Mainframe offloading and modernization: Using mainframe data to build cloud native services with AWS

Many companies in the financial services and insurance industries rely on mainframes for their most business-critical applications and data. But mainframe workloads typically lack agility. This is one reason that organizations struggle to innovate, iterate, and pivot quickly to develop new applications or release new capabilities. Unlocking this mainframe data can be the first step in your modernization journey.

In this blog post, we will discuss some typical offloading patterns. Whether your goal is developing new applications using mainframe data or modernizing with the Strangler Fig Application pattern, you might want some guidance on how to begin.

Refactoring mainframe applications to the cloud

Refactoring mainframe applications to cloud-native services on AWS is a common industry pattern and a long-term goal for many companies to remain competitive. But this takes an investment of time, money, and organizational change management to realize the full benefits. We see customers start their modernization journey by offloading data from the mainframe to AWS to reduce risks and create new capabilities.

The mainframe data offloading patterns that we will discuss in this post use software services that facilitate data replication to Amazon Web Services (AWS):

  • File-based data synchronization
  • Change data capture
  • Event-sourced replication

Once data is liberated from the mainframe, you can develop new agile applications for deeper insights using analytics and machine learning (ML). You could create a microservices-based, or voice-based mobile application. For example, if a bank could access their historical mainframe data to analyze customer behavior, they could develop a new solution based on profiles to use for loan recommendations.

The patterns we illustrate can be used as a reference to begin your modernization efforts with reduced risk. The long-term goal is to rewrite the mainframe applications and modernize them workload by workload.

Solution overview: Mainframe offloading and modernization

This figure shows the flow of data being replicated from mainframe using integration services and consumed in AWS

Figure 1. Mainframe offloading and modernization conceptual flow

Mainframe modernization: Architecture reference patterns

File-based batch integration

Modernization scenarios often require replicating files to AWS, or synchronizing between on-premises and AWS. Use cases include:

  • Analyzing current and historical data to enhance business analytics
  • Providing data for further processing on downstream or upstream dependent systems. This is necessary for exchanging data between applications running on the mainframe and applications running on AWS
This diagram shows a file-based integration pattern on how data can be replicated to AWS for interactive data analytics

Figure 2. File-based batch ingestion pattern for interactive data analytics

File-based batch integration – Batch ingestion for interactive data analytics (Figure 2)

  1. Data ingestion. In this example, we show how data can be ingested to Amazon S3 using AWS Transfer Family Services or AWS DataSync. Mainframe data is typically encoded in extended binary-coded decimal interchange code (EBCDIC) format. Prescriptive guidance exists to convert EBCDIC to ASCII format.
  2. Data transformation. Before moving data to AWS data stores, transformation of the data may be necessary to use it for analytics. AWS analytics services like AWS Glue and AWS Lambda can be used to transform the data. For large volume processing, use Apache Spark on AWS Elastic Map Reduce (Amazon EMR), or a custom Spring Boot application running on Amazon EC2 to perform these transformations. This process can be orchestrated using AWS Step Functions or AWS Data Pipeline.
  3. Data store. Data is transformed into a consumable format that can be stored in Amazon S3.
  4. Data consumption. You can use AWS analytics services like Amazon Athena for interactive ad-hoc query access, Amazon QuickSight for analytics, and Amazon Redshift for complex reporting and aggregations.
This diagram shows a file-based integration pattern on how data can be replicated to AWS for further processing by downstream systems

Figure 3. File upload to operational data stores for further processing

File-based batch integration – File upload to operational data stores for further processing (Figure 3)

  1. Using AWS File Transfer Services, upload CSV files to Amazon S3.
  2. Once the files are uploaded, S3’s event notification can invoke AWS Lambda function to load to Amazon Aurora. For low latency data access requirements, you can use a scalable serverless import pattern with AWS Lambda and Amazon SQS to load into Amazon DynamoDB.
  3. Once the data is in data stores, it can be consumed for further processing.

Transactional replication-based integration (Figure 4)

Several modernization scenarios require continuous near-real-time replication of relational data to keep a copy of the data in the cloud. Change Data Capture (CDC) for near-real-time transactional replication works by capturing change log activity to drive changes in the target dataset. Use cases include:

  • Command Query Responsibility Segregation (CQRS) architectures that use AWS to service all read-only and retrieve functions
  • On-premises systems with tightly coupled applications that require a phased modernization
  • Real-time operational analytics
This diagram shows a transaction-based replication (CDC) integration pattern on how data can be replicated to AWS for building reporting and read-only functions

Figure 4. Transactional replication (CDC) pattern

  1. Partner CDC tools in the AWS Marketplace can be used to manage real-time data movement between the mainframe and AWS.
  2. You can use a fan-out pattern to read once from the mainframe to reduce processing requirements and replicate data to multiple data stores based on your requirements:
    • For low latency requirements, replicate to Amazon Kinesis Data Streams and use AWS Lambda to store in Amazon DynamoDB.
    • For critical business functionality with complex logic, use Amazon Aurora or Amazon Relational Database Service (RDS) as targets.
    • To build data lake or use as an intermediary for ETL processing, customers can replicate to S3 as target.
  3. Once the data is in AWS, customers can build agile microservices for read-only functions.

Message-oriented middleware (event sourcing) integration (Figure 5)

With message-oriented middleware (MOM) systems like IBM MQ on mainframe, several modernization scenarios require integrating with cloud-based streaming and messaging services. These act as a buffer to keep your data in sync. Use cases include:

  • Consume data from AWS data stores to enable new communication channels. Examples of new channels can be mobile or voice-based applications and can be innovations based on ML
  • Migrate the producer (senders) and consumer (receivers) applications communicating with on-premises MOM platforms to AWS with an end goal to retire on-premises MOM platform
This diagram shows an event-sourcing integration reference pattern for customers using middleware systems like IBM MQ on-premises with AWS services

Figure 5. Event-sourcing integration pattern

  1. Mainframe transactions from IBM MQ can be read using a connector or a bridge solution. They can then be published to Amazon MQ queues or Amazon Managed Streaming for Apache Kakfa (MSK) topics.
  2. Once the data is published to the queue or topic, consumers encoded in AWS Lambda functions or Amazon compute services can process, map, transform, or filter the messages. They can store the data in Amazon RDS, Amazon ElastiCache, S3, or DynamoDB.
  3. Now that the data resides in AWS, you can build new cloud-native applications and do the following:

Conclusion

Mainframe offloading and modernization using AWS services enables you to reduce cost, modernize your architectures, and integrate your mainframe and cloud-native technologies. You’ll be able to inform your business decisions with improved analytics, and create new opportunities for innovation and the development of modern applications.

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