Compare 2 SQL datasets

Used for Compare 2 SQL datasets, this automated workflow compares order data from 2003-2004 and 2004-2005, providing insights on total amounts and order counts. It simplifies data analysis by merging and comparing datasets, enabling users to identify trends and discrepancies effectively.

7/8/2025
5 nodes
Simple
manualsimplecomparedatasetsmysqldatabasedata
Categories:
Manual TriggeredSimple WorkflowData Processing & Analysis
Integrations:
CompareDatasetsMySQL

Target Audience

This workflow is ideal for:
- Data Analysts: Professionals who need to compare sales data across different years to identify trends and anomalies.
- Business Intelligence Teams: Teams looking to gather insights from historical payment data to inform strategic decisions.
- Database Administrators: Individuals responsible for maintaining and optimizing SQL databases who need to ensure data integrity in comparisons.
- Small to Medium Enterprises (SMEs): Businesses that want to analyze their customer payment behaviors over specific years without extensive coding knowledge.

Problem Solved

This workflow addresses the challenge of comparing two SQL datasets from different years. Specifically, it allows users to:
- Efficiently aggregate and compare payment data for 2003-2004 and 2004-2005.
- Identify changes in customer spending habits and order counts across the years.
- Provide a clearer picture of financial performance and customer engagement over time, helping businesses make informed decisions.

Workflow Steps

  • Manual Trigger: The workflow begins when the user clicks "Execute Workflow".
    2. Fetch Data for 2003-2004: It executes a SQL query to retrieve the total payment amounts and order counts for each customer for the years 2003 and 2004.
    3. Fetch Data for 2004-2005: Simultaneously, it runs another SQL query to gather the same data for 2004 and 2005.
    4. Compare Datasets: The workflow uses the CompareDatasets node to analyze the two datasets based on customerNumber and year, allowing for the identification of trends and discrepancies.
    5. Update Order Count: Finally, it modifies the order count value to 1, potentially indicating a baseline for further calculations or comparisons.

    • Customization Guide

    Users can customize this workflow by:
    - Adjusting SQL Queries: Modify the SQL queries in the "Orders from 2003 and 2004" and "Orders from 2004 and 2005" nodes to target different years or additional fields as needed.
    - Changing Comparison Criteria: Alter the fields used in the Compare Datasets node to include other relevant parameters, such as product types or regions.
    - Adding More Data Sources: Incorporate additional MySQL queries to compare more years or datasets, enhancing the analysis.
    - Modifying Output Values: Change the value set in the "Change ordercount" node to represent different metrics or thresholds based on business requirements.