Apigee is committed to continually innovating new capabilities and solutions for our customers, and 2021 saw new product launches, partnerships, and best practices for managing your expanding range of business-critical use cases. Here are some of our favorite stories from 2021. 

Spotting the trends in APIs

Our State of API Economy 2021 Report surveyed over 700 IT leaders globally and identified five key API trends that emerged post-COVID. SaaS and hybrid cloud-based API deployments are increasing with half of all respondents reporting increases in these areas, and AI- and ML-powered API management is also gaining traction, with usage growing 230% year-over-year among Apigee customers. Business metrics like Net Promoter Score (NPS) and speed-to-market are API users’ preferred way to measure success, and API ecosystems are increasingly innovation drivers, with high-maturity organizations much more likely to focus on building a developer ecosystem or B2B partner ecosystem around their API. Finally, API security and governance is more important than ever, as research showed that increased investment in security and governance was a high priority. Check out the blog to explore these five trends in more detail.

Launching new capabilities with Apigee X

We announced Apigee X, our next-generation platform that brings the powerful scale of Google technologies to Apigee API Management and allows enterprises to power API programs for enhanced scale, security, and automation. Apigee X customers can harness the capabilities of Cloud CDN to maximize the availability and performance of APIs across the globe, deploying across more than two dozen Google Cloud regions and enhancing caching at over 100 locations. Apigee X customers can apply solutions like Cloud Armor web application firewall for enhanced API security and Cloud Identity and Access Management (IAM) for authenticating and authorizing access to the Apigee platform. Apigee X also enhances automation by applying Google Cloud’s AI and ML capabilities to historical API metadata to detect anomalies, predict traffic, and ensure compliance. To read more about these features, check out our blogs on Apigee X and Cloud Armor, Apigee X and Cloud CDN, and Apigee X and AI. 

Making new connections with Apigee Integration

Apigee brought our successful API-first approach to integration this year with the release of Apigee Integration. This silo-busting solution lets customers connect existing data and applications, and surface them as easily accessible APIs. Apigee Integration brings together the best of API management and integration into one unified platform so IT teams can scale their operations, improve developer productivity, and increase the speed to market. The platform comes with built-in connectors to Salesforce, Cloud SQL (MySQL, PostgreSQL), Cloud Pub/Sub and BigQuery, with connectors for additional third-party applications and databases on their way. Advanced integration patterns also serve our customers with even more use cases. Check out our launch blog and our Next session video for more details. 

Managing GraphQL APIs with Apigee

The exponential rise in digital services adoption among enterprises now generates petabytes of data every minute. You can harness the power of this data with query languages like GraphQL, accessing the data your app needs with one single request. The growing popularity of GraphQL APIs and their business-critical use cases mean it’s important to manage them with full life cycle capabilities, much like you manage your REST APIs. Last year we compared REST and GraphQL and introduced best practices for managing GraphQL APIs. You can also read our blog announcing Apigee’s support for the management of GraphQL APIs, and our partnership with StepZen to deliver these capabilities. To dive deeper into building GraphQL APIs, check out our Next session video. 

Looking back at the year’s top stories

2021 was the year of the customer, and we published the following stories to show how API management helps enterprises modernize their applications, build digital ecosystems, and generate value for their customers and their own organizations:

• Telco:
  • Leveraging APIs to create value for telco ecosystems 
  • How the MTN Group is expanding its business with APIs
• Retail:
  • Conversational AI with Apigee API Management for enhancing customer experiences
  • Seven-Eleven Japan uses Google Cloud to serve up real-time data for fast business decisions
• Financial services:
  • Arab Bank: Accelerating application innovation with Anthos and Apigee
  • Military Bank: Turns to Apigee to manage its API strategy and deliver digital transformation
• Energy: 
  • How Veolia’s API-first approach is powering sustainable resource management
  • GRTgaz: Calling on Apigee to help internal and external partners access intelligence securely

That’s a wrap for 2021! We hope you have a safe and happy holiday season, and we can’t wait to see what the new year brings for us. Stay tuned in 2022 for product launch announcements, partnerships, tips, and stories of how organizations like yours are innovating with Apigee.

Editor’s note: This post is part of an ongoing series on IT predictions from Google Cloud experts. Check out the full list of our predictions on how IT will change in the coming years.

Prediction: Over half of all organizations using public cloud will freely switch their primary cloud provider as a result of available multicloud capabilities

In the years ahead, companies will use a multicloud strategy not just as a way to hedge their bets, but as a way to switch from their first cloud to their next one. Research shows that the majority of companies are already multicloud, meaning they use more than one hyperscale cloud provider.

More and more, we’re talking to companies that describe using multicloud technologies as a way to do switch not just workloads — but mindshare — to a different cloud. In other words, for many people, multicloud is a phase, not a permanent state.

You may start using one cloud but still need to be able to incorporate existing investments you’ve already made in other clouds without having to move anything. Here at Google Cloud, we’ve made unique investments to make sure we can meet our customers wherever, and in whatever cloud, they are.

For instance, Anthos, our multicloud management plane, ensures consistency when working with your compute and data on other clouds. You can view workloads, deploy services, and apply common security policies across multiple clouds. BigQuery Omni allows you to query data in other cloud storage accounts in Amazon S3 or Azure Storage without having to move the data itself, helping to bring analytics to data wherever it resides.

Building new skills and getting comfortable in other clouds is not where multicloud stops. Many organizations are taking it a step further — upgrading technology, moving core data, and continuing to grow cloud adoption with a secondary provider.

What starts out as wanting the best capabilities to achieve IT goals will often lead organizations to swap from their first cloud to their next cloud — and by 2025, we believe that most organizations will be doing just that.

Communication services providers (CSPs) are experiencing a period of disruption. Overall revenue growth is decelerating and is projected to remain below 1 percent per year, following a trend that started even before the pandemic.¹ At the same time, driven by the pandemic, data consumption in 2020 increased by 30 percent relative to 2019, with some operators even reporting increases of 60 percent.² 

The combination of pressure on revenues with rising data traffic costs is forcing operators to innovate in three fundamental ways. First, operators are looking to establish new sources of revenue. Second, increased network utilization must be met with a reduction in network cost. And third, there is an opportunity to gain new customers by improving the customer experience.

Fortunately, 5G offers a path forward across each of these three areas. Concepts such as network slicing and private networks allow CSPs to offer differentiated network services to public sector and enterprise customers. The disaggregation of hardware and software allows new vendors with unique strengths to enter the market and to enable CSPs to build, deploy, and operate networks in fundamentally new ways. And the ability to place workloads at the edge permits CSPs to offer compelling experiences to consumers and businesses alike. In this blog, we will discuss how CSPs can create a solid foundation for their cloud networks. 

Understanding telecommunications networks

First, it is useful to consider the way telecommunications networks were traditionally built. Initially, networks were built using physical network functions (PNFs) — appliances that used a tight combination of hardware and software to perform a specific function. PNFs offered the benefit of being purpose-built for a specific application, but they were inflexible and difficult to upgrade. As an example, deploying new features frequently required replacing the entire PNF, i.e., deploying a new hardware appliance.

The first step in improving deployment agility came with the concept of virtualized network functions (VNFs), software workloads designed to operate on commercial off-the-shelf (COTS) hardware. Rather than utilizing an integrated hardware and software appliance, VNFs disaggregated the hardware from the software. As such, it became possible to procure the hardware from one vendor and the software from another. It also became possible to separate the hardware and software upgrade cycles. 

However, while VNFs offered advantages over PNFs, VNFs were still an intermediate step. First, they typically needed to be run within a virtual machine (VM), and as such required a hypervisor to interface between the host operating system (OS) and the guest OS inside the VM. The hypervisor consumed CPU cycles and added inefficiency. Second, the VNF itself was frequently designed as a monolithic function. This meant that while it was possible to upgrade the VNF separately from the hardware, such an upgrade, even for a feature that affected only a portion of the VNF, required deployment of the entire large VNF. This created risk and operational complexity, which in turn meant that upgrades were delayed just as they were with PNFs.

Creating the foundation for cloud networks

The trick to establishing your cloud based network resides in the challenge of moving from VNFs to containerized network functions (CNFs) — network functions organized as containers as a collection of small programs, each of which can be independently operated. 

The concept of containers is not new. In fact, Google has been using containerized workloads for over 15 years. Kubernetes, which Google developed and open-sourced, is the world’s most popular container orchestration system, and is based on Borg, Google’s internal container management system.³ There are lots of benefits to using containers, but fundamentally, it frees developers from worrying about resource scheduling, interprocess communication, security, self-healing, load balancing, and many other tedious (but important!) tasks. 

Consider just a couple examples of benefits that containerization brings to network functions. First, when upgrading the network function to implement new features, you no longer need to re-deploy the entire network function. Instead, you only need to re-deploy the containers that are affected by the upgrade. This improves developer velocity and reduces the risk of the upgrade because, rather than infrequent upgrades that each introduce substantial changes, you can now have frequent upgrades that each deploy small changes. Small changes are less risky because they are easier to understand and to roll back in case of anomaly. Incidentally, this also improves your security posture because it reduces the time between when a security vulnerability is discovered and when a patch is deployed.

Speaking of security, another example of the benefits that containerization brings to network functions is an automatic zero-trust security posture. In Kubernetes, the communication among microservices can be handled by a service mesh, which manages mundane aspects of inter-services communication such as retries in case of failure and providing observability into communication. It can also manage other essential aspects such as security. For example, Anthos Service Mesh, which is a fully-managed implementation of the open-source Istio service mesh (also co-developed by Google), includes the ability to authenticate and encrypt all communications using mutual TLS (mTLS) and to deploy fine-grained access control for each individual microservice.

Automation and orchestration for cloud networks

CNFs bring tremendous benefits, but they also bring challenges. In place of a relatively small number of network appliances, we now have a large number of containers, each of which requires configuration, management, and maintenance. In the past, many of these processes were accomplished using manual techniques, but this is impossible to accomplish economically and reliably at the scale required by CNFs.

Fortunately, there are cloud-native approaches to solving these challenges. First, consider the problem of autonomously deploying and maintaining CNFs. The ideal way is to use the concept of Configuration as Data. Unlike imperative techniques such as Infrastructure as Code, which provide a detailed description of a sequence of steps that need to be executed to achieve an objective, Configuration as Data is a declarative method whereby the user specifies the desired end state (i.e., the actual desired configuration) and relies on automated controllers to continuously drive the infrastructure to achieve that state. Kubernetes includes such automated controllers, and the great news is that this method can be used not just for infrastructure but also for the applications residing on top of it, including CNFs. This cloud-native technique frees you from the toil and associated risk of writing detailed configuration procedures, so you can focus on the business logic of your applications.

As another example, consider the problem of understanding your network performance, including anomaly detection, root cause analysis, and resolution. The cloud-native approach starts with creating a data platform where both infrastructure and CNF monitoring data can be ingested, regularized, processed, and stored. You can then correlate data sets against each other to detect anomalies, and with AI/ML techniques, you can even anticipate anomalies before they happen. AI/ML is likewise indispensable in gaining an understanding of why the anomaly is happening, i.e. performing root cause analysis, and automated closed-loop controllers can be developed to correct the problem, ideally before it even happens.

Architecting for the edge

The transition from VNFs to CNFs is a critical piece in addressing the challenge that CSPs face today, but it alone is not enough. CNFs need infrastructure to run on, and not all infrastructure is created equal. 

Consider a typical 5G network. There are some functions, such as those associated with an access network, that need to be deployed at the edge. These functions require low latency, high throughput, or even a combination of the two. In 5G networks, examples of such functions include the radio unit (RU), distributed unit (DU), centralized unit (CU), and the user plane function (UPF). The first three are components of the radio access network (RAN), while the last is a component of the 5G core. At the same time, there are some other control plane functions such as the session management function (SMF) or the authentication and mobility management function (AMF) that do not have such tight latency and high throughput requirements and can thus be placed in a more centralized data center. Furthermore, consider an AI/ML use case where a particular model (perhaps for radio traffic steering) needs to run at the network edge because of its latency requirements. While the model itself needs to run at the edge, model training (i.e., generating the model coefficients) is frequently a compute-intensive exercise that is latency-insensitive and is thus more optimal to run in a public cloud region.

All of these use cases have one thing in common: they call for a hybrid deployment environment. Some applications must be deployed at the edge as close to the user as possible. Others can be deployed in a more centralized environment. Still others can be deployed in a public cloud region to take advantage of the large amount of compute and economies of scale available therein. Wouldn’t it be convenient — if not transformational — if you could use a single environment for deploying at the edge, in a private datacenter, and in public cloud, with a consistent set of security, lifecycle management, policy, and orchestration resources across all such locations? This is indeed what Google Distributed Cloud, enabled by Anthos, brings to the table.

With Google Distributed Cloud, you can architect a 5G network deployment such as the one shown below.

Business benefits of cloud networks

Beyond the technical benefits, consider the business benefits of such an architecture. First, by following the best practices of hardware and software disaggregation, it permits the CSP to procure the infrastructure and the network functions from different vendors, spurring competition among vendors. Second, each workload is placed in precisely the right location, enabling efficient utilization of hardware resources and offering compelling low-latency, high-throughput services to users. Third, because the architecture utilizes a common hybrid platform (Anthos), it makes it easy to move workloads across infrastructure locations. Fourth, the separation of workloads into microservices accelerates time-to-market when developing new features or applications, such as those enabling enterprise use cases. And finally, the container management platform supports the simultaneous deployment of both network functions and edge applications on the same infrastructure, allowing the operator to deploy new experiences such as AR/VR directly on bare metal as close to the user as possible.

The next generation cloud network is now

There is a lot more we could say, but perhaps the most important takeaway is that this architecture is not a future dream. It exists today, and Google is working with leading CSPs and network vendor partners to deploy it, helping them realize the promise of 5G to deliver new revenues, reduce operating costs, and enable new customer experiences.

To learn more, watch the video series on the cloudification of CSP networks.

Discover what’s happening at the edge: How CSPs Can Innovate at the Edge.

^{1.Statista, Forecast growth worldwide telecom services spending from 2019 to 2024
2 PricewaterhouseCoopers, Global entertainment and media outlook 2021-2025
3. Borg: The Predecessor to Kubernetes}

Video AI is a powerful way to enable content discovery and engaging video experiences.

Here, try it out right now!

Google Cloud’s easy-to-access video AI solutions can accomplish a bunch of things. Here are a few:

Precise video analysis: Video Intelligence API automatically recognizes more than 20,000 objects, places, and actions in stored and streaming video. It also distinguishes scene changes and extracts rich metadata at the video, shot, or frame level. Use in combination with AutoML Video Intelligence to create your own custom entity labels to categorize content. Imagine being able to categorise hundreds of videos of customer interactions quickly to improve service training!

Recommended content: Build a content recommendation engine with labels generated by Video Intelligence API and a user’s viewing history and preferences. This will simplify content discovery for your users and guide them to the most relevant content that they want.

Simplify media management: Find value in vast archives by making media easily searchable and discoverable. Easily search your video catalog the same way you search text documents. Extract metadata that can be used to index, organize, and search your video content, as well as control and filter content for what’s most relevant.Imagine being able to locate insight in hundreds of enterprise videos to improve productivity and customer experience!

Easily create intelligent video apps: Gain insights from video in near real time using the Video Intelligence Streaming Video APIs, and trigger events based on objects detected. Build engaging customer experiences with highlight reels, recommendations, interactive videos, and more. Marketers, imagine being able to trigger a customer workflow, in real time, based on a live customer interactions.

Automate expensive workflows: Reduce time and costs associated with transcribing videos and generating closed captions, as well as flagging and filtering inappropriate content.

Content moderation: Identify when inappropriate content is being shown in a given video. You can instantly conduct content moderation across petabytes of data and more quickly and efficiently filter your content or user-generated content.

What can your organisation do with video AI?

Application modernization is quickly becoming one of the pillars of successful digital transformation and cloud migration initiatives. Many organizations are becoming aware of the dramatic benefits that can be achieved by moving legacy, on-premises apps and databases into cloud native infrastructure and services, such as reduced Total Cost of Ownership (TCO), elimination of expensive commercial software licenses, and improved performance, scalability, security and availability.

The complexity of applications and databases to a cloud-centric architecture requires a rapid, accurate, and customized assessment of modernization potential and identification of challenges. Addressing business and functional drivers, TCO calculations, uncovering technological challenges and cross-platform incompatibilities, preparation of migration, and rollback plans can be essential to the success and outcome of the migration. 

These cloud migration initiatives are often divided into three high-level phases: 

1. Discovery: identifying and cataloging the source inventory. Output is usually an inventory of source apps, databases, servers, networking, storage, etc. The discovery of existing assets within a data center is usually straightforward and can often be highly automated. 
2. Pre-migration readiness: the planning phase. This includes the analysis of the current portfolio of the databases and applications for migration readiness, determining the target architecture, identifying technological challenges or incompatibilities, calculating TCO, and preparing detailed migration plans. 
3. Migration execution: where the rubber hits the road. During this phase of the migration process, database schemas are actively converted, the application data access layer is refactored, data is replicated from source to target, often in real-time, and the application is deployed in its determined compute platform(s). 

Successful evaluation and planning phase as part of the pre-migration readiness phase can bolster confidence in investment towards modernization. Skipping or inaccurately completing the pre-migration phase can lead to a costly and sub-optimal result. Relying on manual pre-migration assessments can lead to long migration timelines, reduced success rates and poor confidence in the post-migration state, increased risk and total migration cost.

Some of the commonly asked question during pre-migration include:

1. How compatible are my source databases, which are often commercial and proprietary in nature, with their open-source cloud-native alternatives? For example, how compatible are my Oracle workloads and usage patterns with Cloud SQL for PostgreSQL?
2. What’s my degree of vendor lock-in with my current technology stack? Are proprietary features and capabilities being used that are incompatible with open-source database technologies?
3. How tightly-coupled are my applications with my current database engine technology? Can my applications be deployed as-is, refactored for cloud readiness with ease, or will it be a big undertaking?
4. How much effort will my migration require? How expensive will it be? What will be my run-rate in Google Cloud post-migration and my ROI?
5. Can we identify quick-win applications and databases to start with?

There is a direct association between the accuracy and speed of the pre-migration phase and the outcome of the migration itself. The faster and more accurately organizations complete the required pre-migration analysis, the more cost efficient and successful the migration itself will usually be.  

EPAM Systems, Inc., a leader in digital transformation, worked with Google Cloud as a preferred partner to accelerate cloud migrations beginning with pre-migration assessments. Leveraging EPAM’s migVisor for Google Cloud—a unique pre-migration accelerator that automates the pre-migration process—and EPAM’s consulting and support services, organizations can quickly generate a cloud migration roadmap for rapid and systematic pre-migration analysis. This approach has resulted in the completion of thousands of database assessments for hundreds of customers.

migVisor is agentless, non-intrusive, and hosted in the EPAM cloud. migVisor seamlessly connects to your source databases and runs SQL queries to ascertain the database configuration, code, schema objects and infrastructure setup. Scanning of source databases is done rapidly and without interruption to production workloads.

migVisor prepares customers to land applications in Google Cloud and its managed suite of databases services and platforms such as Cloud SQL, bare metal hosting, Spanner and Cloud Bigtable. migVisor supports re-hosting (lift-and-shift), re-platforming, and re-factoring.  

“EPAM’s recent application assessment update to its migration tooling system, migVisor, will bring a new level of transparency to the entire application and database modernization process”,  said Dan Sandlin, Google Cloud Data GTM Director at Google Cloud. “This enables organizations to make the most of digital technologies and provides a clear IT ecosystem transformation that allows our customers to build a flexible foundation for future innovation.”

Previously, migVisor focused on assessments of the source databases and the compatibility of customers’ existing database portfolio with cloud-centric database technologies. Coming this quarter, migVisor adds support for application assessments, augmenting its existing and class-leading capabilities in the database space. 

The addition of application modernization assessment functionality in migVisor, combined with EPAM’s certification and specialization in Google Cloud Data Management and hands-on engineering experience, strengthens EPAM’s position as a leader for large-scale digital transformation projects and migVisor as a trusted product for cloud migration assessments to Google Cloud customers. EPAM provides customers an end-to-end solution for faster and more cost-effective migrations.  Assessments that used to take weeks can now be completed in mere days. 

Within minutes of registering for an account, anyone can start using migVisor by EPAM to automatically assess applications and application code. Visit the migVisor page to learn more and sign up for your account.