Enterprises can Push the Limits of Edge Even Further!

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Whether with the cloud or within their own data centers, enterprises have undergone a period of remarkable consolidation and centralization of their compute resources. But with the rise of ever more powerful mobile devices, and increasingly capable cellular networks, application architects are starting to think beyond the confines of the data center, and looking out to the edge.
What exactly do we mean by edge? Think of the edge as distributed compute happening on a wide variety of non-traditional devices — mobile phones of course, but also equipment sensors in factories, industrial equipment, or even temperature and reaction monitoring in a remote lab. Edge devices are also connected devices, and can communicate back to the mothership over wireless or cellular networks.
Equipped with increasingly powerful processors, these edge devices are being called upon to perform tasks that have thus far been outside the scope of traditional IT. For enterprises, this could mean pre-processing incoming telemetry in a vehicle, collecting video in kiosks at a mall, gathering quality control data with cameras in a warehouse, or delivering interactive media to retail stores. Enterprises are also relying on edge to ingest data from outposts or devices that have even more intermittent connectivity, e.g., oil rigs or farm equipment, filtering that data to improve quality, reducing it to right-size information load, and processing it in the cloud. New data and models are then pushed back to the edge; in addition, we can also push configuration, software, and media updates and decentralize processing workload.
Edge isn’t all about enabling new use cases – it’s also about right-sizing environments and improving resource utilization. For example, adopting an edge model can also relieve load on existing data centers.
But while edge computing is full of promise for enterprises, there are many pieces that are still works in progress. Further, developing edge workloads is very different from developing traditional applications, which enjoy the benefits of persistent data connections and run on well-resourced hardware platforms. As such, cloud architects are still in the early days of figuring out how to use and implement edge for their organizations.
Fortunately, there are tools you can use to help ease the transition to edge computing — and that likely fit into your organization’s existing computing systems. Kubernetes, of course, but also higher level management tools like Anthos, which provides a consistent control plane across cloud, private data center and edge locations. Other parts of the Anthos family – Anthos Config Management and Anthos Service Mesh — go one step further and provide consistent, centralized management to your edge and cloud deployments. And there’s more to come.
For the remainder of this blog post, we’ll dive deeper into the past and current state of edge computing, and the benefits that architects and developers can expect to see from edge computing. In a next post, we’ll take a deeper look at some of the challenges that designing for edge introduces, and some of the advantages the average enterprise has in adopting the edge model. Finally, we’ll look at the Google Cloud tools that are available today to help you build out your edge environment, and look at some early customer examples that highlight what’s possible today — and that will spark your imagination for what to do tomorrow.
The evolution of edge computing
The edge is not a new concept. In fact, it’s been around for the last two decades, spanning many use cases that are prevalent today. One of the first applications for edge was to use content delivery networks (CDN) to cache and serve daily static website pages near clients, for example, web servers in California data centers serving financial data to European customers.
As connectivity has improved and software evolved, the edge has evolved too, and the focus has shifted towards using edge to distribute services. First, simple services expanded from static HTML to javascript libraries or image repositories. Common functions like image transformation, credit and address validation support services followed. Soon, organizations were deploying more complex cloudlet and clustered microservices installations, as well as distributed and replicated datasets. The term “endpoint” became ubiquitous, and APIs profilerated.
In parallel, there’s been an explosion of creativity in hardware, microcontrollers and dedicated edge devices. Fit-for-purpose products were deployed globally. Services like Google Cloud IoT Core extended our ability to manage and securely connect these dispersed devices, allowing platform managers to register tools and leverage managed services like Pub/Sub and Dataflow for data ingestion. And with Kubernetes, large remote clusters — mini private clouds in and of themselves — operate as self-healing, autoscaling services across the broader internet, opening the door to new models for applications and architectural patterns. In short, both distributed asynchronous systems and economies have blossomed.
What does this mean for enterprises? For the purposes of this series, edge means you can now go beyond the corporate network, beyond cloud VPCs, and beyond hybrid. The modern edge is not sitting at a major remote data center, nor is it a CDN, cloud provider, or in a corporate data center rack — it’s just as likely to look like 100 of these attached to a thousand sensors.

Edge, in short, is about having hardware and devices installed at remote locations that can process and communicate back the information they collect and generate. The edge management challenge, meanwhile, is being able to push configuration and software/model/media updates to these remote locations when they are connected.
Enable new use cases
Today, we have reached a new threshold for edge computing — one where micro-data-processing centers are deployed as the edge of a fractal arm, as it were. Together, they form a broad, geographically distributed, always-on framework for streaming, collecting, processing and serving asynchronous data. This big, loosely coupled application system lives, breathes and grows. Always changing, always learning from the data it collects — and always pushing out updated models when the tendrils are connected.
Right now, the rise of 5G is pushing the limits of edge even further. Devices enabled with 5G can transmit using a mobile network — no ISP required — enabling connectivity anywhere within reach of a cell tower. Granted, these networks have lower bandwidth, but they are often more than adequate for certain types of data, for example fire sensors in forests bordering remote towns that emit temperature or carbon monoxide data periodically. Recently, Google Cloud partnered with AT&T to enhance business use of 5G edge technology but there is so much more that can be done.
Reduce data center investments
In addition to enabling the digitization of a broad range of new use cases, adopting edge can also benefit your existing data center.
Let’s face it: data centers are expensive to maintain. Moving some data center load to edge locations can reduce your data center infrastructure investment, as well as compute time spent there. Edge services tend to have much lower service level objectives (SLOs) than data center services, driving lower levels of hardware investment. Edge installations also tend to tolerate disconnectedness, and thus function perfectly well with lower SLOs — and lower costs.
Let’s look at an example of where edge can really reduce costs: big data. Back in the day, we used to build monolithic serial processors — state machines — that had to keep track of where they were in processing in case of failure. But time and again, we’ve seen that smaller, more distributed processing can break down big, expensive problems into smaller, more cost-effective chunks.
Starting with the explosion of MapReduce almost 20 years ago, big-data workloads were parallelized across clusters on a network, and state management was simplified with intermediate output to share, wait for, or restart processing from checkpoints. Those monolithic systems were replaced by cheaper, smarter, networked clusters and data repositories where parallel work could be executed and rendered into workable datasets.
Flash forward to today, and we are seeing those same concepts applied and distributed to edge data-collection points. In this evolution of big data processing, we are scaling up and out to the point where observation data is so massive that it must first be prefiltered, and then preprocessed down to a manageable size and still be actionable. Only then should it be written back to the main data repositories for more resource-intensive processing and model building.
In short, data collection, cleanup, and potentially initial aggregation happens at the edge location, which reduces the amount of junk data sitting in costly data stores. This increases performance of the core data warehouse, and reduces the size and cost of network transfers and storage!
The edge is a huge opportunity for today’s enterprises. But designing environments that can make effective use of the edge isn’t without its challenges. Stick around for part two of this series, where we look at some of the architectural challenges typically encountered while designing for the edge and how we begin to address them.
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Enhancing Collaboration with Sheets, Python, and Google Cloud
See how you can enhance collaboration within your organization using Google Sheets. Watch to learn about a new set of tools to create custom functions that tap into the power of Python and to expose functions in a standardized fashion throughout your organization.
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Modernize Your Security Posture for Cloud-Native Applications with Anthos
Modern security approaches have moved beyond a traditional perimeter-based security model. As many organizations seek to adopt cloud-native architectures and are deploying applications in hybrid and multi-cloud environments they demand a more flexible and extensible approach towards security.
Learn how to address security issues as early in the development and deployment life-cycle as possible—when addressing security issues can be less costly—and do so in a way that is standardized and consistent. Help keep your organization secure and compliant with Anthos.
Transform ‘Dark Data’ from Documents with Document AI, Cloud Functions and Workflows

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At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.
At Cloud Next 2021, we presented how to build easy AI-powered applications with Google Cloud. We introduced a sample application for handling incoming expense reports, analyzing expense receipts with Procurement Document AI, a DocAI solution for automating procurement data capture from forms including invoices, utility statements and more. Then organizing the logic of a report approval process with Workflows, and used Cloud Functions as glue to invoke the workflow, and do analysis of the parsed document.

We also open sourced the code on this Github repository, if you’re interested in learning more about this application.

In the above diagram, there are two user journeys: the employee submitting an expense report where multiple receipts are processed at once, and the manager validating or rejecting the expense report.
First, the employee goes to the website, powered by Vue.js for the frontend progressive JavaScript framework and Shoelace for the library of web components. The website is hosted via Firebase Hosting. The frontend invokes an HTTP function that triggers the execution of our business workflow, defined using the Workflows YAML syntax.
Workflows is able to handle long-running operations without any additional code required, in our case we are asynchronously processing a set receipt files. Here, the Document AI connector directly calls the batch processing endpoint for service. This API returns a long-running operation: if you poll the API, the operation state will be “RUNNING” until it has reached a “SUCCEEDED” or “FAILED” state. You would have to wait for its completion. However, Workflows’ connectors handle such long-running operations, without you having to poll the API multiple times till the state changes. Here’s how we call the batch processing operation of the Document AI connector:
- invoke_document_ai:call: googleapis.documentai.v1.projects.locations.processors.batchProcessargs:name: ${"projects/" + project + "/locations/eu/processors/" + processorId}location: "eu"body:inputDocuments:gcsPrefix:gcsUriPrefix: ${bucket_input + report_id}documentOutputConfig:gcsOutputConfig:gcsUri: ${bucket_output + report_id}skipHumanReview: trueresult: document_ai_response
Machine learning uses state of the art Vision and Natural Language Processing models to intelligently extract schematized data from documents with Document AI. As a developer, you don’t have to figure out how to fine tune or reframe the receipt pictures, or how to find the relevant field and information in the receipt. It’s Document AI’s job to help you here: it will return a JSON document whose fields are: line_item, currency, supplier_name, total_amount, etc. Document AI is capable of understanding standardized papers and forms, including invoices, lending documents, pay slips, driver licenses, and more.
A cloud function retrieves all the relevant fields of the receipts, and makes its own tallies, before submitting the expense report for approval to the manager. Another useful feature of Workflows is put to good use: Callbacks, that we introduced last year. In the workflow definition we create a callback endpoint, and the workflow execution will wait for the callback to be called to continue its flow, thanks to those two instructions:
- create_callback:call: events.create_callback_endpointargs:http_callback_method: "POST"result: callback_details...- await_callback:try:call: events.await_callbackargs:callback: ${callback_details}timeout: 3600result: callback_requestexcept:as: esteps:- update_status_to_error:...
In this example application, we combined the intelligent capabilities of Document AI to transform complex image documents into usable structured data, with Cloud Functions for data transformation, process triggering, and callback handling logic, and Workflows enabled us to orchestrate the underlying business process and its service call logic.
Going further
If you’re looking to make sense of your documents, turning dark data into structured information, be sure to check out what Document AI offers. You can also get your hands on a codelab to get started quickly, in which you’ll get a chance at processing handwritten forms. If you want to explore Workflows, quickstarts are available to guide you through your first steps, and likewise, another codelab explores the basics of Workflows. As mentioned earlier, for a concrete example, the source code of our smart expense application is available on Github. Don’t hesitate to reach out to us at @glaforge and @asrivas_dev to discuss smart scalable apps with us.
What Users and System Designers Must Learn from Google’s Best Practices for Password Management

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Updated for 2021: This post includes updated best practices including the latest from Google’s Best Practices for Password Management whitepapers for both users and system designers.
Account management, authentication and password management can be tricky. Often, account management is a dark corner that isn’t a top priority for developers or product managers. The resulting experience often falls short of what some of your users would expect for data security and user experience.
Fortunately, Google Cloud brings several tools to help you make good decisions around the creation, secure handling and authentication of user accounts (in this context, anyone who identifies themselves to your system—customers or internal users). Whether you’re responsible for a website hosted in Google Kubernetes Engine, an API on Apigee, an app using Firebase, or other service with authenticated users, this post lays out the best practices to follow to ensure you have a safe, scalable, usable account authentication system.
1. Hash those passwords
My most important rule for account management is to safely store sensitive user information, including their password. You must treat this data as sacred and handle it appropriately.
Do not store plaintext passwords under any circumstances. Your service should instead store a cryptographically strong hash of the password that cannot be reversed—created with Argon2id, or Scrypt. The hash should be salted with a value unique to that specific login credential. Do not use deprecated hashing technologies such as MD5, SHA1 and under no circumstances should you use reversible encryption or try to invent your own hashing algorithm. Use a pepper that is not stored in the database to further protect the data in case of a breach. Consider the advantages of iteratively re-hashing the password multiple times.
Design your system assuming it will be compromised eventually. Ask yourself “If my database were exfiltrated today, would my users’ safety and security be in peril on my service or other services they use?” As well as “What can we do to mitigate the potential for damage in the event of a leak?”
Another point: If you could possibly produce a user’s password in plaintext at any time outside of immediately after them providing it to you, there’s a problem with your implementation.
If your system requires detection of near-duplicate passwords, such as changing “Password” to “pAssword1”, save the hashes of common variants you wish to ban with all letters normalized and converted to lowercase. This can be done when a password is created or upon successful login for pre-existing accounts. When the user creates a new password, generate the same type of variants and compare the hashes to those from the previous passwords. Use the same level of hashing security as with the actual password.
2. Allow for third-party identity providers if possible
Third-party identity providers enable you to rely on a trusted external service to authenticate a user’s identity. Google, Facebook, and Twitter are commonly used providers.
You can implement external identity providers alongside your existing internal authentication system using a platform such as Identity Platform. There are a number of benefits that come with Identity Platform, including simpler administration, a smaller attack surface, and a multi-platform SDK. We’ll touch on more benefits throughout this list.
3. Separate the concept of user identity and user account
Your users are not an email address. They’re not a phone number. They’re not even a unique username. Any of these authentication factors should be mutable without changing the content or personally identifiable information (PII) in the account. Your users are the multi-dimensional culmination of their unique, personalized data and experience within your service, not the sum of their credentials. A well-designed user management system has low coupling and high cohesion between different parts of a user’s profile.
Keeping the concepts of user account and credentials separate will greatly simplify the process of implementing third-party identity providers, allowing users to change their username, and linking multiple identities to a single user account. In practical terms, it may be helpful to have an abstract internal global identifier for every user and associate their profile and one or more sets of authentication datavia that ID as opposed to piling it all in a single record.
4. Allow multiple identities to link to a single user account
A user who authenticates to your service using their username and password one week might choose Google Sign-In the next without understanding that this could create a duplicate account. Similarly, a user may have very good reason to link multiple email addresses to your service. If you’ve properly separated user identity and authentication, it will be a simple process to link several authentication methods to a single user.
Your backend will need to account for the possibility that a user gets part or all the way through the signup process before they realize they’re using a new third-party identity not linked to their existing account in your system. This is most simply achieved by asking the user to provide a common identifying detail, such as email address, phone, or username. If that data matches an existing user in your system, require them to also authenticate with a known identity provider and link the new ID to their existing account.
5. Don’t block long or complex passwords
NIST publishes guidelines on password complexity and strength. Since you are (or will be very soon) using a strong cryptographic hash for password storage, a lot of problems are solved for you. Hashes will always produce a fixed-length output no matter the input length, so your users should be able to use passwords as long as they like. If you must cap password length, do so based on the limits of your infrastructure; often this is a matter of memory usage (memory used per login operation * potential concurrent logins per machine), or more likely—the maximum POST size allowable by your servers. We’re talking numbers from hundreds of KB to over 1MB. Seriously. Your application should already be hardened to prevent abuse from large inputs. This doesn’t create new opportunities for abuse if you employ controls to prevent credential stuffing and hash the input as soon as possible to free up memory.
Your hashed passwords will likely already consist of a small set of ASCII characters. If not, you can easily convert a binary hash to Base64. With that in mind, you should allow your users to use literally any characters they wish in their password. If someone wants a password made of Klingon, Emoji, and ASCII art with whitespace on both ends, you should have no technical reason to deny them. Just make sure to perform Unicode normalization to ensure cross-platform compatibility. See our system designers whitepaper (PDF) for more information on Unicode and supported characters in passwords.
Any user attempting to use an extreme password is probably following password best practices (PDF) including using a password manager, which allows the entry of complex passwords even on limited mobile device keyboards. If a user can input the string in the first place (i.e., the HTML specification for password input disallows line feed and carriage return), the password should be acceptable.
6. Don’t impose unreasonable rules for usernames
It’s not unreasonable for a site or service to require usernames longer than two or three characters, block hidden characters, and prevent whitespace at the beginning and end of a username. However, some sites go overboard with requirements such as a minimum length of eight characters or by blocking any characters outside of 7-bit ASCII letters and numbers.
A site with tight restrictions on usernames may offer some shortcuts to developers, but it does so at the expense of users and extreme cases will deter some users.
There are some cases where the best approach is to assign usernames. If that’s the case for your service, ensure the assigned username is user-friendly insofar as they need to recall and communicate it. Alphanumeric generated IDs should avoid visually ambiguous symbols such as “Il1O0.” You’re also advised to perform a dictionary scan on any randomly generated string to ensure there are no unintended messages embedded in the username. These same guidelines apply to auto-generated passwords.
7. Validate the user’s identity
If you ask a user for contact information, you should validate that contact as soon as possible. Send a validation code or link to the email address or phone number. Otherwise, users may make a typo in their contact info and then spend considerable time using your service only to find there is no account matching their info the next time they attempt login. These accounts are often orphaned and unrecoverable without manual intervention. Worse still, the contact info may belong to someone else, handing full control of the account to a third party.
8. Allow users to change their username
It’s surprisingly common in legacy systems or any platform that provides email accounts not to allow users to change their username. There are very good reasons not to automatically release usernames for reuse, but long-term users of your system will eventually come up with significant reasons to use a different username and they likely won’t want to create a new account.
You can honor your users’ desire to change their usernames by allowing aliases and letting your users choose the primary alias. You can apply any business rules you need on top of this functionality. Some orgs might limit the number of username changes per year or prevent a user from displaying or being contacted via anything but their primary username. Email address providers are advised to never re-issue email addresses, but they could alias an old email address to a new one. A progressive email address provider might even allow users to bring their own domain name and have any address they wish.
If you are working with a legacy architecture, this best practice can be very difficult to meet. Even companies like Google have technical hurdles that make this more difficult than it would seem. When designing new systems, make every effort to separate the concept of user identity and user account and allow multiple identities to link to a single user account and this will be a much smaller problem. Whether you are working on existing or greenfield code, choose the right rules for your organization with an emphasis on allowing your users to grow and change over time.
9. Let your users delete their accounts
A surprising number of services have no self-service means for a user to delete their account and associated PII. Depending on the nature of your service, this may or may not include public content they created such as posts and uploads. There are a number of good reasons for a user to close an account permanently and delete all their PII . These concerns need to be balanced against your user experience, security, and compliance needs. Many if not most systems operate under some sort of regulatory control (such as PCI or GDPR), which provides specific guidelines on data retention for at least some user data. A common solution to avoid compliance concerns and limit data breach potential is to let users schedule their account for automatic future deletion.
In some circumstances, you may be legally required to comply with a user’s request to delete their PII in a timely manner. You also greatly increase your exposure in the event of a data breach where the data from “closed” accounts is leaked.
10. Make a conscious decision on session length
An often overlooked aspect of security and authentication is session length. Google puts a lot of effort into ensuring users are who they say they are and will double-check based on certain events or behaviors. Users can take steps to increase their security even further.
Your service may have good reason to keep a session open indefinitely for non-critical analytics purposes, but there should be thresholds after which you ask for password, 2nd factor, or other user verification.
Consider how long a user should be able to be inactive before re-authenticating. Verify user identity in all active sessions if someone performs a password reset. Prompt for authentication or 2nd factor if a user changes core aspects of their profile or when they’re performing a sensitive action. Re-authenticate if the user’s location changes significantly in a short period of time. Consider whether it makes sense to disallow logging in from more than one device or location at a time.
When your service does expire a user session or requires re-authentication, prompt the user in real time or provide a mechanism to preserve any activity they have not saved since they were last authenticated. It’s very frustrating for a user to take a long time to fill out a form, only to find all their input has been lost and they must log in again.
11. Use 2-Step Verification
Consider the practical impact on a user of having their account stolen when choosing 2-Step Verification (also known as two-factor authentication, MFA, or 2FA) methods. Time-based one-time passwords (TOTP), email verification codes, or “magic links” are consumer-friendly and relatively secure. SMS 2FA auth has been deprecated by NIST due to multiple weaknesses, but it may be the most secure option your users will accept for what they consider a trivial service.
Offer the most secure 2FA auth you reasonably can. Hardware 2FA such as the Titan Security Key are ideal if feasible for your application. Even if a TOTP library is unavailable for your application, email verification or 2FA provided by third-party identity providers is a simple means to boost your security without great expense or effort. Just remember that your user accounts are only as secure as the weakest 2FA or account recovery method.
12. Make user IDs case-insensitive
Your users don’t care and may not even remember the exact case of their username. Usernames should be fully case-insensitive. It’s trivial to store usernames and email addresses in all lowercase and transform any input to lowercase before comparing. Make sure to specify a locale or employ Unicode normalization on any transformations.
Smartphones represent an ever-increasing percentage of user devices. Most of them offer autocorrect and automatic capitalization of plain-text fields. Preventing this behavior at the UI level might not be desirable or completely effective, and your service should be robust enough to handle an email address or username that was unintentionally auto-capitalized.
13. Build a secure auth system
If you’re using a service like Identity Platform, a lot of security concerns are handled for you automatically. However, your service will always need to be engineered properly to prevent abuse. Core considerations include implementing a password reset instead of password retrieval, detailed account activity logging, rate-limiting login attempts to prevent credential stuffing, locking out accounts after too many unsuccessful login attempts, and requiring two-factor authentication for unrecognized devices or accounts that have been idle for extended periods. There are many more aspects to a secure authentication system, so please see the further reading section below for links to more information.
Further reading
There are a number of excellent resources available to guide you through the process of developing, updating, or migrating your account and authentication management system. I recommend the following as a starting place:
- Our Modern Password Security for System Designers whitepaper (PDF)
- The related Modern password security for users whitepaper (PDF)
- NIST 800-063B covers Authentication and Lifecycle Management
- OWASP continually updates their Password Storage Cheat Sheet
- OWASP goes into even more detail with the Authentication Cheat Sheet
- Google Cloud Identity Platform is a multi-protocol customer identity and access management solution, with robust authentication features
- Google’s Firebase Authentication site has a rich library of guides, reference materials and sample code
A Run-through of an Innovative 2021 with Apigee

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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:
- Retail:
- Financial services:
- Energy:
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.
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