As CentOS 7 reaches end of life, many enterprises are considering their options for an enterprise-grade, downstream Linux distribution on which to run their production applications. Rocky Linux has emerged as a strong alternative that, like CentOS, is 100% compatible with Red Hat Enterprise Linux.

In April 2022, we announced a customer support partnership with CIQ, the official support and services partner and sponsor of Rocky Linux, as the first step in providing a best-in-class enterprise-grade supported experience for Rocky Linux on Google Cloud. Today we’re excited to announce the general availability of Rocky Linux Optimized for Google Cloud. We developed this collection of Compute Engine virtual machine images in close collaboration with CIQ so that you get optimal performance when using Rocky Linux on Compute Engine to run your CentOS workloads.

These new images contain customized variants of the Rocky Linux kernel and modules that optimize networking performance on Compute Engine infrastructure, while retaining bug-for-bug compatibility with Community Rocky Linux and Red Hat Enterprise Linux. The high bandwidth networking enabled by these customizations will be beneficial to virtually any workload, and are especially valuable for clustered workloads such as HPC (see this page for more details on configuring a VM with high bandwidth).

Going forward, we’ll collaborate with CIQ to publish both the community and Optimized for Google Cloud editions of Rocky Linux for every major release, and both sets of images will receive the latest kernel and security updates provided by CIQ and the Rocky Linux community. And of course, we’ll offer support with CIQ for both these images, per our partnership.

Rocky Linux Optimized for Google Cloud lets you take advantage of everything Compute Engine has to offer, including day-one support for our latest VM families, GPUs, and high-bandwidth networking. And for customers building for a multi-cloud deployment environment, the community Rocky images have you covered.

Starting today, Rocky Linux 8 Optimized for Google Cloud is available for all x86-based Compute Engine VM families (and soon for the new Arm-based Tau T2A), with version 9 soon to follow. Give it a try and let us know what you think.

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.

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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!

In our conversations with technology leaders about data-driven transformation using Google Data Cloud –  industry’s leading unified data and AI solution – , one important topic is incorporating continuous intelligence to move from answering questions such as “What has happened? to questions like “What is happening?” and “What might happen?”. The core to this evolution is the need for an underlying data processing that not only provides powerful real-time capabilities for events happening close to origination, but also brings together existing data sources under one unified data platform to enable organizations to draw insights and take actions holistically. Dataflow, Google’s cloud-native data processing and streaming analytics platform, is a key component of any modern data and AI architecture and data transformation journey, along with BigQuery, Google’s internet-scale warehouse with built-in streaming, BI engine and ML; Pub/Sub, a global no-ops event delivery service; and Looker, a modern BI and embedded analytics platform. One of the key evaluation factors is potential economic value of Dataflow to their organization, particularly in the context of engaging other stakeholders is key for many of the leaders that we engage with. So we commissioned Forrester Consulting to conduct a comprehensive study on the impact that Dataflow had on their organization by interviewing actual customers . 

Today we’re excited to share our commissioned study conducted by Forrester Consulting, the Total Economic Impact™ of Google Cloud Dataflow, which allows data leaders to understand and quantify the benefits of Dataflow, and use cases it enables. Forrester conducted interviews with Dataflow customers to evaluate the benefits, costs, and risks of investing in Dataflow across an organization. Based on their interviews, Forrester identified major financial benefits across four different areas: business growth, infrastructure cost savings, data engineer productivity, and administration efficiency. In fact, Forrester found that customers adopting Dataflow can achieve a 55% boost in developer productivity and a 50% reduction in infrastructure costs. In fact, Forrester projects that customers adopting Dataflow can achieve a range of up to 171% Return on Investment (ROI) and a less than six months payback period. Customers can now use figures in the report to compute their own Return on Investment (ROI) and payback period.

“Dataflow is integral to accelerating time-to-market, decreasing time-to-production, reducing time to figure out how to use data for use cases, focusing time on value-add tasks, streamlining ingestion, and reducing total cost of ownership.” – Lead technical architect, CPG

Let’s take a deeper look at the ways that Forrester found that Dataflow can help you achieve your goals and unlock your business potential. 

Benefit #1: Increase data engineer productivity by 55%

Developers can choose among a variety of programming languages to define and execute data workflows. Dataflow also seamlessly integrates with other Google Cloud Platform and open source technologies to maximize value and applicability to a wide variety of use cases. Dataflow streamlined workflows with code reusability,dynamic templates, and the simplicity of a managed service. Engineers trusted pipelines to run correctly and adhere to governance. Data engineers avoided laborious issue-monitoring and remediation tasks that were common in the legacy environments such as poor performance, lack of availability, and failed jobs. Teams valued the language flexibility and open source base.

“Dataflow provided us with ETL replacement that opened limitless potential use cases and enabled us to do smarter data enhancement while data remains in motion.” — Director of data projects, financial services

Benefit #2: Reduce infrastructure costs by up-to 50% for batch and streaming workloads 

Dataflow’s serverless autoscaling and discrete control of job needs, scheduling, and regions eliminated overhead and optimized technology spending. Consolidating global data processing solutions to Dataflow further eliminated excess costs while ensuring performance, resilience, and governance across environments. Dataflow’s unified streaming and batch data platform gives organizations the flexibility to define either workload in the same programming model, run it on the same infrastructure, and manage it from a single operational management tool. 

“Our costs with our cloud data platform using Dataflow are just a fraction of the costs we faced before. Now we only pay for cloud infrastructure consumption because the open source base helps us avoid licensing costs. We spend about $120,000 per year with Dataflow, but we’d be spending millions with our old technologies.” – Lead technical architect, CPG

Benefit #3: Increase top-line revenue by improving customer experience and retention with payback time of < 6 months

Streaming analytics is an essential capability in today’s digital world to gain real-time actionable insights. Likewise, organizations must also have flexible, high- performance batch environments to analyze historical data for building machine learning models, business intelligence, and advanced analytics. Dataflow enabled real-time streaming use cases, improved data enrichment, encouraged data exploration,improved performance and resiliency, reduced errors, increased trust, and eliminated barriers to scale. As a result, organizations provided customers with more accurate, relevant, and in-the-moment data-backed services and insights — boosting customer experience, creating new revenue streams, and improving acquisition, retention, and enrichment.

“It’s already been proven that we are getting more business [with Dataflow] because we can turn around results faster for customers.” – VP of technology, financial services technology

“When we provide data to our customers and partners with Dataflow, we are much more confident in those numbers and can provide accurate data within a minute. Our customers and partners have taken note and commented on this. It’s reduced complaints and prevented churn.” – Senior software engineer, media

Other benefits 

Eliminated administrative overhead and toil

As a cloud-native managed service, all administration tasks such as provisioning, scaling, and updates are automatically handled by Google Cloud. Teams no longer need to manage servers and related software for legacy data processing solutions. Admins also streamlined processes for setting up data sources, adding pipelines, and enforcing governance.

Saved business operations costs for support teams and data end users

Dataflow improved the speed, quality, reliability, and ease of access to data for insights for general business users, saving time and empowering users to drive better data-backed outcomes. It also reduced support inquiry volume while automating manual job creation.

What’s next?

Download the Forrester Total Economic Impact study today to dive deep into the economic impact Dataflow can deliver your organization. We would love to partner with you to explore the potential Dataflow can unlock in your teams. Please reach out to our sales team to start a conversation about your data transformation with Google Cloud.