Cloud Services / Programming Languages / Serverless

This Week in Programming: Google Cloud’s Shock and Awe Campaign

12 Apr 2019 12:00pm, by

In discussing the pace of tech news this week, the phrase “shock and awe” came up and it stuck with me. Maybe it’s an insensitive metaphor to compare Google’s announcements this week at its Cloud Next 2019 conference to a U.S. military action meant to intimidate the opposition into submission, but it can feel a bit like an assault on the ol’ attention span and a barrage on the ability to keep pace when this many announcements come at once.

At the same time, many are quick to point out Google’s underdog status when it comes to enterprise cloud, with Amazon AWS and Microsoft Azure leading the pack, and so the analogy seems… appropriate. When the competition is ahead, you need to shock everyone with unilateral integrations that you won’t ever expect to be reciprocated and awe them with platform flexibility that says “we’ll even work with the competition to bring you to our side.” Over at TechCrunch, Ron Miller offers a take on this idea, writing that the Google Cloud Platform (GCP) is making some strong moves to differentiate itself from AWS and Microsoft by “selling themselves as the hybrid cloud company that can help with your digital transformation.”

And that’s what Google seems to be doing, with its onslaught of announcements, but fear not intrepid developer, as we hope to highlight some of the main announcements of interest for you, from the addition of open-source data management and analytics services to Google Cloud to its newest serverless offering to its latest cloud native coding tool. Without further ado…

Cloud Next 2019 for Developers

  • GCP Gets Data-Service Integrations: First up, Google announced that it is bringing in the best of open source to Google Cloud customers, with seven partnerships in the realm of data management and analytics, including Confluent, DataStax, Elastic, InfluxData, MongoDB, Neo4j, and Redis Labs. With these partnerships, Google will offer tightly integrated managed services into Google Cloud Platform (GCP), providing a single user interface and unified billing, as well as support. In a separate but similar announcement, Google also announced three new enterprise databases: a sneak preview of Cloud SQL for Microsoft SQL Server, CloudSQL for PostgreSQL with version 11 support, and multiregion replication for Cloud Bigtable.
  • Google Cloud Services Platform Goes GA as Anthos: Ever since Google introduced Google Cloud Services Platform a couple months ago, I’ve confused its name with GCP, so I am happy that the company has rebranded the service as “Anthos.” Renaming aside, the introduction of Anthos also means the general availability of Google’s hybrid Kubernetes platform, as well as the ability to manage workloads running on third-party clouds like AWS and Azure. This integration of other third-party cloud competitors, writes TechCrunch’s Frederic Lardinois, remains “highly unusual” and he notes that he doesn’t “think we’ll see AWS and Azure react with similar tools, but if they do, it’s a good thing for their customers.” In addition to all that, Anthos will also include an “expanded ecosystem of 30+ hardware, software and systems integration partners, including Cisco, Dell EMC, HPE, VMware, Intel, Dell, Lenovo, ATOS and Deloitte.”
  • Google Cloud Run: Now, we start to get to the meat of the week’s announcements, with Cloud Run, the newest member of Google’s serverless compute stack, which it says is meant to provide “the ease and velocity that comes with serverless or the flexibility and portability that comes with containers.” Cloud Run is in beta and lets developers focus on writing code without worrying about the underlying infrastructure. Based on Knative, an Open API and runtime environment that lets you run your serverless workloads anywhere you choose, Cloud Run “serverless workloads anywhere you choose” and “takes care of all infrastructure management including provisioning, configuring, scaling, and managing servers.” Cloud Run is available fully managed on GCP, on your GKE cluster, or your on-prem Kubernetes, and movable between. In addition, Google also announced new investments in Cloud Functions and second-generation runtimes in App Engine, including new language runtimes support such as Node.js 8, Python 3.7, and Go 1.11 in general availability, Node.js 10 in beta; Java 8 and Go 1.12 in alpha.

  • Cloud Code and Cloud Build FTW: Read through the comments on Hacker News on this next one and you’ll see many comments about attempts at vendor lock-in and focus on the fact that Google has released a closed source plug-in for an open source Microsoft IDE. Nonetheless, Google’s Cloud Code sounds like an interesting tool for developers working with cloud native applications. The tool is a plug-in for Microsoft Visual Studio Code and JetBeans IntelliJ IDEs that “gives developers local, continuous feedback into their project as they build it, extending this local edit-compile-debug loop to create cloud-native Kubernetes environments, on their local workstations or in the cloud” using Skaffold, Jib and Kubectl “under the hood.” Cloud Code also comes with easy integrations to numerous Google APIs (there’s the lock-in) and DevOps tools and services, such as Cloud Build and Stackdriver. “For example,” the announcement reads, “once your code is ready to deploy, simply do a pull request or commit, which triggers Cloud Build to automatically build, test, and deploy your application.” Another tool, an integrated library manager for IntelliJ, automatically adds required dependencies, enables the API, and manages required secrets. In addition to all that, Cloud Code and Cloud Build can work together to edit, review, test and change Kubernetes yaml files, with built-in templates, linting and error highlighting, as well as the ability to view application logs from your IDE. Finally, you can “define different deployment targets, like local development, shared development, test, or production, so you can easily test and debug on your workstation or in the cloud.”

  • And Of Course… The Artificial Intelligence and Machine Learning: What would any good slew of announcements be these days without the prerequisite AI and ML news? While some of the new tools are meant specifically for the business at large, Google writes that it is expanding its platform to make it easier for developers to build and deploy AI. The first of those announcements is the beta release of AI Platform, a “comprehensive, end-to-end development platform that helps teams prepare, build, run, and manage ML projects via the same shared interface” with model sharing, training, and workload scaling all managed from Cloud Console — and of course, like the other tools, it is meant to work in a hybrid environment, not just in the public cloud. Google also announced some updates to Cloud AutoML, including AutoML Tables, AutoML Vision, and AutoML Video to make it easier for businesses to build and deploy their own custom ML models.
  • Let’s Just Have Our Phones Talk It Out: One final bit of Google news, though I’m not even sure if this was part of Next or not, comes as the company announces ML Kit’s expansion into NLP with Language Identification and Smart Reply. Already, I wonder how many of my text message conversations are actually two AI-powered devices just going through the motions with each other and now, we’ll move one step closer to that reality across ALL our messaging applications with Language Identification and Smart Reply. Basically, these tools bring the ability to determine what language you’re writing in, and then offer “smart replies”, to any app, not just Google’s apps. The new Smart Reply API is a stateless API that runs on-device and provides suggested replies based on the last 10 messages in a conversation, although it still works if only one previous message is available. Google does note that at least it attempts to remain appropriate, as it has “added a model to detect sensitive topics, so that we avoid making suggestions in response to profanity or in cases of personal tragedy/hardship.” Currently, “ML Kit recognizes text in 110 different languages and typically only requires a few words to make an accurate determination.”

Feature image from Alex Williams.

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