For many enterprises, the venerable mainframe is home to decades’ worth of data about the company’s customers, processes and operations. And it goes without saying that the business would like access to that mainframe data — to report on it, to analyze it with big data analysis tools, or to use it as the basis of new machine learning and artificial intelligence initiatives.

At Google Cloud, we are eager to work with organizations to help them transform their mainframe assets for the cloud era. Of course, we can help them modernize their mainframe applications by migrating them to the cloud. At the same time, working with partners and customers, we’ve developed another, more lightweight approach that can help them start to leverage the cloud for their mainframe assets much more quickly than performing a full-fledged migration. We call this approach data-first digitization.   

In this rapidly evolving digital ecosystem, it’s imperative to understand the difference between ‘modernization’ and ‘digitization.’ With modernization you start with the current state and look forward, and rely on mainframe application migration approaches such as rehosting (emulation), refactoring (automated code transformation), reengineering — or simply replacing a custom application with a commercial package. With digitization, you start with the future state that you want to achieve, and work back to what is required to get there.

This data-first digitization approach includes a mainframe data-first integration framework comprising in-house and partner products and tools to migrate heterogeneous data sources from the mainframe to Google Cloud Storage. Once mainframe data has been copied to Cloud Storage, it can then be integrated and leveraged by Google Cloud tools such as BigQuery, AI and machine learning prodcuts  and Smart and Stream analytics platforms. The integration framework covers both bulk batch data transfers and real-time data replication (change data capture).

Data-first digitization is based on the tenet that ‘applications are transient, data is permanent.’ By bringing data first to Google Cloud instead of traditional ways of modernizing applications (for example, with Gartner’s 7 options to Modernize), this allows organizations to leapfrog to new business models, use cases and innovative ways to serve end customers. For example:

• Making decisions with smart and stream analytics platforms and AI/ML engines. These tools need data to make decisions. Google is a pioneer in extracting information and value from the raw structured and unstructured data, and this approach opens up mainframe data for use by BigQuery and AI/ML models. 
• Building new reporting applications. With access to mainframe data, you can use Google cloud products like Looker and Appsheet to build net-new reporting applications, expediting the process of retiring mainframe reporting applications, and accelerating your overall transformation.

In our experience, taking a data-first digitization approach to your mainframe offers a number of benefits:

1. Faster time-to-business: Because data-first modernization is built on existing products, the implementation cycle is much shorter.
2. Less capital investment: You spend your time integrating products, not developing applications.
3. Minimized risk: Data-first integrates with existing, proven and reliable Google Cloud products.
4. Faster overall mainframe transformation: When you shift your modernization center of gravity from the application to the data, you look at mainframe applications from a business perspective instead of just “keeping the lights on.” As a result, only the most business-critical applications are modernized and many support applications can be decommissioned, accelerating your transformation journey. 

Taking a data-first approach to digitization is still relatively new, but we’re heartened by customers’ early successes. Watch this space for additional insights, reference architectures and technical white papers around data-first. And if you think this approach may be right for you, reach out to mainframe@google.com.

Learn more:

• Liberating your mainframe data with Confluent and Google Cloud
• Beyond mainframe modernization: The art of possibilities

The typical smart factory is said to produce around 5 petabytes of data per week. That’s equivalent to 5 million gigabytes, or roughly 20,000 smartphones.

Managing such vast amounts of data in one facility, let alone a global organization, would be challenging enough. Doing so on the factory floor, in near-real-time, to drive insights, enhancements, and particularly safety, is a big dream for leading manufacturers. And for many, it’s becoming a reality, thanks to the possibilities unlocked with edge computing.

Edge computing brings computation, connectivity, and data closer to where the information is generated, enabling better data control, faster insights, and actions. Taking advantage of edge computing requires the hardware and software to collect, process, and analyze data locally to enable better decisions and improve operations.

At Hannover Messe 2022, Intel and Google Cloud will demonstrate a new technology implementation that combines the latest generation of Intel processors with Google Cloud’s data and AI expertise to optimize production operations from edge to cloud. This proof-of-concept project is powered by the Edge Insights for Industrial platform (EII), an industry-specific platform from Intel; and a pair of Google Cloud solutions: Anthos, Google Cloud’s managed applications platform, and the newly-launched Manufacturing Data Engine.

Edge computing exploits the untapped gold mine of data sitting on-site and is expected to grow rapidly. The Linux Foundation’s “2021 State of the Edge” predicts that by 2025, edge-related devices will produce roughly 90 zettabytes of data. Edge computing can help provide greater data privacy and security, and can accomodate the reduced bandwidth needs between local storage and the cloud.

Imagine a world in which the power of big data and AI-driven data analytics is available at the point where the data is gathered to inform, make, and implement decisions in near real-time.

This could be anywhere on the factory floor, from a welding station to a painting operation or more. Data would be collected by monitoring robotic welders, for example, and analyzed by industrial PCs (IPCs) located at the factory edge. These edge IPCs would detect when the welders are starting to go off spec, predicting increased defect rates even before they appear, and adding preventive maintenance to correct the errors without any direct intervention. Real time, predictive analytics using AI could substantially prevent defects before they happen. Or the same IPCs could use digital cameras for visual inspection to monitor and identify defects in real-time, allowing them to be addressed quickly.

Edge computing has powerful potential applications in assisting with data gathering, processing, storage and analysis in many manufacturing sectors, including automotive, semiconductor and electronics manufacturing, and consumer packaged goods. Whether modeling and analysis is done and stored locally or in the cloud, or is predictive, simultaneous, or lagged, technology providers are aligning to meet these needs. This is the new world of edge computing.

The joint Intel and Google Cloud proof of concept aims to extend the Google Cloud capabilities and solutions to the edge. Intel’s full breadth of industrial solutions, hardware and software, are coming together in this edge-ready solution, encompassing Google Cloud industry-leading tools. The concept shortens the time to insights, streamlining data analytics and AI at the edge.

The Intel-Google Cloud proof of concept demonstrates how manufacturers can gather and analyze data from over 250 factory devices using Manufacturing Connect from Google Cloud, providing a powerful platform to run data ingestion and AI analytics at the edge.

In this demonstration in Hannover, Intel and Google Cloud show how manufacturers can capture time-series data from robotic welders to inspect welding quality and show how predictive analytics can benefit the factory operators. In addition, the video and image data is captured from a factory camera to show how visual inspection can highlight anomalies on plastic chips with model scoring. The demo also features zero-touch device onboarding using FIDO Device Onboard (FDO) to illustrate the ease with which additional computers could be added to the existing Anthos cluster.

By combining Google Cloud’s expertise in data, AI/ML and Intel’s Edge Insight’s for Industrial platform that was optimized to run on Google Anthos, manufacturers can run and manage their containerized applications at the edge, in on-premise data center, or in public clouds using an efficient and secure connection to the Manufacturing Data Engine from Google Cloud. It forges a complete edge-to-cloud solution.

Simplified device onboarding is available using Fido Device Onboard (FDO)—an open IoT protocol that brings fast, secure, and scalable zero-touch onboarding of new IoT devices to the edge. FDO allows factories to easily deploy automation and intelligence in their environment without introducing complexity into their OT infrastructure.

The Intel-Google Cloud implementation can analyze that data using localized Intel or third-party AI and machine learning algorithms. Applications can be layered on the Intel hardware and Anthos ecosystem, allowing customized data monitoring and ingestion, data management and storage, modeling, and analytics. This joint PoC facilitates and support improved decision making and operations, whether automated or triggered by the engineers on the front lines.

Intel collaborates with a vibrant ecosystem of leading hardware partners to develop solutions for the industrial market by using the latest generation of Intel processors. These processors can run data intensive workloads at the edge with ease.

Putting data and AI directly into the hands of manufacturing engineers can improve quality inspection loops, customer satisfaction, and ultimately the bottom line.

The new manufacturing solutions will be demonstrated in person for the first time at Hannover Messe 2022, May 30–June 2, 2022. Visit us at Stand E68, Hall 004, or schedule a meeting for an onsite demonstration with our experts.

As an IT leader, you’re asked to do it all: innovate and optimize your tech stack for business outcomes — all while being secure and compliant. It takes heroic efforts to achieve innovation and progress while also tightening budgets and teams. This is why many of you are considering migrating applications to Google Cloud, for benefits like scalability and flexibility, security and compliance, disaster recovery and business continuity, and cutting-edge technologies at lower costs.

To help you do this, we suggest using Google Cloud’s Migrate to Virtual Machines – part of Migration Center. This managed cloud service lets you lift and shift workloads at scale to Google Cloud Compute Engine with minimal changes and risk.

And recently, we rolled out our latest release which introduces support for workload migration from AWS to Google Compute Engine. With this addition, you can now migrate both your on-prem and AWS workloads at scale. This means centralized management for your end to end workload migration journey from both sources via Cloud Console or APIs. 

Simple and easy migrations from AWS and VMware sources to Google Cloud

Migration of AWS EC2 instances directly to Google Compute Engine using Migrate to Virtual Machines follows a well established and easy journey, which means a minimal learning curve for users who are already migrating workloads from VMware. Workload migration is agent-less, which means you do not need to access or alter workloads as a prerequisite for migration, allowing you to execute zero-touch migrations. Migrate instance data with no interruptions to the running workload at the source for a fast cutover to Google Cloud. In addition, our end-to-end cloud console interface surfaces your AWS EC2 inventory, migrations, and groups so you can execute migrations without ever leaving the cloud console interface. 

Large-scale migrations 

Completing a large-scale migration project in a timely manner calls for careful planning and streamlined migration sprints. The Migrate to Virtual Machines’ Groups construct enables you to group source VMs together in the planning phase. When it’s time to execute the planned migration, VM Groups let you execute migration operations on a group level, or on a subset of the group, streamlining the process at scale.

Minimal downtime and risk

Application uptime is key to keeping your business running. Every migration with this latest release of the service periodically replicates data from the source workload to the destination without manual steps or interruptions to the running workload, minimizing workload downtime and enabling fast cutover to Google Cloud. You can also launch non-disruptive migration tests — referred to as test-clones — to help you validate that these workloads will work properly in the cloud before cutting over. This helps avoid issues that might have otherwise been costly or disruptive to your business. 

How the service works

Migrations simply work, at scale, in a managed service fashion. With Migrate to Virtual Machines, there’s no requirement to provision or manage migration-specific resources in the cloud. The service uses replication-based migration technology to lift and shift workloads from source environments to Google Cloud. The Migrate Connection replicates source VM disk snapshots in the background with no interruption to the source workload. Replicated data is encrypted in transit and at rest, and when you instantiate a migrating VM using a test-clone or cut-over, the service seamlessly adapts your source VM operating system to boot and run natively in the cloud — including configuring network settings and deploying Google Cloud guest packages. 

The migration journey of an EC2 instance — or VMware VM — to Google Cloud is comprised of the following steps:

1. Onboarding a source VM for migration: Onboard one or more VMs for migration from the source environment fleet.

2. Configure landing zone target: You can migrate an instance to any Google Cloud project in your environment and update landing zone details at any time before executing a test-clone or cutover.

3. Initiate VM data replication of source workload: Migrate to Virtual Machines periodically replicates instance disks to the cloud with no interruption to the source instance. You can control replication frequently and pause or resume at any point in time. 

4. Test migrating instance: Test-clone creates a copy of your source instance in the defined landing zone to validate the migrating instance in the cloud before executing a cut-over. You can repeat the test-clone multiple times to multiple landing zones for thorough validation

5. Cutover migrating instance: Cutover operation shuts down your source instance and then performs the short final sync to Google Cloud. Migrated VM is instantiated in the target landing zone.

Getting started with Migrate to Virtual Machines 

It’s quick and easy to start migrating your AWS EC2 instances and on-premises VMs today:

1. Enable the vmmigration API in a Google Cloud project 
2. Create an AWS source in your environment
3. Onboard and initiate replication of instance data from source 
4. Set migrating instance target details. 
5. Perform non-disruptive tests of your migrating instance using test-clone
6. Cutover your instance to the cloud with minimal down time

You can also visit our website to learn more about Migrate to Virtual Machines. If you know you have to migrate in 2023 but aren’t sure how to get started, you can sign up for a free discovery and assessment of your current IT landscape so we can help craft the ideal migration plan for you and your business.