Part 1: Concept – Leveraging Legacy Data with Modern AI and ML Tools
Many organizations rely on legacy systems like AIX and IBM i to handle critical workloads. These systems, though powerful, often exist in isolation from modern analytics and artificial intelligence (AI) tools. As a result, businesses may struggle or hesitant to fully unlock the potential of their valuable data, which remains trapped within these older platforms.
However, if your organization runs AIX or IBM i workloads in Azure, you’re already in the cloud. This transition makes it much easier to bridge the gap between legacy systems and modern AI technologies, such as Azure Machine Learning (ML) and OpenAI. By extracting and transforming data from AIX or IBM i systems, you can integrate it into Azure’s ecosystem, enabling predictive analytics, natural language processing (NLP) for chatbot copilots, or automated summarization of unstructured data.
Key Use Cases for Legacy System Data Integration
- Sales Forecasting – Use legacy order history to predict future sales trends.
- Chatbot Support – Leverage green screen system logs to power intelligent chatbots.
- Anomaly Detection – Automatically flag errors or outliers in batch jobs or job runs.
- Spool File Summarization – Use GPT models to summarize and provide insights from legacy spool files.
- Text Classification – Classify and analyze unstructured text from logs or user input.
High-Level Architecture
The process of extracting data from AIX or IBM i systems and using it with Azure ML/OpenAI follows a streamlined, secure, and scalable architecture.
- Move to Skytap on Azure: Host your AIX or IBM i systems in Azure.
- Secure Data Transfer: Use SFTP, NFS, or APIs to move the data.
- Azure Data Services: Store the data in Azure Data Lake or Blob Storage and then process it using Azure ML or OpenAI for advanced analysis.
Step-by-Step Overview of Data Integration:
1. Extracting Data from AIX/IBM i:
There are two primary methods to extract data from AIX/IBM i systems. The file-based approach involves generating flat files (e.g., CSV or TXT), spool files, or exporting data from DB2 in formats like XML or JSON, which can then be transferred for further processing. Alternatively, direct database access can be achieved using ODBC/JDBC connectors, allowing external systems or scripts (e.g., written in Python or Java) to query data in real-time. This method supports automated, scheduled extractions and is typically more scalable for ongoing integration needs.
2. Storing Data in Azure:
Once the data has been extracted, it needs to be landed into Azure for centralized access. Depending on the volume and nature of the data, options include Azure Data Lake Storage for big data analytics, Azure Blob Storage for general-purpose unstructured data storage, direct ingestion into databases like Azure SQL, Cosmos DB (for NoSQL scenarios), or Azure Synapse Analytics (for large-scale analytics). The choice depends on your processing and querying needs.
3. Data Preprocessing:
Before data can be used for reporting or modeling, it must be cleaned and transformed. Azure Data Factory (ADF) provides robust ETL (Extract, Transform, Load) capabilities, enabling tasks like filtering, joining, or converting data formats. For more complex scenarios, Azure Synapse can be used to run powerful SQL-based transformations or Spark notebooks. This step ensures that the data is accurate, consistent, and structured for downstream applications.
4. Analyzing Data with Azure ML/OpenAI:
With clean data in place, you can now perform advanced analytics. Azure Machine Learning allows users to build, train, and deploy models for tasks like regression, classification, or clustering. You can also integrate OpenAI models via Azure OpenAI Service to handle unstructured data—like generating summaries, extracting entities from text, or converting emails and notes into structured datasets. This combination enables both predictive insights and intelligent automation.
By combining AIX or IBM i data with Azure Machine Learning and OpenAI, organizations can harness the power of AI and predictive analytics without modernizing their entire application stack. The process is simple, secure, and efficient—allowing businesses to derive meaningful insights from legacy systems in just a few steps. Stay tuned for part 2 where we’ll look at a real-world deployment with test data of a customer who extracted data from their legacy IBM Power workloads.
Leverage your legacy data with Skytap on Azure
Start extracting and transforming data from your AIX or IBM i systems today. Skytap on Azure is a favored choice for migrating legacy workloads, such as AIX and IBM i, to the cloud without extensive modifications, rewriting, or refactoring. This extends the lifespan of investments while leveraging Azure’s AI technologies. Contact one of our cloud migration experts here.
Connect with the author:
Abhishek Jain – Cloud Solutions Architect at Skytap
