Google Cloud Platform Gains Serverless Features, More
Google is easing the development and deployment of applications on Google Cloud Platform (GCP). The company unveiled several GCP products, services and technologies at Google Cloud Next in San Francisco, last week. The lineup included…
1. An Expanded Version of App Engine
The company announced a major expansion of its App Engine serverless runtime environment.
The new release is centered around openness and developer choice, GCP Vice President of Cloud Platforms Brian Stevens wrote in a blog post. It also supports:
- Java 8.
- Python 2.7 or 3.5.
- Go 1.8.
- PHP 7.1. and .NET Core (both in beta).
App Engine provides developers with an open platform and a fully managed environment, Stevens stated. It is backed by a 99.95 percent service-level agreement (SLA).
2. Google Cloud Functions: Serverless Apps
The company launched Google Cloud Functions, a serverless environment to help developers build and connect cloud services without having to manage infrastructure.
Cloud Functions offers “a great way to build lightweight backends [and] extend the functionality of existing services,” Stevens noted. It enables developers to:
- Respond to file changes in Google Cloud Storage or incoming Google Cloud Pub/Sub messages.
- Complete lightweight data processing/ETL jobs.
- Provide a layer of logic to respond to webhooks emitted by any internet event.
Also, Cloud Functions enables mobile developers to build backends integrated with Firebase. It can handle events emitted from Firebase Realtime Database, Firebase Authentication and Firebase Analytics.
3. BigQuery Data Transfer Service
BigQuery Data Transfer Service automates data movement from select Google applications directly into BigQuery, GCP’s analytics data warehouse.
Business and marketing analysts can use the BigQuery Data Transfer Service to export data from Adwords, DoubleClick and YouTube into BigQuery, according to Stevens. By doing so, these analysts can make data available for instant analysis and visualization.
4. Google Cloud Dataprep
Google Cloud Dataprep is a serverless browser-based service designed to reduce the time it takes to prepare data for analysis. It creates a data pipeline in Google Cloud Dataflow, cleans the information and exports it to BigQuery or other destinations.
“[The service] intelligently connects to your data source, identifies data types, identifies anomalies and suggests data transformations,” Stevens pointed out. “In other words, you can now prepare structured and unstructured data for analysis with clicks, not code.”
Google Updates G Suite
The company also announced various G Suite updates at Google Cloud Next, including:
- Team Drives: Team Drives enable enterprise teams to manage permissions, ownership and file access for an organization.
- Hangouts Meet and Chat: Hangouts Meet allows up to 30 people to join a video meeting without browser plugins or downloads. Meanwhile, Hangouts Chat empowers enterprise team members to embed content into virtual conversations.
- @meet: @meet provides a machine learning-powered bot that uses natural language to schedule meetings.
The G Suite updates meet the security, compliance and connectivity standards of today’s enterprises, Google Cloud Vice President of Apps Prabhakar Raghavan wrote in a blog post. Raghavan also pointed out the G Suite updates highlight the potential of machine learning technologies for enterprises.
Google Cloud, SAP HANA Launch Partnership
Multiple Google Cloud partnerships and products surfaced at Google Cloud Next, including a Google Cloud-SAP HANA agreement to develop and integrate Google’s cloud and machine learning solutions with SAP enterprise applications.
In addition, Google Cloud unveiled Google Engineering Support for GCP, a subscription-based model to replace the tiers that linked support costs directly to cloud usage. The company said it plans to roll out Engineering Support for GCP this spring.
Overall, the launches are designed to help the company compete more effectively against Amazon Web Services and Microsoft Azure, particularly in terms of big data and machine learning.