There are a number of terms and concepts in cloud computing, and not everyone is familiar with all of them. To help, we’ve put together a list of common questions, and the meanings of a few of those acronyms. You can find all these, and many more, in our learning resources.

What are containers?

Containers are packages of software that contain all of the necessary elements to run in any environment. In this way, containers virtualize the operating system and run anywhere, from a private data center to the public cloud or even on a developer’s personal laptop. Containerization allows development teams to move fast, deploy software efficiently, and operate at an unprecedented scale. Read more.

Containers vs. VMs: What’s the difference?

You might already be familiar with VMs: a guest operating system such as Linux or Windows runs on top of a host operating system with access to the underlying hardware. Containers are often compared to virtual machines (VMs). Like virtual machines, containers allow you to package your application together with libraries and other dependencies, providing isolated environments for running your software services. However, the similarities end here as containers offer a far more lightweight unit for developers and IT Ops teams to work with, carrying a myriad of benefits. Containers are much more lightweight than VMs, virtualize at the OS level while VMs virtualize at the hardware level, and share the OS kernel and use a fraction of the memory VMs require. Read more.

What is Kubernetes?

With the widespread adoption of containers among organizations, Kubernetes, the container-centric management software, has become the de facto standard to deploy and operate containerized applications. Google Cloud is the birthplace of Kubernetes—originally developed at Google and released as open source in 2014. Kubernetes builds on 15 years of running Google’s containerized workloads and the valuable contributions from the open source community. Inspired by Google’s internal cluster management system, Borg, Kubernetes makes everything associated with deploying and managing your application easier. Providing automated container orchestration, Kubernetes improves your reliability and reduces the time and resources attributed to daily operations. Read more.

What is microservices architecture?

Microservices architecture (often shortened to microservices) refers to an architectural style for developing applications. Microservices allow a large application to be separated into smaller independent parts, with each part having its own realm of responsibility. To serve a single user request, a microservices-based application can call on many internal microservices to compose its response. Containers are a well-suited microservices architecture example, since they let you focus on developing the services without worrying about the dependencies. Modern cloud-native applications are usually built as microservices using containers. Read more.

What is ETL?

ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake. ETL can be used to store legacy data, or—as is more typical today—aggregate data to analyze and drive business decisions. Organizations have been using ETL for decades. But what’s new is that both the sources of data, as well as the target databases, are now moving to the cloud. Additionally, we’re seeing the emergence of streaming ETL pipelines, which are now unified alongside batch pipelines—that is, pipelines handling continuous streams of data in real time versus data handled in aggregate batches. Some enterprises run continuous streaming processes with batch backfill or reprocessing pipelines woven into the mix. Read more.

What is a data lake?

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Read more.

What is a data warehouse?

Data-driven companies require robust solutions for managing and analyzing large quantities of data across their organizations. These systems must be scalable, reliable, and secure enough for regulated industries, as well as flexible enough to support a wide variety of data types and use cases. The requirements go way beyond the capabilities of any traditional database. That’s where the data warehouse comes in. A data warehouse is an enterprise system used for the analysis and reporting of structured and semi-structured data from multiple sources, such as point-of-sale transactions, marketing automation, customer relationship management, and more. A data warehouse is suited for ad hoc analysis as well custom reporting and can store both current and historical data in one place. It is designed to give a long-range view of data over time, making it a primary component of business intelligence. Read more.

What is streaming analytics?

Streaming analytics is the processing and analyzing of data records continuously rather than in batches. Generally, streaming analytics is useful for the types of data sources that send data in small sizes (often in kilobytes) in a continuous flow as the data is generated. Read more.

What is machine learning (ML)?

Today’s enterprises are bombarded with data. To drive better business decisions, they have to make sense of it. But the sheer volume coupled with complexity makes data difficult to analyze using traditional tools. Building, testing, iterating, and deploying analytical models for identifying patterns and insights in data eats up employees’ time. Then after being deployed, such models also have to be monitored and continually adjusted as the market situation or the data itself changes. Machine learning is the solution. Machine learning allows businesses to enable the data to teach the system how to solve the problem at hand with machine learning algorithms—and how to get better over time. Read more.

What is natural language processing (NLP)?

Natural language processing (NLP) uses machine learning to reveal the structure and meaning of text. With natural language processing applications, organizations can analyze text and extract information about people, places, and events to better understand social media sentiment and customer conversations. Read more.

Learn more

This is just a sampling of frequently asked questions about cloud computing. To learn more, visit our resources page at cloud.google.com/learn.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

The music industry is rapidly changing. Only 20 years ago, the availability of music and the infrastructure that was required to make an album a sales success were incredibly complex and expensive. With the decline of physical sales and a fundamental shift to digital, music streaming now accounts for more than half of all sales globally.

At the same time, technology has democratized music-making; in many ways, it has made the musical landscape more diverse. Artists can upload their music with the click of a button. But while it’s easier for creators to share their songs with audiences, getting paid has become more fragmented.

Although music is booming, people no longer buy it outright. Instead, listeners download music digitally or subscribe to streaming services to have their libraries with them at all times. To monetize digital content effectively, artists need to know when, where, and how often their songs are played on each service. To help them navigate this complicated royalties landscape and maximize their profits, Berlin-based international music company BMG provides customized, transparent, and fair services to songwriters and artists.

With publishing and recording divisions under one roof, the subsidiary of international media giant Bertelsmann works with both emerging artists and established stars, including John Legend, Kylie Minogue, Mick Jagger, and Keith Richards. With the MyBMG web and mobile application, clients can view and analyze their royalty details in real time and collect payment. When a new record is released, BMG uses data to maximize its impact and revenue for its creators.

“We make sure that everyone who uses our clients’ music knows who needs to be paid the associated royalties, then we collect these royalties and share them out quickly and transparently,” explains Sebastian Hentzschel, Chief Information Officer at BMG. “When our artists release new music, we make sure that it’s marketed and promoted effectively around the world.”

“We needed a scalable solution for our royalty workloads that was intuitive for our developers. We also wanted a partner, not a client-vendor relationship. With autoscaling via BigQuery, excellent customer support, and a clean and simple user interface, Google Cloud ticks every box for us.”

—Gaurav Mittal, Vice President Group Technology, BMG

Getting up to speed with a new way of paying artists

In this digital world, artists aren’t just paid every time a fan buys an album—they’re paid a small amount, or royalty, for each song downloaded or streamed by a listener. So, when the industry shifted to digital, the volume of data that BMG needed to handle grew exponentially. “One CD sale is equivalent to about 1,500 streamed songs or plays,” says Gaurav Mittal, Vice President Group Technology at BMG. “That means IT departments have to process 1,500 times the amount of data to calculate payments for artists, and this makes scalable micropayment processing very important.”

Until 2019, BMG’s infrastructure was entirely hosted on-premises. Hardware limitations made it challenging to scale on-demand, making it harder to handle the data peaks that royalty processing can bring. “With our on-premises infrastructure, we were going to hit a ceiling in a few years,” says Gaurav. “We still managed to process royalty payments for our clients, but it was increasingly time consuming and expensive. To keep focusing on our clients, rather than our infrastructure, we decided to migrate to Google Cloud.”

From the outset, Gaurav and his team had a clear vision for the partnership: “Most importantly, we needed a scalable solution for our royalty workloads that was intuitive for our developers. We also wanted a partner, not a client-vendor relationship,” he says. “With autoscaling via BigQuery, excellent customer support, and a clean and simple user interface, Google Cloud ticks every box for us.”

Keeping artists happy with business-as-usual payouts during migration

To move applications to the cloud while keeping payment cycles on track for its artists, BMG teamed up with Google Cloud partner Rackspace Technology. “We selected Rackspace Technology because it combines strong technical muscle and a global footprint, with the customer service of a local boutique firm,“ shares Gaurav.

BMG’s own technology team put together the outline for the Google Cloud architecture, which they passed on to Rackspace Technology for optimizations and the ultimate stamp of approval. Whenever Gaurav and his developers needed support, Rackspace Technology was ready to go. “So far, we’ve migrated 17 applications successfully, and Rackspace Technology has been 100% spot-on with each suggestion,” says Gaurav. “I can’t recall a single flaw in a Rackspace Technology-approved architecture, and that really says something.”

When BMG began its migration in August 2019, the team developed an ambitious two-year plan. Only 14 months later, however, the project is more than 75% complete. Among the solutions that BMG is using today are Cloud Storage to securely store 130 TB of data, and Cloud SQL as its standard database technology. The web applications run on Compute Engine, App Engine, and Google Kubernetes Engine.

“After successfully moving a few applications, it was clear that with the strong teamwork of BMG and Rackspace Technology, together with the ease of use of Google Cloud, we could speed up the project without sacrificing quality,” says Gaurav. “We’re set to complete our migration six months before schedule, helping us to quickly move out of our hybrid environment.”

“Our income-tracking teams are very savvy on the data, and the simplicity of Google Cloud empowers them to self-serve analytics, rather than wait for IT. Our teams are much more productive.”

—Gaurav Mittal, Vice President Group Technology at BMG

Royalty reporting and processing with BigQuery and Dataproc

So far, all of BMG’s critical workloads are up and running on Google Cloud. Royalty calculations, for example, which require incredible processing power and the collection of many micropayments to ensure full and timely payout, run entirely on Dataproc with output stored on BigQuery for downstream integration and reporting.

Enabling more harmonious workflows through self-serve analytics

As the new beating heart of BMG’s royalty reporting, BigQuery changed the rhythm of collaboration company-wide. In the past, income tracking teams had to contact IT departments if they needed deeper data insights for their work. By integrating Data Catalog with BigQuery, BMG has made the data more accessible to all teams. This helps them detect missing income and new revenue streams independently, maximizing profits for artists.

“Our income-tracking teams are very savvy on the data, and the simplicity of Google Cloud empowers them to self-serve analytics, rather than wait for IT,” says Gaurav. “Our teams are much more productive.”

“Google Cloud enables us to be more client focused and deliver better features faster. We believe it’s just the beginning. We offer rights and royalty services for music publishing, recorded music, neighboring rights, and books. Without scalability limitations, it’s absolutely conceivable to offer our platform as a service to other companies or industries, such as gaming. Google Cloud has opened a world of possibilities.”

—Sebastian Hentzschel, Chief Information Officer, BMG

With a leaner IT environment, BMG can focus its effort on the needs of its clients. Beyond improvements in royalty processing, it can concentrate on app development, releasing new features and enhancements more frequently. By hosting applications on Google Kubernetes Engine, App Engine, and Compute Engine, BMG has built a CI/CD pipeline with automated deployments and testing to significantly speed up workflows.

“In our old system, it could take several weeks to set up an environment,” says Gaurav. “With Google Kubernetes Engine, any of our developers can complete the process in a few clicks. Having that autonomy makes our developers more motivated and self-driven.”

“Google Cloud enables us to be more client focused and deliver better features faster,” adds Sebastian. “We believe it’s just the beginning. We offer rights and royalty services for music publishing, recorded music, neighboring rights, and books. Without scalability limitations, it’s absolutely conceivable to offer our platform as a service to other companies or industries, such as gaming. Google Cloud has opened a world of possibilities.”

With the migration almost complete, BMG is looking forward to its next technology project. It plans to leverage AutoML to further scale and automate royalty tracking with machine learning. On the marketing side, advanced analytics will help BMG determine the effectiveness of promotional campaigns around the world, further increasing profits for artists. By connecting Google Data Studio to BigQuery, BMG will increase the quality of its analyses, helping musicians better understand the reach of their music around the world.

In the end, Gaurav shares, helping musicians is what it all comes down to. “We’re a new kind of music company because we build our services around our artist, songwriter, and publisher clients, not the other way around,” he says. “Google Cloud is helping us maintain strong relationships with our clients, and that’s music to our ears.”

This past year brought both new challenges and successes for our customers around the world. We thought life was going to return to normal… and then it didn’t. Despite the uncertainty, it was exciting to see customers make huge transformations in when, where, and how they run their businesses, with cloud infrastructure as a major enabler. 

According to IDC, “by 2024, 50% of organizations will use applications built on abstraction provided by managed services including cloud-native technologies to enable consistency in running in any and many locations.”¹ Over the past year we were honored to work with so many of you to start to make this prediction a reality.

Let’s take a deeper look at where our infrastructure progressed in 2021, and where we’re headed in the new year.

Google Cloud is recognized by independent experts for its leadership

For the fourth consecutive year, Google Cloud was named a Leader in the Gartner Magic Quadrant for Cloud Infrastructure and Platform Services.

The Forrester Wave for AI infrastructure placed Google at the topmost position, a testament to Google’s investments in tomorrow’s AI-first world. 

Google Cloud also won the HPC Wire Editor’s Choice award for the best use of HPC in the cloud.    

We made it easier to migrate enterprise applications

To support enterprises in their transformation journeys we focused on expanding our support for enterprise applications in our cloud. The Home Depot just wrote about its successful migration of SAP to Google Cloud, highlighting how crucial it is to have good processes and strong partnerships. For other companies using SAP applications we also launched Filestore Enterprise, which delivers 99.99% regional availability ​​backed by an SLA. 

Our partner NetApp deepened its integration with Google Cloud to enable easier and faster Windows-based application migration, and provided flexible deployment options to modernize workloads. We also added new Network Connectivity Center partners to include companies like Cisco, Palo Alto Networks, and VMware, so as you migrate over time you can easily connect all your networking resources together in one place. 

For those of you considering migration to cloud, Forrester dove deeper into the economics of migrating enterprise applications to cloud. (Spoiler alert: companies are finding major cost savings and performance benefits!) It’s so important to us that companies trust that their biggest, most complex applications will run smoothly and securely with us, and we’re pleased at the progress we’ve made toward supporting more enterprise applications this year.

We also made it easier for you to migrate VMware workloads to the cloud by expanding Google Cloud VMware Engine to 12 regions worldwide and enabling several new capabilities across networking, compliance, and scale. Customers such as Mitel and Carrefour migrated their VMware estates to Google Cloud to transform their applications with Google services to increase agility, save money (up to 45%) and reduce energy costs (about 35%) compared to running on-premises. 

For High Performance Computing (HPC), we continued to add capabilities such as optimized VM images for HPC, enhanced integration with schedulers such as SchedMD, Slurm, and Altair PBSPro.    

New cost and performance options

To further support customers doing high performance computing for things like real-time advertising, dynamic e-commerce, and gaming, we launched a new VM family: Tau VMs. Tau VMs offer 56% higher absolute performance and 42% higher price-performance compared to general-purpose VMs from any of the leading public cloud vendors.  We are seeing companies such as Nylas switching from other cloud platforms to Google Cloud to make the most of Tau VMs.

That isn’t the only VM advancement we made. For customers with higher levels of fault tolerance and looking for greater cost efficiencies, we launched Spot VMs, which open up access to Google Cloud’s idle capacity so you can run your application at the lowest price possible. With Spot VMs, you save anywhere from 60-91% off the price of on-demand VMs. 

We also launched more options for the highest performance ephemeral block storage — 6TB and 9TB Local SSDs, which offer greater IOPS per dollar when attached to general purpose N2 Compute Engine VMs. 

Distributed cloud brings us closer to you

For years, the industry has been telling companies, “move to cloud!” but not all workloads can move to the public cloud immediately or entirely. Factors include industry- or region-specific compliance and data sovereignty needs, low latency or local data-processing requirements, or the need to run applications close to other services. 

In October, we expanded our distributed cloud strategy and announced Google Distributed Cloud, a portfolio of fully managed hardware and software solutions that extend Google Cloud’s infrastructure and services to data centers and the edge. This is a major step forward in our ability to meet you where you are, in private data centers and at the edge, and we’re excited to see how this helps you as we move into 2022.

Data protection and security that never sleeps

This year we worked hard to help improve your data protection capabilities. We doubled down on our Actifio acquisition by launching several releases that deepened its integration with Google Cloud. For customers with containerized applications, we took a big step forward with the launch of Backup for GKE. We’re particularly excited about how this new option for GKE users allows you to more easily meet your service-level objectives, automate common backup and recovery tasks, and show reporting for compliance and audit purposes. 

We also enhanced Cloud Storage with data protection features like custom region selection for dual-region buckets. Previously, Google Cloud assigned dual-region pairs for you to choose from. With this release, you can  select your own region pairs that meet your regulatory or compliance requirements, or optimize your app performance. We also launched Turbo Replication for dual-region buckets, which replicates 100% of your data between regions in 15 minutes or less, backed by a Service Level Agreement, a first from a leading cloud provider. 

Security is a key area of focus for us and we partnered with Palo Alto Networks to deliver their industry-leading Intrusion Detection System (IDS) solution natively on Google Cloud. With the recent Apache Log4j vulnerability, we automatically updated Cloud IDS to help detect exploit attempts and protect our customers’ environments quickly. We also integrated Cloud Armor with reCAPTCHA Enterprise to deliver a best-in-class bot and fraud management solution to prevent volumetric attacks. Cloud Armor is deployed with our Cloud Load Balancer and Cloud CDN, extending its security benefits at the network edge for traffic coming into Google Cloud. 

We made it easier to build and manage applications

Developers want to focus on code, not configuring, managing or scaling infrastructure. To help, we worked on simplifying networking with new services such as Private Service Connect, which allows you to connect VPCs to applications and services securely without configuring all the network underlay. To make it easier to run workloads in hybrid environments, we introduced BYOIP so you don’t need to change your IP address, and we enhanced Cloud Load Balancer with advanced traffic management, regional and hybrid app delivery to load balance traffic between on-prem and cloud workloads. We also introduced IPv6, DNS policy manager, Cloud Domains, GKE Gateway Controller, eBPF data plane, and Service Directory, all with the goal of making networking easier for developers.

We continue to invest in visibility and observability for efficient day 2 operations. Network Intelligence Center now has dynamic reachability within the Connectivity Tests module, interconnect and VPN visualization in Network Topology, and the Firewall Insights module, which provides visibility into firewall rules to ensure they are being used appropriately and as intended. 

And while not new this year, developers continue to tell us how much they love the ease of architecting storage using Cloud Storage. See how easy it is to configure a dual-region bucket. As a result, developers can treat a continent like a single bucket, dramatically simplifying the application programming model. Google Cloud is unique in offering this capability among major public cloud vendors.

We continue to be the most sustainable cloud for you

Another area Google has invested in deeply and that is becoming increasingly important to more organizations is sustainability. Many cloud providers have a vision for a sustainable future, and many aim to match their electricity consumption with 100% renewable energy by 2025 or 2030. We accomplished 100% renewable energy in 2017, which means we’re the only hyperscale cloud to do this today and on data centers that are twice as energy efficient as the average data center.

We also announced a number of new features like our new Carbon Footprint tool. Every Google Cloud user — that means you! — can see the gross carbon emissions associated with the services you use in Google Cloud. This means picking the data center that meets your sustainability goals, and reducing the overall emissions tied to your infrastructure and applications. And we have some pretty big goals for the future. For more, don’t miss our full sustainability recap.

A big thank you to our customers

While we were busy building out our infrastructure in 2021, we’d be nowhere without the partnership of our amazing customers. It’s hard to pick from all the exciting stories we heard this year, but here are just a few: Pega migrated its SAP applications to Google Cloud with zero downtime. Paypal exited its non-strategic data centers and achieved major cost savings. And Wix used our network to serve tens of millions of requests per day. 

I’m proud to look back at how our cloud infrastructure has evolved this year to support each of our customers on their transformation journey. As we head into 2022 I’m excited to continue to partner with you to solve your most difficult challenges together. 

Interested in hearing more about Google’s vision for the future? Listen to Thomas Kurian’s thoughts from Google Cloud Next ‘21.

^{1. IDC FutureScape: Worldwide Cloud 2022 Predictions}