rate (FV)
      • SLN
      • SYD
    • String
      • Center (String)
      • Chr (IntAscii)
      • Asc (String)
      • AddCDATAEnvelope
      • Concatenate (String)
      • ContainsAnyChar (String)
      • Contains (String)
      • Compact (String)
      • Find (Int64)
      • EndsWith (String)
      • FindIntStart (Int32)
      • Extract (String)
      • GetFindCount (Int64)
      • FindLast (Int64)
      • GetDigits (String)
      • GetLineFeed
      • Insert (String)
      • IsAlpha
      • GetToken
      • IndexOf
      • IsBlank
      • IsLower
      • IsUpper
      • IsSubstringOf
      • Length (String)
      • LeftOf (String)
      • Left (String)
      • IsValidName
      • Mid (String)
      • PadLeft
      • Mid (String Chars)
      • LSplit (String)
      • PadRight
      • ReplaceAllSpecialCharsWithSpace
      • RemoveChars (String str, StringCharsToRemove)
      • ReplaceLast
      • RightAlign
      • Reverse
      • Right (String)
      • RSplit (String)
      • SplitStringMultipleRecords
      • SplitStringMultipleRecords (2 Separators)
      • SplitString (3 separators)
      • SplitString
      • SplitStringMultipleRecords (3 Separators)
      • Trim
      • SubString (NoOfChars)
      • StripHtml
      • Trim (Start)
      • TrimExtraMiddleSpace
      • TrimEnd
      • PascalCaseWithSpace (String str)
      • Trim (String str)
      • ToLower(String str)
      • ToProper(String str)
      • ToUpper (String str)
      • Substring (String str, Integer startAt)
      • StartsWith (String str, String value)
      • RemoveAt (String str, Integer startAt, Integer noofChars)
      • Proper (String str)
      • Repeat (String str, Integer count)
      • ReplaceAll (String str, String lookFor, String replaceWith)
      • ReplaceFirst (String str, String lookFor, String replaceWith)
      • RightOf (String str, String lookFor)
      • RemoveChars (String str, String charsToRemove)
      • SplitString (String str, String separator1, String separator2)
    • Date Time
      • AddMinutes (DateTime)
      • AddDays (DateTimeOffset)
      • AddDays (DateTime)
      • AddHours (DateTime)
      • AddSeconds (DateTime)
      • AddMonths (DateTime)
      • AddMonths (DateTimeOffset)
      • AddMinutes (DateTimeOffset)
      • AddSeconds (DateTimeOffset)
      • AddYears (DateTimeOffset)
      • AddYears (DateTime)
      • Age (DateTime)
      • Age (DateTimeOffset)
      • CharToSeconds (Str)
      • DateDifferenceDays (DateTimeOffset)
      • DateDifferenceDays (DateTime)
      • DateDifferenceHours (DateTimeOffset)
      • DateDifferenceHours (DateTime)
      • DateDifferenceMonths (DateTimeOffset)
      • DateDifferenceMonths (DateTime)
      • DatePart (DateTimeOffset)
      • DatePart (DateTime)
      • DateDifferenceYears (DateTimeOffset)
      • DateDifferenceYears (DateTime)
      • Month (DateTime)
      • Month (DateTimeOffset)
      • Now
      • Quarter (DateTime)
      • Quarter (DateTimeOffset)
      • Second (DateTime)
      • Second (DateTimeOffset)
      • SecondsToChar (String)
      • TimeToInteger (DateTime)
      • TimeToInteger (DateTimeOffset)
      • ToDate Date (DateTime)
      • ToDate DateTime (DateTime)
      • ToDateString (DateTime)
      • ToDateTimeOffset-Date (DateTimeOffset)
      • ToDate DateTime (DateTimeOffset)
      • ToDateString (DateTimeOffset)
      • Today
      • ToLocal (DateTime)
      • ToJulianDate (DateTime)
      • ToJulianDayNumber (DateTime)
      • ToTicks (Date dateTime)
      • ToTicks (DateTimeWithOffset dateTime)
      • ToUnixEpoc (Date dateTime)
      • ToUtc (Date dateTime)
      • UnixTimeStampToDateTime (Real unixTimeStamp)
      • UtcNow ()
      • Week (Date dateTime)
      • Week (DateTimeWithOffset dateTime)
      • Year (Date dateTime)
      • Year (DateTimeWithOffset dateTime)
      • DateToJulian (Date dateTime, Integer length)
      • DateTimeOffsetUtcNow ()
      • DateTimeOffsetNow ()
      • Day (DateTimeWithOffset dateTime)
      • Day (Date dateTime)
      • DayOfWeekStr (DateTimeWithOffset dateTime)
      • DayOfWeek (DateTimeWithOffset dateTime)
      • DayOfWeek (Date dateTime)
      • DateToJulian (DateTimeWithOffset dateTime, Integer length)
      • DayOfWeekStr (Date dateTime)
      • FromJulianDate (Real julianDate)
      • DayOfYear (Date dateTime)
      • DaysInMonth(Integer year, Integer month)
      • DayOfYear (DateTimeWithOffset dateTime)
      • FromUnixEpoc
      • FromJulianDayNumber (Integer julianDayNumber)
      • FromTicksUtc(Integer ticks)
      • FromTicksLocal(Integer ticks)
      • Hour (Date dateTime)
      • Hour (DateTimeWithOffset dateTime)
      • Minute (Date dateTime)
      • JulianToDate (String julianDate)
      • Minute (DateTimeWithOffset dateTime)
      • DateToIntegerYYYYMMDD (DateTimeWithOffset dateTime)
      • DateToIntegerYYYYMMDD (Date dateTime)
    • Files
      • AppendTextToFile (String filePath, String text)
      • CopyFile (String sourceFilePath, String destFilePath, Boolean overWrite)
      • CreateDateTime (String filePath)
      • DeleteFile (String filePath)
      • DirectoryExists (String filePath)
      • FileExists (String filePath)
      • FileLength (String filePath)
      • FileLineCount (String filePath)
      • GetDirectory (String filePath)
      • GetEDIFileMetaData (String filePath)
      • GetExcelWorksheets (String excelFilePath)
      • GetFileExtension (String filePath)
      • GetFileInfo (String filePath)
      • GetFileName (String filePath)
      • GetFileNameWithoutExtension (String filePath)
      • LastUpdateDateTime (String filePath)
      • MoveFile (String filePath, String newDirectory)
      • ReadFileBytes (String filePath)
      • ReadFileFirstLine (String filePath)
      • ReadFileText (String filePath)
      • ReadFileText (String filePath, String codePage)
      • WriteBytesToFile (String filePath, ByteArray bytes)
      • WriteTextToFile (String filePath, String text)
    • Date Time With Offset
      • ToDateTimeOffsetFromDateTime (dateTime String)
      • ToUtc (DateTimeWithOffset)
      • ToDateTimeOffsetFromDateTime
      • ToDateTimeOffset (String dateTimeOffsetStr)
      • ToDateTimeFromDateTimeOffset
    • GUID
      • NewGuid
    • Encoding
      • ToBytes
      • FromBytes
      • UrlEncode
      • UrlDecode
      • ComputeSHA256
      • ComputeMD5
      • ComputeHash (Str, Key)
      • ComputeHash (Str, Key, hex)
      • ConvertEncoding
    • Regular Expressions
      • ReplaceRegEx
      • ReplaceRegEx (Integer StartAt)
      • IsMatchRegEx (StartAt)
      • IsMatchRegEx
      • IsUSPhone
      • IsUSZipCode
      • GetMatchRegEx
      • GetMatchRegEx (StartAt)
    • TimeSpan
      • Minutes
      • Hours
      • Days
      • Milliseconds
      • TotalMilliseconds
      • TimeSpanFromTicks
      • Ticks
      • TotalHours
      • Seconds
      • TotalDays
      • ToTimeSpan (Hours, Min, Sec)
      • ToTimeSpan (Milli)
      • ToTimeSpan
      • TotalSeconds
      • TotalMinutes
    • Matching
      • Soundex
      • DoubleMetaphone
      • RefinedSoundex
    • Processes
      • TerminateProcess
      • IsProcessRunning
  • USE CASES
    • End-to-End Use Cases
      • Data Integration
        • Using Astera Data Stack to Create and Orchestrate an ETL Process for Partner Onboarding
      • Data Warehousing
        • Building a Data Warehouse – A Step by Step Approach
      • Data Extraction
        • Reusing The Extraction Template for Similar Layout Files
  • CONNECTORS
    • Setting Up IBM DB2/iSeries Connectivity in Astera
    • Connecting to SAP HANA Database
    • Connecting to MariaDB Database
    • Connecting to Salesforce Database
    • Connecting to Salesforce – Legacy Database
    • Connecting to Vertica Database
    • Connecting to Snowflake Database
    • Connecting to Amazon Redshift Database
    • Connecting to Amazon Aurora Database
    • Connecting to Google Cloud SQL in Astera
    • Connecting to MySQL Database
    • Connecting to PostgreSQL in Astera
    • Connecting to Netezza Database
    • Connecting to Oracle Database
    • Connecting to Microsoft Azure Databases
    • Amazon S3 Bucket Storage in Astera
    • Connecting to Amazon RDS Databases
    • Microsoft Azure Blob Storage in Astera
    • ODBC Connector
    • Microsoft Dynamics CRM
    • Connection Details for Azure Data Lake Gen 2 and Azure Blob Storage
    • Configuring Azure Data Lake Gen 2
    • Connecting to Microsoft Message Queue
    • Connecting to Google BigQuery
    • Azure SQL Server Configuration Prerequisites
    • Connecting to Microsoft Azure SQL Server
    • Connecting to Microsoft SharePoint in Astera
  • Incremental Loading
    • Trigger Based CDC
    • Incremental CDC
  • MISCELLANEOUS
    • Using Dynamic Layout & Template Mapping in Astera
    • Synonym Dictionary File
    • SmartMatch Feature
    • Role-Based Access Control in Astera
    • Updating Your License in Astera
    • Using Output Variables in Astera
    • Parameterization
    • Connection Vault
    • Safe Mode
    • Context Information
    • Using the Data Source Browser in Astera
    • Pushdown Mode
    • Optimization Scenarios
    • Using Microsoft’s Modern Authentication Method in Email Source Object
    • Shared Actions
    • Data Formats
    • AI Automapper
    • Resource Catalog
    • Cloud Deployment
      • Deploying Astera Data Stack on Microsoft Azure Cloud
      • Deploying Astera Data Stack on Oracle Cloud
      • Deploying Astera Data Stack on Amazon Web Services
      • Setting up the Astera Server on AKS
    • GIT In Astera Data Stack
      • GIT Repositories in Astera Data Stack
      • Moving a Repository to a Remote Server
      • Git Conflicts in Astera Data Stack
    • Astera Best Practices
  • FAQs
    • Installation
      • Why do we need to make two installations for Astera?
      • What’s the difference between Custom and Complete installation?
      • What’s the difference between 32-bit and 64-bit Astera?
      • Can we use a single license for multiple users?
      • Does Astera client work when it’s not connected to the server?
      • Why do we need to build a cluster database and set up a repository while working with Astera?
      • How do we set up multiple servers for load balancing?
      • How do we maintain schedules when migrating server or upgrading version?
      • Which database providers does Astera support for setting up a cluster database?
      • How many Astera clients can be connected to a single server?
      • Why is Astera not able to access my source file or create a new one?
    • Sources
      • Can I use data from unstructured documents in dataflows?
      • Can I extract data from fillable PDF forms in Astera?
      • Does Astera support extraction of data residing in online sources?
      • How do I process multiple files in a directory with a single execution of a flow?
      • Can I write information from the File System Items Source to the destination?
      • Can I split a source file into multiple files based on record count?
      • Does Astera support data extraction from unstructured docs or text files?
      • What is the difference between full and incremental loading in database sources?
      • How is the File System Items Source used in a Dataflow?
      • How does the PDF Form Source differ from the Report Source in Astera?
      • Does Astera support extraction of data from EDI files?
      • How does the Raw Text Filter option work in file sources in Astera?
    • Destinations
      • If I want to have a different field delimiter, say a pipe (“|”), is there an option to export with a
      • Tools Menu > Data Format has different date formats, but it doesn’t seem to do anything.
      • Can we export the Object Path column present in the Data Preview window?
      • I want to change the output format of a column.
      • What will be the outcome if we write files multiple times to the same Excel Destination?
    • Transformations
      • How is the Aggregate Transformation different from the Expression Transformation?
      • Can we omit duplicate records using the Aggregate Transformation in Astera?
      • How many datasets can a single Aggregate object take input from?
      • How is Expression Transformation different from the Function Transformation?
    • Workflows
      • What is a Workflow in Astera?
      • How do I trigger a task if at least one of a set of tasks fails?
      • Can I perform an action based on whether a file has data?
    • Scheduler
      • How can I schedule a job to run every x hours?
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  1. DATAFLOWS
  2. Testing and Diagnostics

Correlation Analysis

PreviousTesting and DiagnosticsNextVisualization

Last updated 1 year ago

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The Correlation Analysis object enables users to compute a two-way association between several numeric data fields. The strength of association is measured by computing correlation coefficients.

In Astera, users have the option to compute three different types of correlation coefficients:

  1. Pearson Correlation: A measure for investigating the strength of association between two quantitative continuous variables, for example, age and height.

  2. Spearman’s Rank Correlation: A measure for investigating the strength of association between two ordinal scale rank variables with mutually exclusive ranks. For example, two physicians ranking a patient’s condition from worst to best.

  3. Kendall’s Tau Correlation: A non-parametric measure of the relationship between columns of ranked data. For example, two interviewers ranked ten candidates as per their performance in the interview.

The coefficient’s value varies between -1 to 1, with ±1 indicating a perfect or 100% association, while a coefficient value of 0 suggests zero association between the numeric data fields. The higher the magnitude of the coefficient, the stronger the correlation between two variables.

The mathematical sign of coefficient value determines the slope inclination; positive (+) sign indicates an upward slope whereas negative (-) sign indicates a downward slope. Therefore, a positive coefficient indicates that the two variables are directly related, whereas a negative coefficient indicates that the two variables are inversely proportional.

In this document, we will learn to perform Correlation Analysis on a set of numeric data in Astera.

Sample Use-Case

In this case, we are using a object to extract the source data. You can download this sample data file from .

The source file contains information on the demographics of individuals who bought the insurance policy as well as those who chose otherwise.

You can preview this data by right-clicking on source object’s header and selecting Preview Output.

A Data Preview window will open, previewing the insurance data.

Now, we want to figure out the strength of association between these numeric data fields to assess which fields are highly correlated and which fields have low correlation.

For this purpose, we will use the Correlation Analysis object in Astera.

Using Correlation Analysis

1. To get a Correlation Analysis object from the Toolbox, go to Toolbox > Testing and Diagnostics > Correlation Analysis and drag-and-drop the testing object onto the dataflow designer.

2. The testing object contains two sub-nodes, Input and Output. Auto-map the source fields by dragging-and-dropping the root node of the source object onto the Input node of the Correlation Analysis object.

3. Right-click on the object’s header and select Properties from the context menu.

4. A configuration window will open, as shown below. This is the Layout Builder, where users can delete fields, change the Name or Data Type and modify fields with Calculation. Click Next.

5. This is the Properties window that contains configuration options.

  • Method Type: Specifies the type of correlation coefficient to compute, depending on the content of the data and its type. The options available are Pearson, Spearman or Kendall.

  • Treatment of Null: Specifies the handling of missing data. By default, it is set to Assumes No Missing Value, as missing data produces an error. There are two more options:

    • Listwise Deletion: Removes only the missing cell from the data.

    • Pairwise Deletion: Removes the entire row of data if any one fields contains an empty cell.

In this example, the source data is neither ranked nor on an ordinal scale. Hence, we will select Pearson’s Correlation Coefficient and leave Treatment of Null option as default. Click OK.

6. Right-click on the Output node of Correlation Analysis object and go to Write to > Excel Workbook Destination.

8. Right-click on the Correlation Analysis object and select Preview Output. In the Data Preview window, the preview shows the Pearson’s Correlation Coefficients computed for each variable with the rest of the numeric data fields.

By looking at the magnitude and the sign of the coefficient, we can deduce that the highest positive correlation exists between the variables Age and Retire. This means that the higher the age, the higher the chances of a person being retired.

Whereas, the highest negatively correlated variables are EducYear and Hisp. Meaning, if the person is Hispanic, he is more likely to have fewer number of years of education, according to our Correlation Analysis.

This concludes the document on using Correlation Analysis in Astera.

7. Configure the Excel Workbook Destination object to save the correlation output. To perform this step, refer to the article .

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Delimited File Source
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