Astera Data Stack
Version 11
Version 11
  • Welcome to Astera Data Stack Documentation
  • RELEASE NOTES
    • ReportMiner 11.1 - Release Notes
  • SETTING UP
    • System Requirements
    • Product Architecture
    • Installing Client and Server Applications
    • Install Manager
      • Installing Packages on Client Machine
      • Installing Packages on Server Machine
    • 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
    • Enabling Python Server
    • User Roles and Access Control
      • Windows Authentication
      • Azure Authentication
    • Offline Activation of Astera
    • Silent Installation
  • Astera Intelligence
    • LLM Generate
  • 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
      • Text Converter
      • 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
    • 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
    • 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-Generate Layout, Auto-Create Fields and Create Table Region
      • 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
      • 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
  • SERVER APIS
    • 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
        • Integrating Document Processing into Existing Systems with Astera Server APIs
      • 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. DATA MODEL
  2. Dimensional Modelling

Aggregates in Dimensional Modeling

PreviousDate and Time DimensionNextVerifying a Dimensional Model

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Introduction to Aggregate Tables

Aggregate tables in Astera Data Stack allow users to quickly and easily merge data to compute averages, totals, counts, and minimum and maximum values.

These tables are highly beneficial when used as foundations for standard reports that require minimal changes. For such standardized reporting structures, aggregate tables are fast, dependable, and user-friendly for both developers and end-users. Their ease of setup also makes them valuable for impromptu reporting.

However, adaptability is compromised in favor of this efficiency and simplicity. Aggregate tables are not as practical for analysts who need to examine data from various perspectives. Unlike an OLAP cube, consolidated table data cannot be pivoted, nor can it be drilled down to a more detailed level to view underlying transactions. Nevertheless, aggregate tables can be incredibly useful when applied in the appropriate context.

Creating an Aggregate Table

For our use case, we will create an aggregate table to aggregate customers’ sales data by month.  For the purposes of this demonstration, please refer to the simple dimensional model shown here:

  1. As the sales data must be aggregated for our use case, the base fact table will be Sale. Right-click the Sale table header and select Add Aggregate Table from the context menu.

  1. A new Sale_Aggregate entity will be created in the model. This will act as the aggregate entity, indicated by a blue dash-dotted link pointing to its base fact table, as shown below:

Configuring an Aggregate Table

  1. First, right-click the aggregate table’s header and select Properties from the context menu or double-click the table header.

  2. In the Sale_Aggregate: Aggregate Table Properties window, provide the name, schema, and description (optional) according to requirements. Once done, click Next.

  1. In the Sale_Aggregate: Sort Transformation Properties window, select fields on which the aggregate must be performed, along with the operation. Once done, click Next.

  1. In the Sale_Aggregate: Aggregate Group By window, select Group By fields to specify fields to analyze data by and select granularity.

Data Granularity: You can change the granularity for a date field to determine the intervals for which item values are shown. Granularity in Aggregates only works if you have selected a date dimension related field as a Groupby field, which in our use case is the Invoice_Date_key field. You can set the date granularity to any one of the following values:

  • Yearly

  • Quarterly

  • Monthly

  • Weekly

  • Daily (this is the default)

  1. Once the appropriate granularity has been selected, click Next. The Sale_Aggregate: Entity Properties shows the finalized layout of the aggregate table. You can change the length, name, and dB types of the fields according to your needs.

  2. Once done, Click OK to close the window.

Your aggregate table has been configured successfully. The newly created blue, dash-dotted links point to the tables that your aggregate table is dependent on.

Notice that a new dimension (MonthDimension) has also been created. This is because the ‘Monthly’ granularity was selected while configuring the aggregate.

The Month dimension will hold records in monthly granularity and will further allow timely aggregations of these figures when reporting. You will get different dissected dimensions for other granularities except for the ‘Daily’ granularity as the dimension for this granularity is already present in the DateDimension entity in the model.

  1. Now, forward engineer the aggregate entities (aggregate table and dissected dimension) and the dimensional model (if it has not already been forward engineered).

  2. Fill the dissected dimension, the MonthDimension, by right-clicking the table header and selecting the Fill Month Dimension Table option from the context menu.

Note: You can also edit or update already configured aggregate tables.

Verifying Aggregate Tables

After completing all aforementioned steps, the model must be deployed. When deploying a dimensional model containing aggregate tables, Astera Data Stack verifies some set of rules specific to aggregates in order for the deployment to be successful.

These rules are:

  1. All dissected dimensions such as MonthDimension, WeekDimension…etc., must be filled.

  2. At least one field should be selected for Groupby.

  3. At least one field should be selected for Aggregation.

  4. If a DateDimension-related field is selected as GroupBy and Monthly, Weekly, Quarterly or Yearly granularity is selected, then the equivalent dissected dimension (such as YearDimension) must be present in the model.

Loading/Updating the Aggregate Table

Aggregate tables are loaded/Updated along with fact tables.

To load or update a fact table, the Fact Table Loader object can be used in a dataflow.

  1. First, drag-and-drop the Fact Table Loader object from the Toolbox onto the designer.

  2. Double-click the object header or right-click the object header and select properties from the context menu. The FactSale: Database Connection window will open.

  3. Select the appropriate data model deployment and click Next.

  1. In the FactSale: Pick Table window select the fact associated with your aggregate. Once done, click Next.

  1. In the FactSale: Select Aggregate Table window, you will see all the aggregates associated with the selected fact.

  1. Check the aggregate tables which you want to load/update and click OK to close the window.

  1. Now, map all appropriate fields from the DataModelQuery object to your fact loader, in the same manner as loading a fact table.

  1. Now, run the fact loader dataflow. First, your fact table will be loaded, and then the selected aggregates that were checked earlier in the fact loader. You can see the stack trace of your aggregate in the Job Progress window.

Your aggregate table has been loaded successfully. You can now view the resultant data available in your aggregate table.

Also notice that the “invoice Date Key” field shows only monthly level information, as we had selected Monthly Granularity while configuring our aggregate.

Things to consider when working with Aggregate tables:

  • Aggregates only work with Star-Schema dimensional models.

  • If any field/relationship/dimension is deleted and was being used in a configured aggregate, the aggregate table will need to be refreshed in order to make the required changes in the aggregate as well. You can refresh the aggregate by right-clicking the aggregate table header and selecting the Refresh Aggregate Table option from the context menu.

Note: For more information about configuring the Fact Table Loader object, click .

Note: For more information about the Data Model Query object, click .

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