Source: Last month today: GCP in June ‘19 from Google Cloud
As we bid farewell to June, we also say hello to a new partner and new product integrations, all with the goal of making Google Cloud Platform (GCP) ever more useful for your particular needs. Data analytics, in particular, continued to make leaps and bounds this month with new features and integrations. Here’s a look at last month’s top stories.
Data analytics, better together
In June, we announced our intent to acquire Looker, a unified platform for business intelligence, data application, and embedded analytics. Looker extends our business analytics offering with two important capabilities—first, the ability to define business metrics once in a consistent way across data sources. Second, Looker also provides users with a powerful analytics platform that delivers applications for business intelligence and use-case specific solutions such as sales analytics, as well as a flexible, embedded analytics product to collaborate on business decisions. We look forward to sharing more once the deal closes.
We also announced a partnership with data warehouse provider Snowflake, which will help users store and analyze data from a wide variety of sources. You’ll be able to use Snowflake along with our analytics and ML products, so you can store data in GCP, then analyze that data using Snowflake, with strong performance and reliability.
Using BigQuery for blockchain, and integrated with Kaggle
This post about building hybrid cloud/blockchain applications with Ethereum and GCP explains the use of BigQuery data inside of blockchain for applications like prediction marketplaces and transaction privacy. This post shows you how you can use smart contract platform Ethereum together with BigQuery through Chainlink middleware. This brings bidirectional operation between blockchain data and cloud services, adding efficiency and letting developers create new hybrid applications.
Also new in BigQuery this month: Kaggle is now integrated into BigQuery, so you can perform SQL queries, train ML models and then analyze that data using the Kaggle Kernels environment. With Kaggle Kernels and BigQuery, you can link your Google Cloud account, then compose queries directly in the notebook. You can also explore public datasets in BigQuery, and build and evaluate ML regression models without a lot of experience needed. Give it a try using the BigQuery sandbox.
Why cloud-native, and why it matters
The concept of cloud-native architecture has become popular as cloud computing has matured, and this post describes why cloud-native is fundamentally different from on-premises architecture. You’ll get a look at the principles of designing for the cloud, with some tips on improvements you can take advantage of, like more automation, managed services, and defense in depth.
School’s out, but certification is in
We announced a new Google Cloud certification challenge in June. Get certified within 12 weeks, and you’ll get a $100 Google Store voucher. It’s not too late to sign up; if you join the challenge, you can get access to Coursera and Qwiklabs resources for free. You can use those to study for either the Google Cloud Certified Associate Cloud Engineer or the Professional Cloud Architect exam.
There are also a few new Qwiklab quests to help you learn more about Kubernetes, specifically security and monitoring. The first of these self-paced labs covers migration and observability for containers, whether you’re running Kubernetes, Google Kubernetes Engine (GKE), or Anthos. The second focuses on securing Kubernetes apps, with labs on role-based access control, binary authorization, and more.
That’s a wrap for June. We’ll see you next month.