Re-engineer mainframe batch applications on Azure

Azure Data Factory
Azure Databricks
Azure Kubernetes Service (AKS)
Azure SQL Database
Azure Storage

This reference architecture shows how you can use Azure to re-engineer a z/OS mainframe batch application to deliver a secure, scalable, and highly available system in the cloud using Azure. Because of ever evolving business needs, data and applications need to deliver and scale without affecting your infrastructure. Re-engineering to the cloud can help businesses in finance, health, insurance, and retail minimize their product or feature delivery times, and reduce costs.

Mainframe Architecture

The first diagram shows the architecture of a typical batch application running on a z/OS mainframe.

Diagram of a typical batch application running on a z/OS mainframe.

Download a Visio file of this architecture.


  1. Mainframe batch processes can be triggered at a scheduled time using an Operation, Planning, and Control (OPC) Scheduler. They can also be triggered by a message placed in a message queue, like a message that announces that a file was created.

  2. A mainframe direct-access storage device (DASD) is used to store input and output files; for example, flat files that are required by the application. You can trigger the batch process by creating a file on the DASD storage.

  3. The batch process is an execution of a set of jobs, like a job internally running a user or system program to do a specific task. Usually, batch processes are run without user interaction. All batch jobs on a mainframe are executed under the control of a Job Execution System (JES).

  4. Programs in batch processes can read/write data from:

    • A file-based database like Virtual Storage Access Method (VSAM).
    • A relational database like Db2 or Informix.
    • A non-relational database like Information Management System (IMS).
    • A message queue.
  5. The output of the job execution can be monitored through an OPC scheduler or Tivoli Workload Scheduler (TWS). A System Display and Search Facility (SDSF) in the JES is also used on the mainframe to check job execution status.

  6. The management tier provides the following services:

    • Source control, like Endevor or Changeman.
    • Security, like Resource Access Control Facility (RACF). This security provides authentication for running batches, accessing files, and accessing the database.
    • Output management that supports the storage and search of job execution logs.

Azure architecture

The second diagram shows how you can use Azure services to re-engineer a similar application with added capabilities and flexibility.

Diagram of a batch application re-engineered using Azure services. Multiple example services are included.

Download a Visio file of this architecture.


  1. Use one of the following triggers to start the Azure batch process.

    • Use the Azure Databricks job scheduler or Azure Function scheduler.
    • Create a recurring batch process task with Azure Logic Apps.
    • Use a storage event, like the creation or deletion of a file in Azure Blob or File storage.
    • Use a message-based trigger, like the arrival of a message on the Azure Service Bus.
    • Create an Azure Data Factory trigger.
  2. Store files migrated from the mainframe by using Azure Blob Storage or Azure Files. Batch processes re-engineered on Azure can read/write data from this storage.

  3. Azure provides various services to implement a mainframe batch workload. Select specific services that are based on your business requirements. For example, compute power required, total execution time, the ability to split mainframe batch process into smaller units, and cost sensitivity.

    1. Azure Databricks is an Apache Spark-based analytics platform. Jobs can be written in the R, Python, Java, Scala, and Spark SQL languages. It provides a compute environment with fast cluster start times, auto termination, and autoscaling. It has built-in integration with Azure storage like Azure Blob Storage and Azure Data Lake storage. Use Azure Databricks if you need to process large amounts of data in a short time. It's also a good choice if you need to run Extract, Transform, and Load (ETL) workloads.
    2. AKS and Service Fabric provide an infrastructure to implement a service-based application architecture. It might not be cost effective for a single application. You can refactor your mainframe application using Java Spring Boot. The best way to run Spring Boot apps on Azure is to use Azure Spring Apps, a fully managed Spring service. Java developers can use it to easily build and run Spring Boot Microservices on Azure.
    3. You can re-engineer your mainframe batch application using .NET or Java. Batch provides the infrastructure to run this application at scale. It creates and manages a pool of virtual machines (VMs), installs the applications, and then schedules jobs to run on the VMs. There's no cluster or job scheduler software to install, manage, or scale. Write applications in any programming language supported by Windows or Linux.
    4. You can re-engineer short running COBOL or PL/1 batch programs. For these programs, use Azure services like Functions, WebJobs, or Logic Apps.
  4. Azure provides various data services to store and retrieve data.

    • You can migrate mainframe relational databases like Db2 and Informix with minimal changes to the Azure relational database offerings' visibility. For example, relational database services like Azure SQL VM, Azure SQL DB, or Azure SQL MI. You can also use any open-source Relational Database Management System (RDBMS) like Azure PostgreSQL. The selection of an Azure database depends on the type of workload, cross-database queries, two-phase commit requirements, and many other factors.
    • You can migrate mainframe non-relational databases like IMS, Integrated Data Management System (IDMS), or VSAM to Azure Cosmos DB. Azure Cosmos DB provides fast response times, automatic and instant scalability, and guaranteed speed at any scale. It's a cost-effective option for unpredictable or sporadic workloads of any size or scale. Developers can easily get started without having to plan for or manage capacity.
    • You can use Azure Cache for Redis to speed up a re-engineered application.
  5. Applications, the OS, and Azure resources can use agents to send logs and metrics to Azure Monitor Logs.

    • Application Insight monitors your migrated application. It automatically detects performance anomalies and includes powerful analytics tools to help you diagnose issues.
    • Azure Log Analytics helps store, index, query, and derive analytics from the log data collected.

    You can use the output of Log Analytics and Application Insights to create alerts and dashboards, or export to external services. You can also use the output to do an action like the scaling of a VM.

  6. This tier provides Azure services for source control, security, and output management. These services might consist of Azure DevOps and Microsoft Entra ID.


Network and identity

  • Azure ExpressRoute: ExpressRoute lets you extend your on-premises networks into the Microsoft cloud over a private connection from a connectivity provider. With ExpressRoute, you can establish connections to Microsoft cloud services like Microsoft Azure and Office 365.
  • Azure VPN Gateway: A VPN gateway is a specific type of virtual network gateway that is used to send encrypted traffic between an Azure virtual network and an on-premises location over the public Internet.
  • Microsoft Entra ID: Microsoft Entra ID is an identity and access management service that can sync with an on-premises directory.


  • Logic Apps: Logic Apps helps you create and run automated recurring tasks and processes on a schedule. You can call services inside and outside Azure like HTTP or HTTPS endpoints. You can also post messages to Azure services like Azure Service Bus, or get files uploaded to a file share.
  • Service Bus: You can use the Service Bus for messaging between a user interface and back-end services. This system can decouple applications and services and increase reliability and use.
  • Azure Databricks: Azure Databricks is a cloud-based data engineering tool that's used for processing and transforming large amounts of data. You can then explore that data through machine learning models.
  • Azure Spring Apps: Azure Spring Apps makes it easy to deploy, manage, and run Spring microservices to Azure. It supports both Java and .NET Core.
  • AKS: AKS simplifies deploying a managed Kubernetes cluster in Azure by offloading the operational overhead to Azure.
  • Batch: Batch is designed to run general purpose batch computing in the cloud across many VMs that can scale based on the workload being executed. It's a perfect fit for ETL or AI use cases where multiple tasks are executed in parallel, independent from each other.
  • Functions: Use Functions to run small pieces of code without worrying about application infrastructure. With Functions, the cloud infrastructure provides all the up-to-date servers you need to keep your application running at scale.
  • Azure App Service: With WebJobs, a feature of App Service, you can code reusable background business logic as web jobs.
  • Azure Cache for Redis: Applications that use a high volume of backend data can be developed to scale and deliver a highly optimized performance by integrating with an in-memory data store like Redis. Azure Cache for Redis offers both the Redis open-source (OSS Redis) and a commercial product from Redis Labs, Redis Enterprise, as a managed service.


Azure storage provides multiple tiers of hot, cool, and archive data. Effective usage of these storage tiers can give you a price-to-performance advantage.

  • Blob Storage: Scalable and secure object storage for cloud-native workloads, archives, data lakes, high-performance computing, and machine learning.
  • Azure Files: Simple, secure, and serverless enterprise-grade cloud file shares. Azure Files can particularly come in handy for re-engineered mainframe solutions. It provides an effective add-on for the managed SQL storage.
  • Table Storage: A NoSQL key-value store for rapid development using large semi-structured datasets.
  • Azure Queue Storage: Simple, cost-effective, durable message queueing for large workloads.
  • Azure SQL: Azure's fully managed family of services for SQL Server. You can migrate and use the relational data efficiently with other Azure services like Azure SQL Managed Instance, SQL Server on Azure Virtual Machines, and Azure Database for MariaDB.
  • Azure Cosmos DB: A no-SQL offering that you can use to migrate non-tabular data from the mainframes.


  • Azure Monitor: Azure Monitor delivers a comprehensive solution for collecting, analyzing, and acting on telemetry from cloud and on-premises environments. It contains the Application Insights, Azure Monitor Logs, and Azure Log Analytics features.


  • Azure DevOps: Re-engineer mainframe applications on Azure during every phase of software development and team collaboration. DevOps provides the following services:

    • Azure Boards: Agile planning, work item tracking, visualization, and reporting tool.
    • Azure Pipelines: A language, platform, and cloud agnostic CI/CD platform with support for containers or Kubernetes.
    • Azure Repos: Provides cloud-hosted private git repos.
    • Azure Artifacts: Provides integrated package management with support for Maven, npm, Python, and NuGet package feeds from public or private sources.
    • Azure Test Plans: provides an integrated, planned, and exploratory testing solution.

Scenario details

Mainframes are primarily used for processing large amounts of data. Batch processing is a way of processing a high volume of transactions that are grouped together, and then making bulk updates against the database. Once triggered, they require minimal to no user interaction. For example, mainframe systems make it possible for banks and other financial institutions to do end-of-quarter processing and produce reports, like quarterly stock or pension statements.

Potential use cases

This solution is ideal for the finance, insurance, healthcare, and retail industries. Use this architecture to re-engineer mainframe applications on Azure. The architecture works best for:

  • Resource-intensive mainframe batch applications.
  • Batch applications that need high compute during a certain time, like end of month, quarter, or year.
  • Mainframe batch processes that are repetitive and not resource-intensive but might need utilization by external systems.



  • The batch architecture in this article uses multi-node computing or PaaS services, which provide high availability.
  • Azure database services support zone redundancy, and you can design them to fail over to a secondary node if there's an outage or during a maintenance window.


  • The following Azure services in this architecture have autoscaling capabilities:

    • Azure Databricks
    • AKS
    • Spring Apps
    • Batch
    • Azure Functions
    • Logic Apps
  • For more information on autoscaling in Azure, see the autoscaling guide.


  • This reference architecture uses ExpressRoute for a private and efficient connection to Azure from the on-premises environment. However, you can also create a site to site VPN.
  • You can authenticate Azure resources by using Microsoft Entra ID. You can manage permissions with role-based access control (RBAC).
  • Database services in Azure support various security options like Data Encryption at Rest.
  • For more information on designing secure solutions, see Azure security documentation.


  • You can use Azure Monitor and Application Insights, in addition to Log Analytics, to monitor the health of an Azure resource. Set alerts to proactively manage your resource health.
  • For more information on resiliency in Azure, see Designing reliable Azure applications.

Cost optimization

Use the Azure pricing calculator to estimate costs for Azure resources.

See Azure mainframes batch application for an example cost estimate of services.


This article is maintained by Microsoft. It was originally written by the following contributors.

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