CIOs are again evaluating their mainframe investments, balancing rising operational costs and difficulty finding talent with the perceived costs and risks of moving critical applications to the cloud. Increased business agility, technical innovation, computing elasticity, customer insights, and a growing talent pool all encourage CIOs to migrate and modernize from “Big Iron” onto public cloud platforms. 

Google Cloud recently announced Dual Run, a new mainframe modernization solution, to help customers mitigate the risk involved in mainframe migrations and accelerate their migration to the cloud. Leaders can leverage Dual Run in their quest to ensure their mainframe modernization projects will succeed and pay off so let’s dive a little deeper into what Dual Run is, how it works, and how it can help you. 

Mainframe modernization with a proven technology

Since so many businesses still run mainframes, we decided to partner with Banco Santander—one of the largest banks in the world—to bring Dual Run to our enterprise customers, since they had already built a solution. In fact, Dual Run was built on top of Banco Santander’s unique technology which has already demonstrated proven results in the regulated financial services industry. Now that Dual Run is available, Banco Santander has been using it to bring their data and workloads onto Google Cloud’s trusted infrastructure.

The concept is not new, but the solution is unique

Dual Run enables you to run a parallel production system, allowing you to simultaneously run workloads on your mainframes and on Google Cloud. While many enterprises running mainframes have thought about parallel production concept and a few even tried before, Google Cloud is unique among the hyperscalers to provide such a solution as an offering to its customers. 

With a parallel production run, you can perform real-time testing of your applications on Google Cloud and quickly gather data on performance and stability with no disruption to your business. Once you’re satisfied with the functional and performance equivalence of the two systems, you can make the new Google Cloud environment your system of record, while existing mainframe systems can be used as a backup or decommissioned. 

In addition to the transformative benefits you get from moving to Google Cloud–such as AI-based scalability, speed, and security–migrating mainframes with Dual Run offers you even more benefits:

Mitigate migration risk: Dual Run reduces risk during the migration by running your business critical systems in parallel with powerful reporting to track the difference between your current and target systems. This ensures there is no impact or risk to your existing mainframes while migrating to Google Cloud. 

Secure migration investments: Avoid costly migration mistakes by basing your decisions and actions on empirical data acquired from your production system.

Reduce business testing effort: Compare the functional equivalence of outcomes in the current and target system with production data and drastically reduce the testing cycles of your migrated workload. 

Accelerate migration: Speed up the entire mainframe migration process with a well-defined framework, automation components, predefined dashboards, and a tested approach.  Empirical reporting available in Dual Run also enables customers in regulated industries to more readily respond to regulator reviews and requests for information.

Your migration journey with Dual Run

Dual Run is packaged with several automation components to aid your migration journey, from assessment all the way through to production. 

This chart shows how Dual Run plays a key role throughout your mainframe modernization journey:

Let’s explore this illustration in a bit more detail, phase by phase: 

Current state: This is your starting point, when your production workloads are still running in your mainframe. Dual Run helps you assess your mainframe workload for compatibility on Google Cloud. 

After this assessment, Dual Run’s conversion engine helps address the incompatibilities in your current application and then migrates the application and data to Google Cloud. At this stage, you have your current application executing on the mainframe and your migrated application is ready to be executed or tested on Google Cloud.

Dual Run state: In this state, the migrated workload will be executed in two stages.

Dual Run stage 1:

In the first stage of Dual Run, your mainframe will remain as the “primary” system — meaning the response and outputs to other systems are sent from your mainframe — while the migrated workload will be executed in parallel in Google Cloud as “secondary.”

Dual Run performs the following actions as a cyclical process, repeated until you reach your desired migrated application quality is achieved:

• All workloads — batch & transactions — executed in the mainframe are replicated in Google Cloud 
• The outcomes from both systems are validated to report any differences, enabling you to take corrective actions in migrated applications
• The functional and performance differences between the two systems will be observed, and the mainframe and Google Cloud data are periodically synchronized to bring both the systems in sync 

Typically, most of your migration time will be spent in the first stage of Dual Run until you are satisfied with the results. The key goal for this stage is that your primary, business-critical mainframe workload is not disturbed while your migrated application is tuned to provide the exact same results as your current application.

Dual Run stage 2:

When the Dual Run reporting and results confirm that the migrated application matches your mainframe system, Google Cloud then becomes the “primary” system, while your mainframe will still be executed in parallel as “secondary.” Dual Run will enable you to do a smooth switch between primary and secondary systems through a configuration management system.

Target state: In this final state, the mainframe can be decommissioned while the Dual Run components are removed, enabling an optimal and efficient business execution with Google Cloud.

Summary

For any business or organization that has to migrate or modernize their mainframes, Dual Run offers a unique solution to achieve this with reduced risk and time. In fact, what we’re seeing from our customers is that Dual Run offers the right combination of proven experience, engineering, and strategic partnership that is essential for mainframe migration success. If you would like to learn more, check out our mainframe modernization website.

Google Cloud has a genuine passion for solving technology problems that make a difference for our customers. With the release of our BigQuery Connector for SAP, we’re taking a another big step towards solving a major challenge for SAP customers with a quick, easy, and inexpensive way to integrate SAP data with BigQuery, our serverless, highly scalable, and cost-effective multi cloud data warehouse designed for business agility.

Solving for simplified data integration

Like most businesses today, SAP customers are eager to unlock the immediate insights and opportunities within their ever-growing stores of business data. However, many are discovering just how hard it can be to take the first step in any modern, cloud-enabled data analytics strategy: combining SAP data with other cloud-native, and enterprise data sets in real-time and at scale. According to a 2020 SAPInsider study, more than half of SAP customers surveyed said data integration was their top analytics pain point. These companies urgently need a rapid, sustainable, cost-effective and scalable way to integrate SAP data with modern cloud data analytics solutions.

The BigQuery Connector for SAP gives our customers a solution: a fast, simple, cost-effective and massively scalable way to make SAP data fully accessible within BigQuery by leveraging customers’ existing SAP Landscape Transformation Replication Server (SLT) tooling and skill sets. It’s the first SAP SLT direct near real-time connector for BigQuery without the need to set up additional infrastructure or third-party middleware, and can be deployed using a variety of embedded or stand-alone deployment options. In fact, most customers can install the BigQuery Connector for SAP in less than an hour—a remarkably easy way to start working with our industry-leading analytics solution that delivers proven and quantifiable business advantages for customers. Additionally, the BigQuery Connector for SAP is not restricted to customers who have deployed their SAP applications on Google Cloud. Customer’s who are running their SAP applications on-premise, or on any cloud, can also deploy and realize the analytical benefits of the solution.

Designing a solution with customer requirements and investments in mind

When the Google Cloud team started work on an analytics data integration tool for our SAP customers, we began with a set of requirements designed to root out the usual sources of cost and complexity. These included: 

• The need for real-time performance with deltas replicated in milliseconds
• The ability to integrate data from almost any SAP Netweaver based application running today, regardless of its deployment location (on premises, any cloud, Google Cloud)
• Automatic BigQuery data type mapping with minimal transformation required
• Generation of target tables in BigQuery directly from source, if required 
• Application layer integration that avoids the issues of direct database access
• Leveraging customers’ existing SAP skillsets, change data capture, and infrastructure

An important step towards meeting these requirements came when Alphabet, Google’s parent company, decided to leverage SAP SLT as a foundation for developing direct data replication between SAP and BigQuery for its internal corporate landscape. SLT as part of SAP’s strategic Business Technology Platform, supports real-time replication of data from SAP or third-party systems to SAP HANA, however, one of its limitations was direct integration with targets like BigQuery. 

SAP SLT was a logical foundation for developing the connector for several reasons: 

• It’s widely adopted among SAP customers who likely already leverage SLT for SAP analytics data integration
• It works with almost every non-SaaS SAP application environment running today
• It supports real-time replication performance at massive scale

It was an obvious choice for the Alphabet engineering team who saw immediate value from integrating SAP with BigQuery. 

“The BigQuery Connector for SAP has enabled fast, low latency data replication for billions of records from 500+ tables of our most critical financial and supply chain data. Now in one cost-effective BigQuery data lake, this ERP data can be combined with other data sources for previously impossible real-time analytics and ML use cases. This allows us to drive much deeper strategic insights that support business and operational excellence, management and P&L reporting and more.”—Anil Nagalla, Sr. Engineering Director, Financial Systems, Google

SAP data integration with BigQuery enables new value

By leveraging SAP SLT, the BigQuery Connector for SAP can integrate real-time data streams from any SAP system—while also taking advantage of customers’ existing SAP investments and skillsets. 

At the same time, the BigQuery Connector for SAP does a lot of heavy lifting on its own. For example, it automatically handles the complex, multi-step process of transforming SAP data types for use in BigQuery—mapping data-type transitions between the SAP and BigQuery environments, creating a target table schema on BigQuery for the transformed data types, building the target BigQuery table, and even adapting as new data types appear in your SAP environment.

For teams that want to fine-tune the BigQuery Connector for SAP’s automated recommendations, the connector supports additional levels of customization and choice. But if you simply want to get the job done and give your data analytics team greater support for their high-value work, then you’ll love just how quickly and easily the BigQuery Connector for SAP turns the complicated work of data integration and performance to process large volumes of data into a done deal. By integrating enterprise data sets in real time, customers can drive differentiated value and unlock new insights and actions that drive a competitive advantage. 

The BigQuery Connector for SAP really shines as an enabling tool that transports and transforms your SAP data to power analytics solutions enabled by accelerators like the Google Cloud Cortex Framework: a comprehensive set of reference architectures, deployment accelerators, and integration services designed to give SAP customers a fast and seamless path to value with their data analytics investments. Simply put, the more SAP data you make available within Google Cloud, the easier it is to get meaningful—and often game-changing—insights from these solutions.

Learn more about the BigQuery Connector for SAP

Ready to get started with your own SAP data analytics strategy on Google Cloud? Install the Google Cloud BigQuery Connector for SAP, and discover a faster, simpler, more sustainable way to power your company’s data analytics strategy.