Dual Run: A Proven Solution for Secure Mainframe Modernization - Build What's Next
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Dual Run: A Proven Solution for Secure Mainframe Modernization

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Discover how Google Cloud's Dual Run empowers CIOs to mitigate risks, reduce testing effort, and accelerate mainframe migration while ensuring a seamless and secure transition to the cloud. Read now!

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:

https://storage.googleapis.com/gweb-cloudblog-publish/images/1_Exploring_Dual_Run.max-1800x1800.jpg

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.

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LearningMate & Google Cloud Partnership to Aid Equitable Educational Opportunities

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Google Cloud and LearningMate designed, Student Success Services closes the digital gap among students and helps them learn with intelligent, user-friendly and personalized experiences built on AI and analytics tools. Explore more!

A “one size fits all” approach to education no longer works in today’s classrooms. Using cloud-based technologies, schools and educators can take a more personalized approach to education–one that suits each student’s unique learning style, abilities, and needs. 

Taking the lead toward more equitable educational opportunities worldwide, education technology pioneer, LearningMate, is partnering with Google Cloud to offer intelligent, personalized learning via Google Cloud’s Student Success Services.

In a student survey by EDUCAUSE, a nonprofit association whose mission is to advance higher education through the use of information technology, nearly all respondents asked for more digital learning and study options. Given a list of educational material types, such as study guides and recorded lessons, 93 percent said they would like to have online access to at least two options and more than half (56 percent) chose seven or more. 

The trend towards student choice challenges educators to reconsider how they teach and support learners. Students expect the same qualities in their lessons as they encounter in their other, non-school related digital experiences: personalization; user-friendliness; and engagement. Educators who are used to a more top-down education model can struggle to meet these new expectations. 

Google Cloud created Student Success Services to help education institutions meet learners where they are—in the digital age. This suite of tools and services uses Google’s advanced artificial intelligence (AI) and analytic tools to:

  • Engage with students via an AI-powered learning platform and interactive tutor
  • Help educators and learners collaborate more effectively
  • Provide current, actionable data on student progress 

To bring our Student Success Services to more schools and organizations worldwide, we’re partnering with LearningMate — a global leader in digital learning infrastructure. LearningMate is a key go-to-market partner, helping schools design, launch, and maintain their own learning infrastructure. To start, LearningMate is adding Google’s AI-powered learning platform to its Frost platform, a popular content management for education. 

As the education model continues to change and digital learning becomes the norm, disadvantaged students risk getting left behind. For these learners, the “digital divide” is very real, and stands to hinder them further–not only in school, but in society and the workplace in later life. 

“The gap will only widen between those with digital advantages and those who struggle to gain access to devices and network necessities,” EDUCAUSE states. To meet all these challenges, schools need a diverse mix of tools, methods, and partnerships.

To help close the digital divide among students, digital tools need to be able to scale up or down so institutions and districts of any size and budget can use them. Google Cloud designed Student Success Services with this ability in mind. By offering these services, LearningMate aims to ensure that even schools with smaller teams and fewer resources can offer individualized learning experiences to their students. 

With LearningMate and Google’s nearly 40 years of combined experience in education, this partnership aims to help educators better understand their students’ engagement, performance, and preferences. To learn more about Student Success Services and our collaboration with LearningMate, watch this session from our Government and Education Summit.

Case Study

Google Cloud and Univision Partnership to Up the Ante in UX for Spanish-speaking Audience

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Consumption of content from streaming platforms grew exponentially in the last year, driving entertainment and media platforms to make meaningful usage of audience data for personalization, better UX and enhanced viewing experience. Spanish-language content and media company, Univision leveraged Google Cloud's AI, ML and data analytics tools to unveil useful insights that enhance engagement of it's global, Spanish-speaking audience.

The past year has given everyone lots to think about—about our priorities as people and as businesses. As the world retreated behind closed doors, we saw how shared interests and experiences can bring us together. As the world grappled with a common enemy, we witnessed just how differently individuals, communities and indeed entire countries can experience a situation. And as we faced seemingly unending obstacles to making it through the pandemic, we saw how making smart decisions based on data can drive meaningful solutions—fast.

That’s why we here at Google Cloud are so proud to partner Univision, the country’s leading Spanish-language content and media company. By partnering with Google Cloud, Univision will be able to accelerate growth across its portfolio of properties, deliver an enhanced user experience for Spanish-speaking audiences and provide the enterprise solutions needed to create the Spanish-language media company of the future.

According to Instituto Cervantes, there are over 580 million Spanish language speakers worldwide. Those viewers, like people everywhere, are avid consumers of streaming content. In Q4 of 2020 alone, viewing time for that content increased by 44%1, and in 2020, from 50%2 more sources. With that surge in demand, Univision needed a cloud provider whose infrastructure could reach Hispanic viewers around the world. With two-plus decades spent building out its network and data centers, as well as global content-delivery capabilities, Google Cloud has the infrastructure Univision needs to reach viewers across the Spanish-speaking world.

At the same time, with such a diverse audience for their content, Univision needs to target that content to viewers’ specific preferences. By applying Google Cloud’s artificial intelligence (AI) and machine learning (ML) technology across its content, Univision intends to personalize content based on shows users have previously watched, enhancing their engagement and viewing experience. 

And as Univision transforms the user experience, it can use Google Cloud’s data and analytics suite to garner deeper insights into its audience and forge stronger relationships with them on an individual basis. With Looker and BigQuery, Univision employees will have access to real-time data to help them make business decisions about programming.

Univision will also migrate video distribution and production operations to Google Cloud, where we’ll help them streamline media workflows and develop innovative new capabilities. Meanwhile, Google Cloud’s tight business and technical integration with other Google services will help ensure Univision reaches viewers on the device of their choice, wherever they are in the world. For example, in the coming years, Univision will expand its global YouTube partnership and will integrate with entertainment features on Google Search that help people better discover TV shows and movies. The company will also use Google Ad Manager for global ad decisioning and Google’s Dynamic Ad Insertion for PrendeTV and future video-on-demand offerings. Finally, Univision will distribute its content and services on Google Play across Android phones and tablets, as well as Google TV and other Android TV OS devices.

We’re thrilled to partner with Univision to help them reach the Spanish-speaking world with their content. With our cloud portfolio, we can help them reach individual viewers around the world, with personalized content that they can consume however they see fit. Best of all, together, we can help them achieve this vision fast, leveraging established cloud, content delivery, and data analytics technologies. You can learn more about the partnership here.

How-to

How to Pick a Database that is Suitable for Your Application

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Read the post to explore your options within Google Cloud across relational (SQL) and non-relational (NoSQL) databases, along with use cases to pick the best for your application!

Picking the right database for your application is not easy. The choice depends heavily on your use case—transactional processing, analytical processing, in-memory database, and so on—but it also depends on other factors. This post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option. 

DB Sketch
Click to enlarge

Relational databases 

In relational databases information is stored in tables, rows and columns, which typically works best for structured data. As a result they are used for applications in which the structure of the data does not change often. SQL (Structured Query Language) is used when interacting with most relational databases. They offer ACID consistency mode for the data, which means:

  • Atomic: All operations in a transaction succeed or the operation is rolled back.
  • Consistent: On the completion of a transaction, the database is structurally sound.
  • Isolated: Transactions do not contend with one another. Contentious access to data is moderated by the database so that transactions appear to run sequentially.
  • Durable: The results of applying a transaction are permanent, even in the presence of failures.

Because of these properties, relational databases are used in applications that require high accuracy and for transactional queries such as financial and retail transactions. For example: In banking when a customer makes a funds transfer request, you want to make sure the transaction is possible and it actually happens on the most up-to-date account balance, in this case an error or resubmit request is likely fine.

There are three relational database options in Google Cloud: Cloud SQL, Cloud Spanner, and Bare Metal Solution.

Cloud SQL: Provides managed MySQL, PostgreSQL and SQL Server databases on Google Cloud. It reduces maintenance cost and automates database provisioning, storage capacity management, back ups, and out-of-the-box high availability and disaster recovery/failover. For these reasons it is best for general-purpose web frameworks, CRM, ERP, SaaS and e-commerce applications.

Cloud Spanner: Cloud Spanner is an enterprise-grade, globally-distributed, and strongly-consistent database that offers up to 99.999% availability, built specifically to combine the benefits of relational database structure with non-relational horizontal scale. It is a unique database that combines ACID transactions, SQL queries, and relational structure with the scalability that you typically associate with non-relational or NoSQL databases. As a result, Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking and inventory management that require ability to scale limitlessly with strong-consistency and high-availability. 

Bare Metal Solution: Provides hardware to run specialized workloads with low latency on Google Cloud. This is specifically useful if there is an Oracle database that you want to lift and shift into Google Cloud. This enables data center retirements and paves a path to modernize legacy applications. 

Non-relational databases

Non-relational databases (or NoSQL databases) store compex, unstructured data in a non-tabular form such as documents. Non-relational databases are often used when large quantities of complex and diverse data need to be organized. Unlike relational databases, they perform faster because a query doesn’t have to access several tables to deliver an answer, making them ideal for storing data that may change frequently or for applications that handle many different kinds of data. 

For example, an apparel store might have a database in which shirts have their own document containing all of their information, including size, brand, and color with room for adding more parameters later such as sleeve size, collars, and so on.

Qualities that make NoSQL databases fast:

  • Eventual consistency: stores usually exhibit consistency at some later point (e.g., lazily at read time)
  • Horizontal scaling, usually using hashed distributions
  • Typically, they are optimized for a specific workload pattern (i.e., key-value, graph, wide-column)
  • Typically, they don’t support cross shard transactions or flexible isolation modes.

Because of these properties, non-relational databases are used in applications that require large scale, reliability, availability, and frequent data changes.They can easily scale horizontally by adding more servers, unlike some relational databases, which scale vertically by increasing the machine size as the data grows. Although, some relations databases such as Cloud Spanner support scale-out and strict consistency.

Non-relational databases can store a variety of unstructured data such as documents, key-value, graphs, wide columns, and more. Here are your non-relational database options in Google Cloud: 

  • Document databases: Store information as documents (in formats such as JSON and XML). For example: Firestore
  • Key-value stores: Group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. For example: Datastore, Bigtable, Memorystore
  • In-memory database: Purpose-built database that relies primarily on memory for data storage. These are designed to attain minimal response time by eliminating the need to access disks. They are ideal for applications that require microsecond response times and can have large spikes in traffic. For example: Memorystore
  • Wide-column databases: Use the tabular format but allow a wide variance in how data is named and formatted in each row, even in the same table. They have some basic structure while preserving a lot of flexibility. For example: Bigtable
  • Graph databases: Use graph structures to define the relationships between stored data points; useful for identifying patterns in unstructured and semi-structured information. For example: JanusGraph

There are three non-relational databases in Google Cloud:

  • Firestore: Is a serverless document database which scales on demand and acts as a backend-as-a-service. It is DBaaS that increases the speed of building applications. It is perfect for all general purpose uses cases such as ecommerce, gaming, IoT and real time dashboards. With Firestore users can interact with and collaborate on live and offline data making it great for real-time application and mobile apps.  
  • Cloud Bigtable: Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, enabling you to store terabytes or even petabytes of data. It is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at sub-millisecond latency, and it is an ideal data source for MapReduce operations. It also supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark along with Google Cloud ecosystem.
  • Memorystore: Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. It is best for in-memory and transient data stores and automates the complex tasks of provisioning, replication, failover, and patching so you can spend more time coding. Because it offers extremely low latency and high performance, Memorystore is great for web and mobile, gaming, leaderboard, social, chat, and news feed applications.

Conclusion

Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your application requires ACID transactions and your data structure is not going to change much, select a relational database. 

In Google Cloud use Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability is a bigger requirement than consistency then a non-relational database is a preferable choice.  Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide column database spectrum.

For more comparison resources on each database check out the overview. For more hands-on experience with Bigtable, check out our on-demand training here and learn about migrating databases to managed services check out this whitepaper.  

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For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

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Google Cloud Region in Columbus to Accelerate Ohioan Businesses and Tech Transformation

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After creating over 200 jobs in the State of Ohio, Google Cloud region in Columbus is slated to add flexibility to the region distribute workloads across the central, midwest and eastern U.S.!

Digital tools such as cloud computing are fueling economic transformation across the US, including Ohio. Google continues to invest across cities and communities in Ohio, bringing over 200 jobs to the state, and helping provide $12.85 billion of economic activity for tens of thousands of Ohio businesses, nonprofits, publishers, creators and developers. To further accelerate the transformation of all Ohioan businesses and technologists, we’re thrilled to announce our newest Google Cloud region in Columbus, Ohio is open. The Columbus cloud region brings a second region to the Midwest, the 10th region to North America, and grows our global cloud region count to 33.

A region for the Buckeye State


Now open to Google Cloud customers, the Columbus region (us-east5) provides you with the speed and availability you need to innovate faster, build high-performing applications, and serve local customers — all on the cleanest cloud in the industry. Additionally, the region gives you added flexibility to distribute your workloads across the central, midwest, and eastern US.

The Columbus region offers immediate access to three zones, for high availability workloads, and our standard set of products, including Compute Engine, Google Kubernetes Engine, Cloud Storage, Persistent Disk, CloudSQL, and Cloud Identity. Our private backbone connects Columbus to our global network more quickly and securely. In addition, you can integrate your on-premises workloads with our new region using Cloud Interconnect. This means that Columbus-based customers can expand globally from their front door, and those based outside the region can more easily reach their users in the Midwest.

What customers are saying


Industries including retail, financial services, and IT are investing in Columbus. Organizations across these verticals have turned to the Google Cloud to innovate faster and help solve their most complex challenges

“As Wendy’s continues to innovate in new ways to create fast, frictionless, and fun interactions that redefine the way customers visit and enjoy our restaurants, our partnership with Google Cloud is a key enabler to delivering on our AI/ML and data analytics strategies. The proximity of the new Google Cloud region to Wendy’s headquarters provides the ability for us to move and scale quickly as business needs evolve. Additionally, Google Cloud’s investment in Columbus positions central Ohio as a true technology hub, which further boosts Wendy’s and other regional employers’ ability to recruit innovative talent,” said Kevin Vasconi, Chief Information Officer, Wendy’s.

“Huntington National Bank’s API Architecture is a central component to our growth and technology strategy. As our business segments grow from an offering and geographic perspective, we must evolve our technology to provide the optimal experience for our customers and our partners. Collaborating directly with Google Cloud on the build out of their cloud region in Central Ohio, provides the access our technology teams need to innovatively scale our infrastructure to meet the demands of our business with increased availability, lower latency, and greater resiliency,” said Geoff Preston, Chief Architect, Huntington National Bank.

“Google Cloud has been instrumental in our ability to scale and optimize data management and compute resources. We prioritize scale, elasticity and resilience in cloud services and Google Cloud delivers all three globally and locally. With Google Cloud security, we can efficiently process the quantities of application data required to accelerate alert detection and reduce response times for the critical infrastructure our customers depend on to enable the continuity of their vital applications.” said Sheryl Haislet, Chief Information Officer at Vertiv, a global provider of critical digital infrastructure and continuity systems headquartered in Columbus, Ohio, that leverages Google Cloud solutions to provide resilience for its operations and to better support customers.

“The addition of the new cloud region in Ohio continues to demonstrate Google Cloud’s commitment to the enterprise space and their presence in the region,” said Chris Delong, Chief Technology Officer, Designer Brands Inc. / DSW

What’s next


We are thrilled to welcome you to our new cloud region in Columbus, and eagerly await to see what you build with our platform. Register here for our Cloud Study Jam in June – an event for local developers to get hands-on training with Google Cloud. Stay tuned for more region announcements and launches this year, including our next U.S. region in Dallas, TX. And for more information, contact sales to get started with Google Cloud today.

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IDC Survey: Why 95% of CEOs Have a Digital-first Strategy

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CEOs want to acquire greater digital know-how according to an IDC CEO survey. CEOs say about 30% of their organization’s revenue comes from digital products, services and experiences. Learn more.

When IDC recently asked CEOs what single word most reflects what their organization needs to thrive in 2022, the overwhelming response was “technology.” CEOs want to acquire greater digital know-how as they recognize that technology will underpin the business model of the future.

Digital transformation, already well underway, is becoming digital first. 

In fact, an astonishing 95% of CEOs see the need to adopt a digital-first strategy, according to IDC, and the majority of organizations are down the path of executing their plans. Currently, they report that about 30% of their organization’s revenue comes from digital products, services and experiences, a figure that will reach past 40% by 2027. 

But what will power this digital-first enterprise? Successful large-scale digital transformations are executed by a diverse set of partners and vendors—including a cloud partner, valuable channel partners and resellers, independent software vendors (commonly called ISVs), and services partners; multiple cloud partners are even becoming quite common.

These networks of experts are increasingly the driver behind enterprise transformation. They help companies and organizations to establish an actively managed and governed cloud ecosystem that integrates native and third-party solutions, which can create shared value for all partners from the latest digital technologies. In fact, 85% of CEOs told IDC that actively participating in digital ecosystems is important to their revenue growth.

The new digital agenda is built on cloud ecosystems

Recently, I sat down with the author of the IDC CEO survey, Philip Carter. He’s the group vice president, European chief analyst, and worldwide C-suite tech research lead at IDC, and I wanted to better understand from him how executives are approaching their digital-first mission. 

He explained that CEOs are turning to cloud platforms as a primary partner to put in place new, industry-specific digital business models. These platforms are at the center of “industry value networks” that accelerate time to value and benefit from cloud architecture’s ability to deliver scale, extensibility, and AI-driven experiences to create new revenue streams. 

At Google Cloud, we have made building such industry value networks a priority, comprising of: 

  • Google Cloud, which provides the infrastructure and develops industry-specific data and analytics capabilities using artificial intelligence.
  • Broader Google product areas like Google Shopping, Ads, Maps, Pay, Assistant, Android and Chrome.
  • Independent software vendors (ISVs) relevant to a specific industry, who co-innovate with Google to provide end-to-end solutions to industry problems.
  • System integrators (SIs) with deep industry knowledge who build the strategy and implement the business transformation for customers.

As an example, Google Cloud customers across multiple industries are looking for digital transformation to reinvent their core functions, such as supply chain. They might be identifying new distribution channels or restructuring their inventory. Google Cloud understands how to link the enterprise ecosystem with the consumer ecosystem. We can help enterprises access this broader ecosystem while leveraging AI-driven insights from data to help inform decisions in real time, at the point of engagement.

Google Cloud’s partner ecosystem can help achieve multiple items on the C-suite tech agenda such as improving customer experience, powering enterprise intelligence, building trust, supporting a distributed workplace, driving innovation, and streamlining operations. 

According to IDC, 75% of all organizations will be completely digitally transformed by 2030. Google Cloud has been working hard with our partners to create an extensive ecosystem that can help your organization achieve its digital transformation ahead of competitors. This includes co-developing and co-innovating industry-specific solutions with a host of partners in key areas such as operations, enterprise intelligence, customer experience, workplace productivity, innovation, security, and trust. 

And over the next few years, Google Cloud will double its investment in its partner ecosystem with increased co-innovation resources, more incentives, and co-marketing campaigns and funding. We’ll also offer greater integration into the Google Cloud Marketplace and a larger commitment to training and enablement. 

For more insights on the C-suite “Future of Enterprise” agenda, you can watch my full IDC interview. You might also download our ebook featuring IDC data on the future of enterprise with ecosystem examples from partners like AMD, Confluent, Fortinet, Genesys, Informatica, and Workspot.

Our goal is to provide comprehensive best-in-class support for our mutual customers’ digital transformations. As we look toward that goal, one thing is for sure: Our ecosystem of partners will continue to grow, adding solutions to meet the needs of digital-first enterprises. Care to join us on that journey?

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