We’re excited to announce that Spring Cloud GCP version 4.0 is now generally available! In this post, we’ll be describing what the new major version has to offer, and demonstrating the process of using the migration guide on one of our reference architectures, Bank of Anthos.

What’s new?

With this release, Spring Cloud GCP officially supports Spring Boot 3.x. However, this migration involves a number of breaking changes as outlined in the migration guide. The full list of changes made is available on Github, but the one of the most significant differences is that Java 17 is now a minimum requirement.

Another notable feature of this release is the addition of starter artifacts – Spring Boot starters for Google Cloud – that provide dependencies and auto-configurations for 80+ Google Client libraries. Just as the name suggests, these starters can serve as helpful starting points when working with a new client library. For now, they’re in preview. 

These starters are not included in the BOM by default and need to be added as a dependency to your project before they can be used. For example, if you wanted to get started with Cloud Text-to-Speech, you would include the following:

<dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-texttospeech-spring-starter</artifactId>
    <version>4.0.0-preview</version>
  </dependency>

The upgrade process

We prepared a migration guide to help answer any questions involved with moving from 3.x to 4.x.  Let’s follow those instructions to migrate Bank of Anthos.

We’ll start by cloning and building the application before the upgrade, according to the quickstart and development guide:

PROJECT_ID=<YOUR-PROJECT-ID>
gcloud services enable container --project ${PROJECT_ID}

git clone https://github.com/GoogleCloudPlatform/bank-of-anthos.git
cd bank-of-anthos/

gcloud services enable container.googleapis.com monitoring.googleapis.com \
  --project ${PROJECT_ID}

REGION=us-central1
gcloud container clusters create-auto bank-of-anthos \
  --project=${PROJECT_ID} --region=${REGION}

gcloud container clusters get-credentials bank-of-anthos \
  --project=${PROJECT_ID} --region=${REGION}

skaffold run --default-repo=gcr.io/${PROJECT_ID}/bank-of-anthos

At the end of this, you should see a “deployment stabilized” message:

Deployments stabilized in 9.657 seconds

With the environment set up for development, we’re ready to move on to the actual migration. The migration is already complete at time of writing, but here’s the full list of changes made:

• We began by checking the current Java, Spring Boot and Spring Cloud GCP versions.
  • Our Java version is okay, but the other two are out of date, and will need to be updated.
    We can also add the Spring Cloud GCP BOM to manage our versions going forward while we’re here.
• Then, in no particular order, we can make the following code changes:
  • Remove the explicit type declarations from our ResponseEntity instantiations.
  • Replace all javax.* imports with Jakarta.* counterparts, since Spring 3.0 makes use of Jakarta EE 9.0

With those changes in place, the app is upgraded and ready to re-deploy!

Live example

For a finished example of this migration, check out the Bank of Anthos repository on Github. It’s an excellent sample application that showcases a polyglot Java & Python app, served on Kubernetes and Google Cloud. All CI/CD and configurations are open source, so it may be instructive as you approach this migration.

Thanks for reading, and feel free to provide comments or feedback on Twitter, or in the issues section of the repository.

When a software change is deployed — after being designed, coded, tested, packaged, and tested some more — a journey comes to an end. At the same time, a new journey begins: your customer’s relationship with your service. It’s here, in the domain of operations, that abstract risks like launch schedule slippage give way to tangible risks like lost revenue, degraded trust, and tarnished reputation. Only when it’s available to users can software contribute to (or threaten!) the success of your organization. And so, throughout the past several years, the DevOps Research and Assessment (DORA) project has incrementally deepened our research into the reliability of services, through and beyond deployment, into ongoing operation.

Reliability is a broadly defined term, which refers to a team’s ability to meet their users’ expectations — for software services, it may encompass aspects of availability, latency, correctness, or other characteristics that influence the consistency and quality of user experience. Google’s practice of Site Reliability Engineering (SRE), which has been embraced and extended by a global community of reliability engineering practitioners, is an approach to operations that prioritizes user-oriented measurement, shared responsibility, and collaborative, blameless learning. Starting with the 2021 Accelerate State of DevOps Report, we began asking survey respondents detailed questions about reliability engineering in their organizations. We continued and expanded our investigation in 2022, and found further evidence that modern reliability engineering is widespread: a majority of respondents report that they employ SRE-style practices. With this extensive body of data to draw from, this year we pushed further into analyses of the impact of reliability and its interaction with other dynamics present in our model of technology’s influence on organizational success.

Reliability matters

When reliability is poor, improvements to software delivery have no effect — or even a negative effect — on organizational outcomes

Reliability is more than beneficial: it’s essential. As in prior studies, we find that software delivery performance (as measured by the “four key metrics” of change lead time, deploy frequency, change failure rate, and failure recovery time) is predictive of organizational performance. However, this year’s analysis revealed a previously unseen nuance: the influence of software delivery on organizational performance is predicated on reliability. When reliability is high, high-performance software delivery predicts better outcomes for the organization. But when reliability is poor, improvements to software delivery have no effect — or even a negative effect — on organizational outcomes. This affirms a long-held belief among reliability engineers: “reliability is the most important feature of any system.” If a service or product doesn’t meet its users’ reliability expectations, it’s counter-productive to rapidly ship flashy new features, because users can’t properly experience them. Software delivery relies on a foundation of reliability to create value.

https://storage.googleapis.com/gweb-cloudblog-publish/images/dora.max-900×900.jpg

Reliability is a journey

Any experienced leader will tell you that progress is rarely linear: even with a discipline like SRE, widely practiced and with demonstrable benefits, the path to success is unlikely to follow a straight line. DORA describes the “J-Curve” of organizational transformation, a phenomenon in which durable success comes only after setbacks and lessons learned. This year, we compared the depth of teams’ reliability engineering practices to their impact on the services they provide: will an investment in SRE produce greater reliability? The answer is yes, but with a significant caveat: not at first. Comparing reliability outcomes across a range of levels of SRE adoption, the J-Curve is plainly visible. A team which practices SRE only lightly — at the beginning of their SRE journey, perhaps — is likely not only to not benefit, but to regress in terms of the reliability experienced by their users. However, after these practices have more deeply permeated, an inflection point is reached and we see strong reliability benefits from continuing to grow the reliability engineering capability.

Knowing that it will likely take time to realize the benefits of adopting SRE, it may be tempting to start the process as soon, and as broadly, as possible. But we offer a note of caution here: organization-wide cultural transformation initiatives typically fail from overreach. We studied this and reported findings in a previous report. And even if you manage to beat the odds and fully adopt SRE across multiple teams simultaneously, the cost may be unacceptable: the setbacks in reliability that you are likely to experience early on, amplified across an entire organization all at once, could have catastrophic consequences. Therefore the SRE principle of gradual change should also be applied to the adoption of SRE itself.

Reliability is about people

Reflecting back on over a decade of SRE practice and theory, the Enterprise Roadmap to SRE underlines the importance of culture, suggesting that Site Reliability Engineering is in fact emergent from culture. Tools and frameworks are important; language is essential. But only a trustful, psychologically safe culture can support the environment of continuous learning which enables SRE to manage today’s complex, dynamic technology environments. DORA’s research in 2022 demonstrates the interplay between culture and reliability: we found that “generative” culture, as defined by the Westrum model, is predictive of higher reliability outcomes. And reliability has benefits not only for a system’s users, but for its makers as well: teams whose services are highly reliable are 1.6 times less likely to suffer from burnout.

Got a story to share about your DevOps journey? Submit it to Google Cloud’s 2022 DevOps Awards by January 31, 2023!

The digital era turned the traditional formula for media and entertainment success on its head, ushering in new technologies that have changed how content is produced, distributed, experienced, and monetized. Audiences have more choice, flexibility, and power over what they consume, and today’s media companies have to embrace ongoing transformation or risk falling behind – or becoming irrelevant. 

A new wave of transformation is arriving with generative AI, a type of artificial intelligence that can interact with users in natural language and create novel data, ranging from story outlines, reports, and other text outputs to multimodal content like images, videos, and audio. Media and entertainment are inherently about content creation and creativity—so what does this new technology mean for the industry? 

At Google Cloud, we see tremendous opportunity for creative industries, from more efficient creation methods to improved user experiences. Let’s explore.

AI for media with Google Cloud

Google Cloud has a long history with large language models (LLMs) and other generative AI technologies—from their influence over the years on products like Document AI, to recent announcements like Generative AI support in Vertex AI, which lets businesses access and tune generative AI foundation models, and Generative AI App Builder, which lets developers build chatbots and other generative apps in minutes.  

We’ve helped our global media and entertainment customers with AI for personalization, search and recommendations, predictive analytics, and much more — and with generative AI now on the rise, we have some ideas to help media leaders, technologists, and creators think about and prepare to utilize powerful AI in their work. 

Three lenses on innovation in media

The media and entertainment industry is increasingly diverse and complex, with companies spanning over-the-top (OTT) subscription streaming services, 24-hour linear channels, live broadcasts of sporting events, digital journalism, traditional publishing, short-form user-generated social video, and more. More and more, the boundaries between these segments of the media industry are blurring — but common to them all is the focus on providing compelling content in an engaging audience experience that can be directly or indirectly monetized.

With this in mind, we suggest media and entertainment companies look at the application of innovative technologies like generative AI through the following three lenses:

1. Improving content creation, production, and management
2. Enhancing and personalizing audience experiences
3. Improving monetization

Improving content creation, production, and management

Generative AI democratizes many aspects of content creation, opening new ways to create written material, illustrations, sound effects, special effects, and more. Its recent maturation has been so rapid, some in the media industry have expressed concern that generative AI implies the end of creative professions. We think the opposite is more likely: just as photography, audio recordings, and computer generated images have enabled new modes of creativity, rather than making old ones obsolete, generative AI has the potential to both enable new forms of expression and enhance familiar ones. 

For example, journalists could use generative AI to speed up research by helping them synthesize and analyze large volumes of information, or to help them create initial drafts or summaries of editorial content. Film and television producers could leverage the technology to accelerate the post-production editing process, with new AI-enabled interfaces for rapidly adjusting or enhancing scene details such as lighting and color. Broadcasters could use generative AI to make vast libraries of video footage searchable and accessible for use in telling more compelling stories. The potential use cases go on and on.

Far from undermining incredible creative professions, generative AI is poised to free writers, artists, editors, and many others from the tedious and mundane aspects of their work, empowering them to focus more of their time on creativity.

Enhancing and personalizing audience experiences

Every media organization in the world today faces the reality that for most consumers, switching costs are extremely low. This puts incredible pressure on these companies to invest in delivering low-friction and compelling audience experiences that help mitigate subscribers from churning and viewers from abandoning content experiences for competitive platforms. 

Generative AI can help media companies engage and retain viewers, such as by enabling more powerful search and recommendations on their digital content platforms. With its increasingly multimodal capabilities extending from natural language to both audio and video content, generative AI is well-positioned to power more personalized audience experiences. 

Consumers often complain about “the paradox of choice” or their inability to find something interesting to watch on streaming platforms that have incredibly vast libraries of content available on demand. Imagine a not-too-distant future wherein a consumer can simply ask the content platform they’re using to help them find a specific show to watch based on mood, specific types of scenes, combinations of actors, award nominations, or practically anything they can think to ask. And that’s just the tip of the iceberg — imagine generative AI’s potential to curate, assemble, and even create personalized content for a viewer to consume!

Improving monetization

As consumers’ content consumption further expands from traditional theatrical and linear television programming to include digital offerings across an array of platforms, devices, and content types, media companies face the challenge of maintaining and improving monetization. The conventional economics and approaches to advertising and subscription models are proving, in many cases, not to deliver sufficient ROI. 

Generative AI has the potential to help media companies improve their monetization of audience experiences. As mentioned previously, enhanced personalization can play a role in mitigating churn, which in turn can help sustain and grow subscription and advertising revenues. Going beyond this, generative AI can be leveraged to drive even greater advertising revenues via more targeted, contextual, and personalized advertisements. Imagine both display and video advertisements that are generated on the fly to personalize product specifics, messaging, style, colors, and innumerable other characteristics to drive greater engagement and higher click-through rates (CTR), and thus higher advertising CPMs (cost per thousand impressions).

Coming up next

Generative AI presents a significant opportunity for media companies to fundamentally transform content creation, engagement, and monetization. Compelling services are already on the market — but there is far more to come. 

Google Cloud continues to build on its deep experience and expertise with AI, and we are committed to working with the industry to develop compelling, accessible, trusted, and responsible AI solutions that will drive meaningful business outcomes. We are excited to create the future together with our global media customers and partners across the ecosystem. To learn more about this disruptive topic, read “Debunking five generative AI misconceptions” from Google Cloud vice president of AI & Business Solutions Phil Moyer, or explore our Trusted Tester Program for generative AI.