Migrating Actian DataConnect Scripts to Python

Migrating Actian DataConnect Scripts to Python

September 09, 2026

facebook twitter indeed whatsup mail
Overview

Many organizations rely on legacy ETL tools like Actian DataConnect to handle data processing. Over time, these tools become expensive and harder to maintain.

Our client wanted to move away from Actian due to high licensing costs and limited flexibility. The goal was to rebuild all data workflows using a modern, open-source stack.

We migrated all Actian ETL scripts to Python-based pipelines, creating a faster, scalable, and cost-free data processing system.

Migrating-Actian-DataConnect
Client Requirements

The client needed a system that could:

  • Replace Actian DataConnect completely
  • Remove recurring licensing costs
  • Convert all existing ETL scripts to Python
  • Keep existing data logic unchanged
  • Improve performance of data processing
  • Make the system easier to maintain
  • Support future scaling and integration
The Challenge

The migration process had several difficulties:

  • Actian scripts were written in a complex, less common syntax
  • Business logic was hidden inside legacy workflows
  • Very limited documentation was available
  • Developers had to understand old scripts from scratch
  • Each workflow had to be carefully decoded
  • Risk of data mismatch during migration
  • Large number of scripts to convert
Key Problems:
  • Complex legacy scripting format
  • No proper documentation
  • Hidden business logic
  • High risk of data mismatch
  • Time-heavy reverse engineering
  • Large-scale script migration
Our Solution

We built a structured migration approach using Python-based tools:

  • Script analysis and mapping
  • ETL pipeline rebuild in Python
  • Data validation framework
  • Parallel execution testing
  • Automated workflow design

We replaced Actian with:

  • Python (core processing)
  • Pandas (data transformation)
  • PyArrow (data handling)
  • SQLAlchemy (database connection)
  • Airflow (workflow scheduling)
Architecture & Workflow
Steps Process Details Deliverable
Script Analysis We first studied all Actian scripts in detail.

We identified:

  • Input and output sources
  • Transformation rules
  • Business logic inside scripts
  • Conditional flows
 Result:

Clear understanding of all existing data workflows.
Logic Mapping to Python Each Actian function was converted into Python equivalents.

Examples:

  • Data import/export logic → Pandas dataframes
  • Conditional logic → Python if/else
  • File operations → Python file handling
  • Date handling → Python datetime functions
 Result:

All legacy logic successfully mapped to Python.
ETL Pipeline Rebuild We rebuilt complete workflows using Python.

Used:

  • Pandas for data transformation
  • SQLAlchemy for database connection
  • Regex tools for data cleaning
  • Airflow for workflow scheduling
 Result:

Modern and structured ETL pipelines replaced old scripts.
Data Validation We ran both systems in parallel:

  • Actian output vs Python output comparison
  • Cross-checking datasets
  • Testing edge cases
 Result:

100% data accuracy maintained during migration.
Deployment & Optimization Final Python system was deployed after validation.

Improvements included:

  • Faster execution
  • Cleaner structure
  • Reduced processing time
 Result:

Fully working modern data system.
Key Innovations
Results Achieved
Cost Savings
  • Removed Actian licensing completely
  • Reduced long-term operational costs
Better Performance
  • Faster data processing using Python
  • Optimized ETL pipelines
Easier Maintenance
  • Clean and readable codebase
  • Simple onboarding for new developers
Scalability
  • System can now handle larger datasets
  • Easy integration with new tools
No Vendor Dependency
  • Full control over data system
  • No reliance on third-party ETL tools

Before vs After

Aspect Before Solution After Solution
Cost High licensing fees No licensing cost
Performance Slower processing Faster execution
Maintenance Difficult Easy & structured
Flexibility Limited High flexibility
Dependency Vendor locked-in Fully independent
Tech Stack
target-database
Python

Core processing

source-database
Pandas

Data transformation

processing-logic
PyArrow

Data handling

sync-type
Apache Airflow

Workflow automation

data-handling
Regex tools

Data cleaning

Conclusion
This migration helped the client move from a costly and limited ETL system to a modern Python-based data platform. By carefully converting legacy scripts and validating every step, we ensured no data loss and full accuracy. The new system is faster, easier to manage, and fully independent from any vendor tools, giving the client long-term control over their data operations.
SynergyTop Revitalized a Creative Learning Platform for Kids

SynergyTop Revitalized a Creative Learning Platform for Kids

Read More...
SynergyTop Enhances IT Service Management with Custom Autotask Integration and Intelligent SLA Tracking

SynergyTop Enhances IT Service Management with Custom Autotask Integration and Intelligent SLA Tracking

Read More...
SynergyTop Empowers a Leading Construction Company with a Robust SaaS and Mobile App Solution for Construction Project Management

SynergyTop Empowers a Leading Construction Company with a Robust SaaS and Mobile App Solution for Construction Project Management

Read More...
st-log

At SynergyTop, we are more than just an IT company; we are your strategic partner for digital success. With a passionate team of experts, we craft innovative solutions that drive your business forward.

Follow Us

Social 1Social 2Social 3
Social 4Social 5Social 6