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.

Financial markets were among the first to adopt new technologies, and that has certainly been true of the derivatives markets, which were early adopters of electronic trading. Going forward, new capabilities will transform the way industry participants communicate, analyze, and trade.

I sat down with Google Cloud’s Phil Moyer and former SEC Commissioner, Troy Paredes, for a fireside chat at FIA Boca 2022 to discuss the future of markets and policy, the new technologies that are already paving the way for greater speed and transparency, and how cloud can help promote greater resiliency, performance, and security to enable the long-term vision for the market. The following is a summary of our discussion.

The current state of cloud technology

When it comes to technology adoption, we’re seeing the market and participants adopt cloud technologies, and increasingly, machine learning (ML) on a wider scale. Cloud technology allows for easier, faster, and much more secure experimentation with large datasets and ML.

A recent Google sponsored study by Coalition Greenwich (September, 2021) showed that more than 93% of trading systems, exchanges, and data providers are in some way providing services on the cloud. The same study, revealed that about 72% of the financial industry across the buy side and sell side, intend to consume public cloud-data based market data within the next 12 months.

Data-driven decision-making and risk management have always been, and continue to remain, the cornerstones of the financial markets. Over time, technology innovation has facilitated access to better insights from data, and therefore, better decision-making and the ability to manage risk. That expectation is now mainstream, and will continue to grow in sophistication.

The multi-phased technology trajectory

The movement of exchanges to the cloud will occur in a “crawl-walk-run” fashion, with low-hanging fruits the first to be picked in the near term while bigger, paradigmatic changes will occur over the medium and long term. Some organizations are starting all three stages simultaneously, understanding that each will move at an independent cadence.

The “crawl” phase is one in which foundations are built, starting with organizations moving data to the cloud and experimenting with some degree of analytics. It’s one of the most important phases because it’s where the opportunity to increase transparency and risk management takes shape.

In moving to the cloud, the infrastructure – which in the past relied on a combination of people, processes, and some technology – becomes the code that runs applications. This early phase is key to empowering organizations to shift to a cloud-based, agile-first operating model that makes it easier and more seamless to launch new products in the future, including by freeing up people and resources from IT management to more mission-focused work.

Establishing the cloud operating model simplifies the “walk” and “run” phases where compliance is more automated, latency-sensitive applications are more readily available, and the next generation of exchanges, market participants, and regulators is better prepared to meet future challenges.

The “walk” phase is where much of the innovation happens. Exchanges are making significant progress in leveraging foundational data decisions in the “crawl” phase and innovations in the cloud to improve settlement, clearing, risk management, collateral management, and compliance, and launch new products.

And finally, the “run” phase is where organizations will start to move the latency-sensitive markets to the cloud, as the markets increasingly will demand low-latency and high performance along with transparency and analytics to solve historical obstacles to market access.

Opportunities for both regulators and market participants

Any time significant technological change takes place, regulators explore its implications, particularly with respect to their ability to meet their regulatory objectives.

Increasingly, we are seeing technological change driving more opportunities for regulators and market participants alike. Such changes may also allow better protection of the marketplace, with greater integrity and transparency.

Over time, regulatory regimes – rules, regulations, statutes, interpretations, and guidance – will also adjust to new technologies, both benefiting the marketplace and advancing regulatory goals.

As one example, the cloud is increasing the ability to meet compliance obligations by allowing compliance to be built into transactions. Moreover, predicated on the vision of real-time regulatory reporting, and given the pace of technological change in the marketplace over the last several years, various regulators have been using more advanced analytics. This trend will continue to help them more effectively and efficiently meet their objectives, and monitor and meet the expectations they have for the entire market.

Machine learning’s role in the financial markets

Google Cloud’s head of AI and Industry Solutions, Andrew Moore, said that ML will be doing three key things for us in the next 10 years: giving us meaning, providing concierge services, and serving as a guardian. Extracting information that is critical to investor decision-making can be extremely important. With more data than ever, ML can increase the ability to process it while also becoming more accessible in the cloud and better supporting regulatory objectives.

The technology will likely manifest in trading and anti-money laundering activities as they relate market functions, as well as managing a wide variety of risks – supporting the interests of both investors and regulators in terms of decision-making, surveillance, and protections.

Rather than taking individuals out of the equation, the digitization of markets, assets, and guard rails combined with ML will allow people to focus their expertise in different ways to achieve key objectives.

Building the market foundation for the future

The goals of operational resiliency, security, and privacy will continue to be critical for building the market foundation for both participants and regulators. While technology promises to create advantages in concrete, tangible ways, it will be important to scrutinize potential risks and concerns.

Priority one for technology providers is to build an environment of trustless security, including encryption at motion and encryption at rest, ensuring that markets are operationally resilient while instilling confidence for any exchange that runs on top of that infrastructure. Multicloud architectures and approaches are likely also to be part of the solution for operational resilience.

Throughout time, liquidity has been the outcome of improved access, transparency, and security. Technology providers are responding by sharing both the responsibility for, and fate of, the markets of the future to build an efficient, faster, and more transparent and secure financial industry.

You can learn more about our approach in our newest white paper, Building the financial markets foundation for the future.

Organizations are pressed with the need to engage, retain, and upskill employees with many positions staying fully or partially remote as the pandemic continues. The Center for Digital Government (CDG) reports that 74% of state and local governments believe they will continue hybrid operations for the long-term. This transition is the latest in a long list of reasons agencies are looking for solutions to help train their workforce digitally. Some have found Google Classroom to be the perfect solution.

The State of Iowa knew about the advantages of digital classrooms even before the pandemic. They were already successfully using Google Classroom for in-person training, making the transition to digital seamless when the state closed down. Jessica Van Heuveln, a Google support specialist for the State of Iowa CIO’s Office, says the switch to digital doesn’t deprive workers of hands-on training. “You can use Google Classroom for many different environments, so if you’re doing self-taught, in-person or virtual training, it’s going to work with all those approaches.”

The logistics of a digital classroom

Iowa uses Google Classroom to onboard and upskill employees as well as train volunteers. When the pandemic shifted the state’s workforce to 70% working from home, they needed a  platform that could not only handle a vast range of topics and teaching styles to support remote workers but also scale to meet their demand. Van Heuveln notes that Google Classroom was particularly helpful for handling compliance training, especially with its integration with Google Meet. These trainings often require an entire sector of the workforce to attend a single class with more than 100 attendees, according to Van Heuveln. The logistics of these larger sessions cuts down on the number of total sessions the state needs to host. Iowa also found that employees were less apprehensive about learning when they could do it from the comfort of their own homes.

Iowa follows a train-the-trainer model to train employees expected to instruct using Google Classroom. Trainers working in departments across the state learn to use Google Classroom to create and host virtual learning experiences specific to their own departments. 

Collaborative classrooms made simple

Google Classroom meets many needs for Iowa, such as setting up training, managing attendance, reviewing uploaded coursework, and communicating with attendees. Both course attendees and trainers have everything they need in one place, and robust engagement tools help attendees stay focused while giving trainers the feedback they need from their lessons. Built-in survey options let trainers gauge attendee knowledge, and the chat function ensures no one ever misses a question. The reporting dashboard gives trainers access to analytics to track engagement and attendance, providing insights that can improve future training sessions.

Google Classroom also makes it easier to ensure attendees stay on board throughout the training program. Coursework can be uploaded directly into Google Classroom, and training sessions can be recorded and archived. This feature allows employees to work at their own pace or lets them catch up when a scheduling conflict means they might miss a session.

The enterprise version of Google Classroom Iowa uses interfaces with other Google Workspace tools such as Google Meet, Google Calendar, Google Drive, and Gmail, which streamlines training and leads to more collaboration between employees and trainers. It can also interface with applications from other providers, further boosting its utility as an effective virtual classroom. Google Drive stores training materials securely. Lastly, Google Classroom enterprise comes with 24/7 support to make sure agencies always have everything they need for success.

Helping employees be better prepared to serve

Google Classroom can be a powerful part of any training program, whether it be digital or in-person. Building virtual connections empowers organizations to deliver better experiences. Google Classroom is helping the State of Iowa accomplish this for their constituents. Designed to scale and be accessible from anywhere, the platform helps public employees better serve the public. Virtual training strategies are essential for agencies that are looking to grow their workforce and build employee skill levels. The most important thing, however, is that training programs enable employees to further your agency’s mission. To learn more about Google Classroom and see more solutions designed with government in mind, check out the Google Cloud for government page.

Engineers that build last-mile delivery services belong to an elite order, a hallowed subcategory. Delivery customers are incredibly demanding when it comes to speed and convenience, and the services they use must take variables like increased traffic, road conditions, human error, and even driver availability into account every day.

Nuro is a company with a new approach to delivery services. Nuro has a fleet of autonomous vehicles designed to address many of the problems related to last-mile delivery. And every day, these vehicles — and their sensors — generate a lot of data before parking for the night. For Nuro engineers, that data can help them understand the impact of new on-road features, make improvements to their vehicles’ software, and ensure even better deliveries for their customers.

For Nuro, the key challenge is how to move petabytes of data as quickly, securely, and easily as possible from their edge environments, like vehicle depots, to Google’s Cloud Storage. For this delivery effort, Nuro selected Google’s Transfer Appliance with its new online transfer capability, now generally available.

Helping Nuro to speed up data delivery from the edge to the cloud

Like many Google Cloud customers, Nuro collects data from remote environments, like vehicle depots, that have different networking and storage capabilities when compared to a traditional data center. For a transfer solution to be effective moving unstructured data from these environments to the cloud, the solution needs to be easy to deploy and automate, while still providing similar performance as a more complicated alternative.

The Transfer Appliance was built for this use case. It arrives to customers as a physical appliance with a preconfigured version of Google’s Storage Transfer Service software already installed. Customers can move files to the appliance by using SFTP or SCP, or, alternately, can mount the appliance as an NFS share and copy target. Data can be stored locally on the appliance or transferred over the network, and secure encryption — at-rest and in-flight — is enabled by default.

With these new appliances, Nuro will be able to automate much of their storage transfer needs. When their autonomous vehicles return to the depot, they can move data like software logs, LIDAR data, and sensor data — all ideal fits for Google’s Cloud Storage — from parked vehicles to the Transfer Appliance. Online transfers can then be performed throughout the day, ensuring a steady stream of valuable data in the cloud for developers to analyze and use in their nightly builds. All of this will help Nuro’s engineering leaders like Jie Pan to run more productive development teams with less operational overhead.

“Our autonomous vehicles generate a tremendous amount of useful data, and our goal is to get that data to our engineers as soon as possible,” said Jie Pan, Engineering Manager at Nuro. “When vehicles return to the depot, we can move data hourly into Cloud Storage over the network. We also have the flexibility to return the Transfer Appliance back to Google Cloud. Most importantly, this rapid transfer architecture gives a meaningful boost to engineering productivity and development velocity.”

Going the extra mile

Engineering and infrastructure leaders understand the value of delivering the right data to the right teams, as fast as possible. By adding preconfigured, over-the-network transfer into a turnkey Transfer Appliance, Google Cloud customers can more easily automate these data deliveries by scheduling regular migrations of on-premises files, objects, and other unstructured data to our Cloud Storage.

As Nuro continues to grow their manufacturing and testing footprint, they plan to use Transfer Appliances to further scale and simplify their data migration from on-premises to Google Cloud. Cutting the time to migrate their data by more than half will make for happier, more productive developers, and that will help Nuro bring us all the future of delivery a little faster.

If you’d like to learn more about Transfer Appliance and its new online transfer capability, click here or reach out to your Google Cloud account team.