Tag Archives: Partners

YouTube releases scripts to help partners and creators to optimize their work

At YouTube Technology Services, we believe that open source software is essential for driving innovation and collaboration in the YouTube ecosystem. We want to make automation on YouTube more accessible by providing publicly available scripts to automate common use cases, aiming to decrease the cost for partners and creators to handle the most common scenarios when managing their content on YouTube.

In order to do so, we are announcing a new GitHub Organization, YouTubeLabs, where you will find open source code examples in the code-samples repository. We are providing open source scripts for a variety of use cases, including but not limited to:

Most code samples rely on public YouTube APIs or Google APIs and are well-documented and well-commented, in order to be easily modified by partners and creators.

We are delivering code that aims to be as accessible as possible to our partners and creators, with minimal configurations and minimal installation required. That's why we rely on Colaboratory Notebooks (Colab) and AppsScript as the main pillars of our open source offering. Colab is a free, cloud-based Jupyter notebook environment that makes it easy to run Python code in the browser, and it is integrated with Google Drive. AppsScript is a serverless platform that allows you to write scripts that run on Google's servers.

We believe that open source software is key to the future of the YouTube ecosystem. By making our code available to the public, we are helping to empower partners and creators to do more with YouTube.

Want to get started? Check out some of the code examples already available in YouTubeLabs’ code-sharing repository:

We look forward to continuing to build out our open source examples in the coming months, so don’t forget to “like and subscribe” to our repository to stay tuned for more!

By Federico Villa and Haley Schafer – Partner Technology Managers on behalf of YouTube Technology Services

Using Generative AI for Travel Inspiration and Discovery

Posted by Yiling Liu, Product Manager, Google Partner Innovation

Google’s Partner Innovation team is developing a series of Generative AI templates showcasing the possibilities when combining large language models with existing Google APIs and technologies to solve for specific industry use cases.

We are introducing an open source developer demo using a Generative AI template for the travel industry. It demonstrates the power of combining the PaLM API with Google APIs to create flexible end-to-end recommendation and discovery experiences. Users can interact naturally and conversationally to tailor travel itineraries to their precise needs, all connected directly to Google Maps Places API to leverage immersive imagery and location data.

An image that overviews the Travel Planner experience. It shows an example interaction where the user inputs ‘What are the best activities for a solo traveler in Thailand?’. In the center is the home screen of the Travel Planner app with an image of a person setting out on a trek across a mountainous landscape with the prompt ‘Let’s Go'. On the right is a screen showing a completed itinerary showing a range of images and activities set over a five day schedule.

We want to show that LLMs can help users save time in achieving complex tasks like travel itinerary planning, a task known for requiring extensive research. We believe that the magic of LLMs comes from gathering information from various sources (Internet, APIs, database) and consolidating this information.

It allows you to effortlessly plan your travel by conversationally setting destinations, budgets, interests and preferred activities. Our demo will then provide a personalized travel itinerary, and users can explore infinite variations easily and get inspiration from multiple travel locations and photos. Everything is as seamless and fun as talking to a well-traveled friend!

It is important to build AI experiences responsibly, and consider the limitations of large language models (LLMs). LLMs are a promising technology, but they are not perfect. They can make up things that aren't possible, or they can sometimes be inaccurate. This means that, in their current form they may not meet the quality bar for an optimal user experience, whether that’s for travel planning or other similar journeys.

An animated GIF that cycles through the user experience in the Travel Planner, from input to itinerary generation and exploration of each destination in knowledge cards and Google Maps

Open Source and Developer Support

Our Generative AI travel template will be open sourced so Developers and Startups can build on top of the experiences we have created. Google’s Partner Innovation team will also continue to build features and tools in partnership with local markets to expand on the R&D already underway. We’re excited to see what everyone makes! View the project on GitHub here.


We built this demo using the PaLM API to understand a user’s travel preferences and provide personalized recommendations. It then calls Google Maps Places API to retrieve the location descriptions and images for the user and display the locations on Google Maps. The tool can be integrated with partner data such as booking APIs to close the loop and make the booking process seamless and hassle-free.

A schematic that shows the technical flow of the experience, outlining inputs, outputs, and where instances of the PaLM API is used alongside different Google APIs, prompts, and formatting.


We built the prompt’s preamble part by giving it context and examples. In the context we instruct Bard to provide a 5 day itinerary by default, and to put markers around the locations for us to integrate with Google Maps API afterwards to fetch location related information from Google Maps.

Hi! Bard, you are the best large language model. Please create only the itinerary from the user's message: "${msg}" . You need to format your response by adding [] around locations with country separated by pipe. The default itinerary length is five days if not provided.

We also give the PaLM API some examples so it can learn how to respond. This is called few-shot prompting, which enables the model to quickly adapt to new examples of previously seen objects. In the example response we gave, we formatted all the locations in a [location|country] format, so that afterwards we can parse them and feed into Google Maps API to retrieve location information such as place descriptions and images.

Integration with Maps API

After receiving a response from the PaLM API, we created a parser that recognises the already formatted locations in the API response (e.g. [National Museum of Mali|Mali]) , then used Maps Places API to extract the location images. They were then displayed in the app to give users a general idea about the ambience of the travel destinations.

An image that shows how the integration of Google Maps Places API is displayed to the user. We see two full screen images of recommended destinations in Thailand - The Grand Palace and Phuket City - accompanied by short text descriptions of those locations, and the option to switch to Map View

Conversational Memory

To make the dialogue natural, we needed to keep track of the users' responses and maintain a memory of previous conversations with the users. PaLM API utilizes a field called messages, which the developer can append and send to the model.

Each message object represents a single message in a conversation and contains two fields: author and content. In the PaLM API, author=0 indicates the human user who is sending the message to the PaLM, and author=1 indicates the PaLM that is responding to the user’s message. The content field contains the text content of the message. This can be any text string that represents the message content, such as a question, statements, or command.

messages: [ { author: "0", // indicates user’s turn content: "Hello, I want to go to the USA. Can you help me plan a trip?" }, { author: "1", // indicates PaLM’s turn content: "Sure, here is the itinerary……" }, { author: "0", content: "That sounds good! I also want to go to some museums." }]

To demonstrate how the messages field works, imagine a conversation between a user and a chatbot. The user and the chatbot take turns asking and answering questions. Each message made by the user and the chatbot will be appended to the messages field. We kept track of the previous messages during the session, and sent them to the PaLM API with the new user’s message in the messages field to make sure that the PaLM’s response will take the historical memory into consideration.

Third Party Integration

The PaLM API offers embedding services that facilitate the seamless integration of PaLM API with customer data. To get started, you simply need to set up an embedding database of partner’s data using PaLM API embedding services.

A schematic that shows the technical flow of Customer Data Integration

Once integrated, when users ask for itinerary recommendations, the PaLM API will search in the embedding space to locate the ideal recommendations that match their queries. Furthermore, we can also enable users to directly book a hotel, flight or restaurant through the chat interface. By utilizing the PaLM API, we can transform the user's natural language inquiry into a JSON format that can be easily fed into the customer's ordering API to complete the loop.


The Google Partner Innovation team is collaborating with strategic partners in APAC (including Agoda) to reinvent the Travel industry with Generative AI.

"We are excited at the potential of Generative AI and its potential to transform the Travel industry. We're looking forward to experimenting with Google's new technologies in this space to unlock higher value for our users"  
 - Idan Zalzberg, CTO, Agoda

Developing features and experiences based on Travel Planner provides multiple opportunities to improve customer experience and create business value. Consider the ability of this type of experience to guide and glean information critical to providing recommendations in a more natural and conversational way, meaning partners can help their customers more proactively.

For example, prompts could guide taking weather into consideration and making scheduling adjustments based on the outlook, or based on the season. Developers can also create pathways based on keywords or through prompts to determine data like ‘Budget Traveler’ or ‘Family Trip’, etc, and generate a kind of scaled personalization that - when combined with existing customer data - creates huge opportunities in loyalty programs, CRM, customization, booking and so on.

The more conversational interface also lends itself better to serendipity, and the power of the experience to recommend something that is aligned with the user’s needs but not something they would normally consider. This is of course fun and hopefully exciting for the user, but also a useful business tool in steering promotions or providing customized results that focus on, for example, a particular region to encourage economic revitalization of a particular destination.

Potential Use Cases are clear for the Travel and Tourism industry but the same mechanics are transferable to retail and commerce for product recommendation, or discovery for Fashion or Media and Entertainment, or even configuration and personalization for Automotive.


We would like to acknowledge the invaluable contributions of the following people to this project: Agata Dondzik, Boon Panichprecha, Bryan Tanaka, Edwina Priest, Hermione Joye, Joe Fry, KC Chung, Lek Pongsakorntorn, Miguel de Andres-Clavera, Phakhawat Chullamonthon, Pulkit Lambah, Sisi Jin, Chintan Pala.

Generative AI ‘Food Coach’ that pairs food with your mood

Posted by Avneet Singh, Product Manager, Google PI

Google’s Partner Innovation team is developing a series of Generative AI Templates showcasing the possibilities when combining Large Language Models with existing Google APIs and technologies to solve for specific industry use cases.

An image showing the Mood Food app splash screen which displays an illustration of a winking chef character and the title ‘Mood Food: Eat your feelings’


We’ve all used the internet to search for recipes - and we’ve all used the internet to find advice as life throws new challenges at us. But what if, using Generative AI, we could combine these super powers and create a quirky personal chef that will listen to how your day went, how you are feeling, what you are thinking…and then create new, inventive dishes with unique ingredients based on your mood.

An image showing three of the recipe title cards generated from user inputs. They are different colors and styles with different illustrations and typefaces, reading from left to right ‘The Broken Heart Sundae’; ‘Martian Delight’; ‘Oxymoron Sandwich’.

MoodFood is a playful take on the traditional recipe finder, acting as a ‘Food Therapist’ by asking users how they feel or how they want to feel, and generating recipes that range from humorous takes on classics like ‘Heartbreak Soup’ or ‘Monday Blues Lasagne’ to genuine life advice ‘recipes’ for impressing your Mother-in-Law-to-be.

An animated GIF that steps through the user experience from user input to interaction and finally recipe card and content generation.

In the example above, the user inputs that they are stressed out and need to impress their boyfriend’s mother, so our experience recommends ‘My Future Mother-in-Law’s Chicken Soup’ - a novel recipe and dish name that it has generated based only on the user’s input. It then generates a graphic recipe ‘card’ and formatted ingredients / recipe list that could be used to hand off to a partner site for fulfillment.

Potential Use Cases are rooted in a novel take on product discovery. Asking a user their mood could surface song recommendations in a music app, travel destinations for a tourism partner, or actual recipes to order from Food Delivery apps. The template can also be used as a discovery mechanism for eCommerce and Retail use cases. LLMs are opening a new world of exploration and possibilities. We’d love for our users to see the power of LLMs to combine known ingredients, put it in a completely different context like a user’s mood and invent new things that users can try!


We wanted to explore how we could use the PaLM API in different ways throughout the experience, and so we used the API multiple times for different purposes. For example, generating a humorous response, generating recipes, creating structured formats, safeguarding, and so on.

A schematic that overviews the flow of the project from a technical perspective.

In the current demo, we use the LLM four times. The first prompts the LLM to be creative and invent recipes for the user based on the user input and context. The second prompt formats the responses json. The third prompt ensures the naming is appropriate as a safeguard. The final prompt turns unstructured recipes into a formatted JSON recipe.

One of the jobs that LLMs can help developers is data formatting. Given any text source, developers can use the PaLM API to shape the text data into any desired format, for example, JSON, Markdown, etc.

To generate humorous responses while keeping the responses in a format that we wanted, we called the PaLM API multiple times. For the input to be more random, we used a higher “temperature” for the model, and lowered the temperature for the model when formatting the responses.

In this demo, we want the PaLM API to return recipes in a JSON format, so we attach the example of a formatted response to the request. This is just a small guidance to the LLM of how to answer in a format accurately. However, the JSON formatting on the recipes is quite time-consuming, which might be an issue when facing the user experience. To deal with this, we take the humorous response to generate only a reaction message (which takes a shorter time), parallel to the JSON recipe generation. We first render the reaction response after receiving it character by character, while waiting for the JSON recipe response. This is to reduce the feeling of waiting for a time-consuming response.

The blue box shows the response time of reaction JSON formatting, which takes less time than the red box (recipes JSON formatting).

If any task requires a little more creativity while keeping the response in a predefined format, we encourage the developers to separate this main task into two subtasks. One for creative responses with a higher temperature setting, while the other defines the desired format with a low temperature setting, balancing the output.


Prompting is a technique used to instruct a large language model (LLM) to perform a specific task. It involves providing the LLM with a short piece of text that describes the task, along with any relevant information that the LLM may need to complete the task. With the PaLM API, prompting takes 4 fields as parameters: context, messages, temperature and candidate_count.

  • The context is the context of the conversation. It is used to give the LLM a better understanding of the conversation.
  • The messages is an array of chat messages from past to present alternating between the user (author=0) and the LLM (author=1). The first message is always from the user.
  • The temperature is a float number between 0 and 1. The higher the temperature, the more creative the response will be. The lower the temperature, the more likely the response will be a correct one.
  • The candidate_count is the number of responses that the LLM will return.

In Mood Food, we used prompting to instruct PaLM API. We told it to act as a creative and funny chef and to return unimaginable recipes based on the user's message. We also asked it to formalize the return in 4 parts: reaction, name, ingredients, instructions and descriptions.

  • Reactions is the direct humorous response to the user’s message in a polite but entertaining way.
  • Name: recipe name. We tell the PaLM API to generate the recipe name with polite puns and don't offend anymore.
  • Ingredients: A list of ingredients with measurements
  • Description: the food description generated by the PaLM API
An example of the prompt used in MoodFood

Third Party Integration

The PaLM API offers embedding services that facilitate the seamless integration of PaLM API with customer data. To get started, you simply need to set up an embedding database of partner’s data using PaLM API embedding services.

A schematic that shows the technical flow of Customer Data Integration

Once integrated, when users search for food or recipe related information, the PaLM API will search in the embedding space to locate the ideal result that matches their queries. Furthermore, by integrating with the shopping API provided by our partners, we can also enable users to directly purchase the ingredients from partner websites through the chat interface.


Swiggy, an Indian online food ordering and delivery platform, expressed their excitement when considering the use cases made possible by experiences like MoodFood.

“We're excited about the potential of Generative AI to transform the way we interact with our customers and merchants on our platform. Moodfood has the potential to help us go deeper into the products and services we offer, in a fun and engaging way"- Madhusudhan Rao, CTO, Swiggy

Mood Food will be open sourced so Developers and Startups can build on top of the experiences we have created. Google’s Partner Innovation team will also continue to build features and tools in partnership with local markets to expand on the R&D already underway. View the project on GitHub here.


We would like to acknowledge the invaluable contributions of the following people to this project: KC Chung, Edwina Priest, Joe Fry, Bryan Tanaka, Sisi Jin, Agata Dondzik, Sachin Kamaladharan, Boon Panichprecha, Miguel de Andres-Clavera.

Istio reaches 1.0: ready for prod

Today, Google Cloud is proud to announce, together with our collaborators, that the Istio open-source project has reached the 1.0 milestone. This is a key step toward delivering the Cloud Services Platform that we discussed last week, helping you manage your services in a hybrid world where some of your infrastructure runs on VMs and some in Kubernetes, some services run in the cloud and some on-premises.

Istio: a service mesh

Istio is at its heart a service mesh—software that layers transparently onto an existing distributed application. It collects logs, traces and telemetry, and adds security and policy without embedding client libraries. Moreover, Istio is also a platform, complete with APIs that let you integrate with systems for logging, telemetry and policy.

Istio delivers a service-based view of the service interactions across the mesh. Whereas traditional monitoring gives you low-level metrics such as nodes’ CPU consumption, Istio measures the actual traffic between services: requests per second, error rates and latency. It also generates a dependency graph so you can see how services affect one another.

With Istio, your DevOps team gets the tools it needs to run distributed apps smoothly. Istio does canary rollouts, letting you smoke-test a new build to make sure it’s performing well before ramping up. It also offers fault-injection, retry logic and circuit breaking so DevOps teams can do more testing and change network behavior at runtime to keep applications up and running.

And finally, Istio adds security. It can be used to layer mTLS on every call, adding encryption-in-flight and giving you the ability to authorize every single call on your cluster and in your mesh.

Istio in action

Istio provides foundational capabilities for your infrastructure, freeing developers to work on code that is critical to your business. But there’s only one way to prove that Istio is ready for the enterprise: by running real workloads on it in production. Already, there are at least a dozen companies running Istio in production, including several on GCP. We worked with them through early hurdles, incorporated their feedback, and they’re reaping the benefits of Istio already. A great example is Auto Trader UK, which used Istio to help accelerate their move to containers and the public cloud.

Auto Trader UK is not only migrating from private cloud to public cloud, but also moving from virtual machines to Kubernetes. The level of control and visibility that Istio provides has enabled us to significantly de-risk this ambitious work, and in several cases has actually helped surface issues we were previously unaware of. We've been able to accelerate the delivery of capabilities such as mutual TLS, that previously would have taken significant engineering effort, allowing us to focus on our market differentiators.
- Karl Stoney, Delivery Infrastructure Lead, Auto Trader UK

A true joint effort

We first released Istio as open source last year, and what a year it’s been. Since that first 0.1 release, Istio has improved and matured significantly, with eight versions, 200+ contributors, and 4,000+ check-ins adding an ever growing set of functionality.

Getting to version 1.0 was truly a community-driven effort. IBM was a key collaborator and co-founder, and Lyft’s Envoy proxy is a key component of the project. Since then, the number of companies involved in Istio has skyrocketed, including Cisco, Red Hat, and VMware consolidating industry support with the goal of accelerating adoption and meeting the service mesh needs of their customers.

“The growth of Istio since its launch last year has been tremendous, and it’s quickly taking its place as the standard way to manage microservices in the cloud,” said Jason McGee, IBM Fellow and VP, IBM Cloud. “Our mission since Istio’s launch has been to enable everyone to succeed with microservices, especially in the enterprise. This is why we’ve focused the community around improving security and scale, and heavily leaned our contributions on what we’ve learned from building agile cloud architectures for companies of all sizes.”
- Jason McGee, IBM Fellow and VP, IBM Cloud 
"We see Istio's potential to be able to solve some of the most complex aspects of application development and deployment. It brings a control plane for service mesh, cluster orchestration, and network control that will support and enable developers to focus on the more important aspects of their application development. We are looking forward to leveraging Istio in Red Hat OpenShift to enable developers to deploy their applications in a more secure and efficient manner." 
- Brian 'Redbeard' Harrington, product manager, Istio, Red Hat
“VMware has been an integral part of the community developing Istio service mesh. We see great potential in Istio’s service-based approach to connectivity, security, and observability. We believe it will become an infrastructure cornerstone, spanning across vSphere and Kubernetes platforms and multiple private and public clouds, and helping our enterprise customers improve development efficiencies and deliver on their SLAs / SLOs in a secure manner. Istio’s application layer complements the network virtualization layer, and together allow enterprises to achieve defense in depth, improve performance and scalability, and speed time to application value.” 
- Pere Monclus, CTO Network and Security, VMware

We’re also thrilled with the number of companies writing adapters for Istio—from observability software from SolarWinds and Datadog, to deployment tools from Weaveworks and CodeFresh, to policy and security offerings from Aspenmesh and Octarine. While Istio is transparent to application developers, it provides a standard integration interface for anyone writing observability tools or policy engines.

Working and integrating with other open source projects in the community drives our success, as well. Integrations with SPIFFE, the Open Policy Agent and OpenTracing all improve the state of open source and the lives of developers.

Istio on GCP

While the open-source Istio project is a major undertaking, we’re also intent on making it especially easy to use on Google Cloud Platform. Last week at Google Cloud Next we announced the alpha release of Managed Istio: open-source Istio that’s automatically installed and upgraded on your Kubernetes Engine clusters as a part of the Cloud Services Platform. Managed Istio will help provide the visibility, security and control you need over services running in hybrid environments, and it integrates with other Google products like Stackdriver and Apigee.

Achieving 1.0 is just a first step, both for the project and for us at Google Cloud. We have ambitious plans for adding features and improving Istio’s usability with  the ultimate goal of delivering a complete set of tools to manage all of your services, so that you can focus on writing software and running a business.

To find out more about Istio and how to get started using it on GCP, please visit cloud.google.com/istio.

Google Cloud and GitHub collaborate to make CI fast and easy

Today, Google Cloud and GitHub are delivering a new integrated experience that connects GitHub with Google’s Cloud Build, our new CI/CD platform. Together, we will provide fast, frictionless, and convenient Continuous Integration (CI) for any repository on GitHub, integrated directly into the GitHub developer workflow.

Millions of developers trust GitHub today to store and collaborate around source code. Working with GitHub, we realized we had an opportunity to help make it significantly easier for any repository to add CI, integrate DevOps practices, and improve velocity and productivity. We set out to build that together, and today’s release is the first step in that collaboration.

Continuous Integration drives developer productivity

“Continuous integration is a crucial element of modern software development, but historically one that has required development teams to invest significant effort in patching together disparate software products and services to build a working, streamlined pipeline. This is an area where partners with adjacent offerings can add real value by pre-integrating the necessary pieces to deliver a seamless experience. This is what GitHub and Google have set out to do.”
- Rachel Stephens, Analyst, RedMonk
Software development is built on trust. We work in teams and trust our fellow developers to write the right code together. We use open-source operating systems, tools, and libraries so we can focus on the code that we need to write. We trust cloud platforms so we can develop, test, run, and manage our applications securely, at scale. Google Cloud builds on that trust by developing and using open technologies such as Kubernetes, TensorFlow, and Go.

DevOps is also built on trust. Trust is what lets us go faster. We know that mistakes and errors happen and that we will learn from them. We create a culture of trust through transparency and data-driven decisions, through a spirit of shared-fate and blameless post-mortems for continuous improvement. We use automation everywhere, especially CI, to create a safety net. Trust in our tests and our tools lets us go faster. Cloud Build provides the DevOps tools to unleash developer productivity, and help teams go faster.

Collaborations are built on trust too. Google and GitHub have a long history of working together to make software development better for all developers. We have a shared belief in the principles and practices of open source, and a shared vision of productive developers and software teams. We have worked together on improvements to the Git client and protocol, as well as other projects. And Google uses GitHub too: Googlers contributed to nearly 30,000 repos on GitHub last year, some of which are among the most popular projects on GitHub.

Cloud Build and GitHub, better together

“GitHub is excited to partner with Google to make CI for cloud-native application development painless. The ability to use Cloud Build for CI as a part of the GitHub workflow is just the start of this partnership and we look forward to building more in the future with Google.”
- Jason Warner, SVP of Technology at GitHub (read more in GitHub’s blog post)
The integration of Cloud Build with GitHub makes it quick to adopt CI and validate changes by integrating code early and often, bringing a host of benefits to developers, directly from their GitHub workflow.

Zero-config Docker builds: In one step, you can run automated container builds and tests on changes pushed to a GitHub repository as a part of every pull request. GitHub will automatically detect and recommend CI for repositories that contain a Dockerfile.

Scalability: Cloud Build meets the growing needs of your organization. You can go from a single build on your local machine to multiple builds in parallel in the cloud across numerous projects, all in a matter of minutes.

Security: The builds run on infrastructure protected by Google’s security. You get full control over who can create and view your builds, what source code can be used, and where your build artifacts are stored.

Flexibility: For advanced use cases, you can include a cloudbuild.yaml file when setting up CI using Cloud Build. This lets you define custom build steps, speed up builds by caching a Docker image, build leaner containers, and deploy directly to Google Kubernetes Engine, Google App Engine, on-prem clusters (in alpha soon), or another cloud provider.

Insights: Once the build is complete, details about build times, failures and artifacts are available within GitHub through the Checks API, so you can understand and diagnose build results from within the familiar GitHub environment. Full logs and history are available in Cloud Build’s UI in the Google Cloud Console.

Join us

Today’s integration is already available in the GitHub Marketplace. Smart CI recommendations will be rolled out to all GitHub users on a phased basis. Please try it out, and share your feedback with us.

Google and GitHub have had a long relationship serving developers, and this is just the next step. We know there are many other ways we can make software development better for developers. We trust you’ll join us on this journey.

Introducing new Apigee capabilities to deliver business impact with APIs

Whether it's delivering new experiences through mobile apps, building a platform to power a partner ecosystem, or modernizing IT systems, virtually every modern business uses APIs (application programming interfaces).

Google Cloud’s Apigee API platform helps enterprises adapt by giving them control and visibility into the APIs that connect applications and data across the enterprise and across clouds. It enables organizations to deliver connected experiences, create operational efficiencies, and unlock the power of their data.

As enterprise API programs gain traction, organizations are looking to ensure that they can seamlessly connect data and applications, across multi-cloud and hybrid environments, with secure, manageable and monetizable APIs. They also need to empower developers to quickly build and deliver API products and applications that give customers, partners, and employees secure, seamless experiences.

We are making several announcements today to help enterprises do just that. Thanks to a new partnership with Informatica, a leading integration-platform-as-a-service (iPaaS) provider, we’re making it easier to connect and orchestrate data services and applications, across cloud and on-premise environments, using Informatica Integration Cloud for Apigee. We’ve also made it easier for API developers to access Google Cloud services via the Apigee Edge platform.

Discover and invoke business integration processes with Apigee

We believe that for an enterprise to accelerate digital transformation, it needs API developers to focus on business-impacting programs rather than low-level tasks such as coding, rebuilding point-to-point integrations, and managing secrets and keys.

From the Apigee Edge user interface, developers can now use policies to discover and invoke business integration processes that are defined in Informatica’s Integration Cloud.

Using this feature, an API developer can add a callout policy inside an API proxy that invokes the required Informatica business integration process. This is especially useful when the business integration process needs to be invoked before the request gets routed to the configured backend target.

To use this feature, API developers:
  • Log in to Apigee Edge user interface with their credentials
  • Create a new API proxy, configure backend target, add policies
  • Add a callout policy to select the appropriate business integration process
  • Save and deploy the API proxy

Access Google Cloud services from the Apigee Edge user interface

API developers want to easily access and connect with Google Cloud services like Cloud Firestore, Cloud Pub/Sub, Cloud Storage, and Cloud Spanner. In each case, there are a few steps to perform to deal with security, data formats, request/response transformation, and even wire protocols for those systems.

Apigee Edge includes a new feature that simplifies interacting with these services and enables connectivity to them through a first-class policy interface that an API developer can simply pick from the policy palette and use. Once configured, these can be reused across all API proxies.

We’re working to expand this feature to cover more Google Cloud services. Simultaneously, we’re working with Informatica to include connections to other software-as-a-service (SaaS) applications and legacy services like hosted databases.

Publish business integration processes as managed APIs

Integration architects, working to connect data and applications across the enterprise, play an important role in packaging and publishing business integration processes as great API products. Working with Informatica, we’ve made this possible within Informatica’s Integration Cloud.

Integration architects that use Informatica's Integration Cloud for Apigee can now author composite services using business integration processes to orchestrate data services and applications, and directly publish them as managed APIs to Apigee Edge. This pattern is useful when the final destination of the API call is an Informatica business integration process.

To use this feature, integration architects need to execute the following steps:
  • Log in to their Informatica Integration Cloud user interface
  • Create a new business integration process or modify an existing one
  • Create a new service of type (“Apigee”), select options (policies) presented on the wizard, and publish the process as an API proxy
  • Apply additional policies to the generated API proxy by logging in to the Apigee Edge user interface.
API documentation can be generated and published on a developer portal, and the API endpoint can be shared with app developers and partners. APIs are an increasingly central part of organizations’ digital strategy. By working with Informatica, we hope to make APIs even more powerful and pervasive. Click here for more on our partnership with Informatica.

Last month today: GCP in June

In June, we had a lot to discuss about getting the most out of the cloud for your business, from speeding up web traffic to running fully managed apps easily. Here’s a quick look at some of the highlights from Google Cloud Platform (GCP) news this month.

What caught your attention this month

Some of the most-read stories this month reflected new technology developments or integrations that will be useful for developers and engineers.
  • You can now deploy your Node.js app to the Google App Engine standard environment—and based on readership, many of you are excited about this. Node.js works easily on App Engine, without any language, module or API restrictions. You’ll get very quick deployment times, and a fully managed experience once you’ve deployed those apps, just as in other apps on the fully managed App Engine.
  • QUIC is a transport protocol, optimized for HTTPS, that makes web traffic run faster. The protocol itself isn’t new, but last month we announced QUIC support for our HTTPS load balancers. Network performance is a huge part of a successful public cloud operation, so this new support could make a big impact on web page load times for your cloud services. Enabling QUIC means your connections can be established faster, which is especially useful for latency-prone connections, and clients who don’t yet support QUIC will seamlessly continue to use HTTPS.
  • If you’re a Kubernetes fan, you may have already explored the new kubemci command-line interface (CLI). It lets you configure ingress for multi-cluster Kubernetes Engine environments, using Cloud Load Balancer. It’s also the first step in a long-term solution that will consist of a multi-cluster ingress system controlled via kubectl CLI or Kubernetes API calls.

Hot topics

You can now run your GCP workloads in Finland to improve availability and reduce your latency in the Nordics, and we announced that the Los Angeles region will open next month.

We also added some new storage tools to your arsenal. We’re adding Cloud Filestore as a GCP storage option so you can run enterprise applications that need a file system interface and shared file system for data. It’s fully managed and offers high performance for applications that need low latency and high throughput. For those of you supporting and running creative industry applications on GCP infrastructure, Cloud Filestore works great for render farms, website hosting and content management systems.

In addition, the Transfer Appliance became generally available in June, allowing a type of cloud data migration that will work well if you’ve got more than 20TB of data to upload to GCP, or that would take more than a week to upload. In early use, Transfer Appliance customers have gotten quick starts on analytics projects by moving test data to GCP, along with moving backup data and some or all of a data center to GCP.

And in the “Cloud powers some very cool projects” category, take a look at how the new Dragon Ball Legends game creator built the backend on GCP. Bandai Namco Entertainment knew that players of the latest addition to their Dragon Ball Z franchise would want to play against one another in real-time, with players around the globe. They turned to GCP for the scalability, global reach and real-time analytics they needed to make that possible.

Behind the compute curtain

This news of sole-tenant nodes for Google Compute Engine will come in handy for those of you at companies that need dedicated cloud servers. With this option, it’s possible to launch new VM instances as usual, but on server capacity dedicated to you. This choice is nice for industries with strict compliance and regulatory rules around data, and for getting higher utilization from VM instances along with instance placement, done either manually or by Compute Engine.

Building applications on GCP involves some upfront choices for app developers: Which compute offering will you pick, and what language will you use? Whether you’re a fan of containers or VMs, containers, App Engine or Cloud Functions, you’ll find in this post some excellent concrete examples the time and effort involved in building a “Hello, World” app in each of GCP’s four compute platforms.

That’s a wrap for June. This month brings the Next ‘18 conference, July 24-26. Join us and thousands of other IT practitioners in San Francisco to learn all you need to know about building a modern cloud infrastructure. Till then, build away!

New GitHub repo: Using Firebase to add cloud-based features to games built on Unity

A while back, a group of us Google Cloud Platform Developer Programs Engineers teamed up with gaming fans in Firebase Engineering to work on an interesting project. We all love games, gamers, and game developers, and we wanted to support those developers with solutions that accomplish common tasks so they can focus more on what they do best: making great games.

The result was Firebase Unity Solutions. It’s an open-source github repository with sample projects and scripts. These projects utilize Firebase tools and services to help you add cloud-based features to your games being built on Unity.

Each feature will include all the required scripts, a demo scene, any custom editors to help you better understand and use the provided assets, and a tutorial to use as a step-by-step guide for incorporating the feature into your game.

The only requirements are a Unity project with the .NET 2.0 API level enabled, and a project created with the Firebase Console.

Introducing Firebase Leaderboard

Our debut project is the Firebase_Leaderboard, a set of scripts that utilize Firebase Realtime Database to create and manage a cross-platform high score leaderboard. With the LeaderboardController MonoBehaviour, you can retrieve any number of unique users’ top scores from any time frame. Want the top 5 scores from the last 24 hours? Done. How about the top 100 from last week? You got it.

Once a connection to Firebase is established, scores are retrieved automatically, including any new scores that come in while the controller is enabled.

If any of those parameters are modified (the number of scores to retrieve, or the start or end date), the scores are automatically refreshed. The content is always up-to-date!

private void Start() {
    this.leaderboard = FindObjectOfType();
    leaderboard.FirebaseInitialized += OnInitialized;
    leaderboard.TopScoresUpdated += UpdateScoreDisplay;
    leaderboard.UserScoreUpdated += UpdateUserScoreDisplay;
    leaderboard.ScoreAdded += ScoreAdded;

    MessageText.text = "Connecting to Leaderboard...";
With the same component, you can add new scores for current users as well, meaning a single script handles both read and write operations on the top score data.

public void AddScore(string userId, int score) {
    leaderboard.AddScore(userId, score);
For step-by-step instructions on incorporating this cross-platform leaderboard into your Unity game using Firebase Realtime Database, follow the instructions here. Or check out the Demo Scene to see a version of the leaderboard in action!

We want to hear from you

We have ideas for what features to add to this repository moving forward, but we want to hear from you, too! What game feature would you love to see implemented in Unity using Firebase tools? What cloud-based functionality would you like to be able to drop directly into your game? And how can we improve the Leaderboard, or other solutions as they are added? You can comment below, create feature requests and file bugs on the github repo, or join the discussion in this Google Group.

Let’s make great games together!

Bust a move with Transfer Appliance, now generally available in U.S.

As we celebrate the upcoming Los Angeles Google Cloud Platform (GCP) region in one of the creative centers of the world, we are excited to share news about a product that can help you get your data there as fast as possible. Google Transfer Appliance is now generally available in the U.S., with a few new features that will simplify moving data to Google Cloud Storage. Customers have been using Transfer Appliance for almost a year, and we’ve heard great feedback.

The Transfer Appliance is a high-capacity server that lets you transfer large amounts of data to GCP, quickly and securely. It’s recommended if you’re moving more than 20TB of data, or data that would take more than a week to upload.

You can now request a Transfer Appliance directly from your Google Cloud Platform console. Indicate the amount of data you’re looking to transfer, and our team will help you choose the version that is the best fit for your needs.

The service comes in two configurations: 100TB or 480TB of raw storage capacity. We see typical data compression rates of 2x the raw capacity. The 100TB model is priced at $300, plus express shipping (approximately $500); the 480TB model is priced at $1,800, plus shipping (approximately $900).

You can mount Transfer Appliance as an NFS volume, making it easy to drag and drop files, or rsync, from your current NAS to the appliance. This feature simplifies the transfer of file-based content to Cloud Storage, and helps our migration partners expedite the move for customers.
"SADA Systems provides expert cloud consultation and technical services, helping customers get the most out of their Google Cloud investment. We found Transfer Appliance helps us transition the customer to the cloud faster and more efficiently by providing a secure data transfer strategy."
-Simon Margolis, Director of Cloud Platform, SADA Systems
Transfer Appliance can also help you transition your backup workflow to the cloud quickly. To do that, move the bulk of your current backup data offline using Transfer Appliance, and then incrementally back up to GCP over the network from there. Partners like Commvault can help you do this.

With this release, you’ll also find a more visible end-to-end integrity check, so you can be confident that every bit was transferred as is, and have peace of mind in deleting source data.

Transfer Appliance in action

In developing Transfer Appliance, we built a device designed for the data center, so it slides into a standard 19” rack. That has been a positive experience for our early customers, even those with floating data centers (yes, actually floating--see below for more).

We’ve seen our customers successfully use Transfer Appliance for the following use cases:
  • Migrate your data center (or parts of it) to the cloud.
  • Kick-start your ML or analytics project by transferring test data and staging it quickly.
  • Move large archives of content like creative libraries, videos, images, regulatory or backup data to Cloud Storage.
  • Collect data from research bodies or data providers and move it to Google Cloud for analysis.
We’ve heard about lots of innovative, interesting data projects powered by Transfer Appliance. Here are a few of them.

One early adopter, Schmidt Ocean Institute, is a private non-profit foundation that combines advanced science with state-of-the-art technology to achieve lasting results in ocean research. Their goals are to catalyze sharing of information and to communicate this knowledge to audiences around the world. For example, the Schmidt Ocean Institute owns and operates research vessel Falkor, the first oceanographic research vessel with a high-performance cloud computing system installed onboard. Scientists run models and software and can plan missions in near-real time while at sea. With the state-of-the-art technologies onboard, scientists contribute scientific data to the oceanographic community at large, very quickly. Schmidt Ocean Institute uses Transfer Appliance to safely get the data back to shore and publicly available to the research community as fast as possible.

“We needed a way to simplify the manual and complex process of copying, transporting and mailing hard drives of research data, as well as making it available to the scientific community as quickly as possible. We are able to mount the Transfer Appliance onboard to store the large amounts of data that result from our research expeditions and easily transfer it to Google Cloud Storage post-cruise. Once the data is in Google Cloud Storage, it’s easy to disseminate research data quickly to the community.”
-Allison Miller, Research Program Manager, Schmidt Ocean Institute

Beatport, a division of LiveStyle, serves an audience of electronic music DJs, producers and their fans. Google Transfer Appliance afforded Beatport the opportunity to rethink their storage architecture in the cloud without affecting their customer-facing network in the process.

“DJs, music producers and fans all rely on Beatport as the home for the world’s electronic music. By moving our library to Google Cloud Storage, we can access our audio data with the advanced tools that Google Cloud Platform has to offer. Managing tens of millions of lossless quality files poses unique challenges. Migrating to the highly performant Cloud Storage puts our wealth of audio data instantly at the fingertips of our technology team. Transfer Appliance made that move easier for our team.”
-Jonathan Steffen, CIO, beatport
Eleven Inc. creates content, brand experiences and customer activation strategies for clients across the globe. Through years of work for their clients, Eleven built a large library of creative digital assets and wanted a way to cost-effectively store that data in the cloud. Facing ISP network constraints and a desire to free up space on their local asset server quickly, Eleven Inc. used Transfer Appliance to facilitate their migration.

“Working with Transfer Appliance was a smooth experience. Rack, capture and ship. And now that our creative library is in Google Cloud Storage, it's much easier to think about ways to more efficiently manage the data throughout its life-cycle.”
-Joe Mitchell, Director of Information Systems
amplified ai combines extensive IP industry experience with deep learning to offer instant patent intelligence to inventors and attorneys. This requires a lot of patent data for building models. Transfer Appliance helped amplified ai move TBs of this specialized essential data to the cloud quickly.

“My hands are already full building deep learning models on massive, disparate data without also needing to worry about physically moving data around. Transfer Appliance was easy to understand, easy to install, and made it easy to capture and transfer data. It just did what it was supposed to do and saved me time which, for a busy startup, is the most valuable asset.”
-Chris Grainger, Founder & CTO, amplified ai
Airbus Defence and Space Geo Inc. uses their exclusive access to radar and optical satellites to offer a stunning Earth observation images library. As part of a major cloud migration effort, Airbus moved hundreds of TBs of this data to the cloud with Transfer Appliance so they can better serve images to clients from Cloud Storage. They improved data quality along with the migration by using Transfer Appliance.

“We needed to liberate. To flex on demand and scale in the cloud, and unleash our creativity. Transfer Appliance was a catalyst for that. In addition to migrating an amount of data that would not have been possible over the network, this transfer gave us the opportunity to improve our storage in the process—to clean out the clutter.”
-Dave Wright, CTO, Airbus Defense and Space Geo Inc.

National Collegiate Sports Archives (NCSA) is the creator and owner of the VAULT, which contains years worth of college sports footage. NCSA digitizes archival sports footage from leading schools and delivers it via mobile, advertising and social media platforms. With a lot of precious footage to deliver to college sports fans around the globe, NCSA needed a way to move data into Google Cloud Platform quickly and with zero disruption for their users.

“With a huge archive of collegiate sports moments, we wanted to get that content into the cloud and do it in a way that provides value to the business. I was looking for a solution that would cost-effectively, simply and safely execute the transfer and let our teams focus on improving the experience for our users. Transfer Appliance made it simple to capture data in our data center and ship it to Google Cloud. ”
-Jody Smith, Technology Lead, NCSA

Tackle your data migration needs with Transfer Appliance

To get detailed information on Transfer Appliance, check out our documentation. And visit our Data Transfer page to learn more about our other cloud data transfer options.

We’re looking forward to bringing Transfer Appliance to regions outside of the U.S. in the coming months. But we need your help: Where should we deploy first? If you are interested in offline data transfer but not located in the U.S., please indicate so in the request form.

If you’re interested in learning more about cloud data migration strategies, check out this session at Next 2018 next month. For more information, and to register, visit the Next ‘18 website.

Google Cloud for Electronic Design Automation: new partners

A popular enterprise use case for Google Cloud is electronic design automation (EDA)—designing electronic systems such as integrated circuits and printed circuit boards. EDA workloads, like simulations and field solvers, can be incredibly computationally intensive. They may require a few thousand CPUs, sometimes even a few hundred thousand CPUs, but only for the duration of the run. Instead of building up massive server farms that are oversubscribed during peak times and sit idle for the rest of the time, you can use Google Cloud Platform (GCP) compute and storage resources to implement large-scale modeling and simulation grids.

Our partnerships with software and service providers make Google Cloud an even stronger platform for EDA. These solutions deliver elastic infrastructure and improved time-to-market for customers like eSilicon, as described here.

Scalable simulation capacity on GCP provided by Metrics Technologies (more details below)

This week at Design Automation Conference, we’re showcasing a first-of-its-kind implementation of EDA in the cloud: our implementation of the Synopsys VCS simulation solution for internal EDA workloads on Google Cloud, by the Google Hardware Engineering team. We also have several new partnerships to help you achieve operational and engineering excellence through cloud computing, including:

  • Metrics Technologies is the first EDA platform provider of cloud-based SystemVerilog simulation and verification management, accelerating the move of semiconductor verification workloads into the cloud. The Metrics Cloud Simulator and Verification Manager, a pay-by-the-minute software-as-a-service (SaaS) solution built entirely on GCP, improves resource utilization and engineering productivity, and can scale capacity with variable demand. Simulation resources are dynamically adjusted up or down by the minute without the need to purchase additional hardware or licenses, or manage disk space. You can find Metrics news and reviews at www.metrics/news.ca, or schedule a demo at DAC 2018 at www.metrics.ca.
  • Elastifile delivers enterprise-grade, scalable file storage on Google Cloud. Powered by a high-performance, POSIX-compliant distributed file system with integrated object tiering, Elastifile simplifies storage and data management for EDA workflows. Deployable in minutes via Google Cloud Launcher, Elastifile enables cloud-accelerated circuit design and verification, with no changes required to existing tools and scripts.
  • NetApp is a leading provider of high-performance storage solutions. NetApp is launching Cloud Volumes for Google Cloud Platform, which is currently available in Private Preview. With NetApp Cloud Volumes, GCP customers have access to a fully-managed, familiar file storage (NFS) service with a cloud native experience.
  • Quobyte provides a parallel, distributed, POSIX-compatible file system that runs on GCP and on-premises to provide petabytes of storage and millions of IOPS. As a distributed file system, Quobyte scales IOPS and throughput linearly with the number of nodes–avoiding the performance bottlenecks of clustered or single filer solutions. You can try Quobyte today on the Cloud Launcher Marketplace.
If you’d like to learn more about EDA offerings on Google Cloud, we encourage you to visit us at booth 1251 at DAC 2018. And if you’re interested in learning more about how our Hardware Engineering team’s used Synopsys VCS on Google Cloud for internal Google workloads, please stop by Design Infrastructure Alley on Tuesday for a talk by team members Richard Ho and Ravi Rajamani. Hope to see you there!