Google Cloud Platform Newsletter

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A new book from Google about "Building Secure and Reliable Systems" was released, to you can take advantage of probable quarantine for reading and of course this newsletter :). 

News

New AI-driven features in Dataprep enhance the wrangling experience - Check out new AI-powered features in Cloud Dataprep from Trifacta to make your data wrangling experience easier.

Learn to build secure and reliable systems with a new book from Google - Engineers across Google's security and SRE organizations share best practices to help you design scalable and reliable systems that are fundamentally secure.

Same Cloud Bigtable database, now for smaller workloads - NoSQL cloud database service now for smaller workloads can handle either one or two nodes, with the same SLA, from Google Cloud.

Improved database performance data: Key Visualizer now in Cloud Bigtable console - Cloud database service monitoring now easier with data visualization -- Cloud Bigtable from Google Cloud.

Machine learning with XGBoost gets faster with Dataproc on GPUs - Machine learning workloads can move a lot faster when run on GPUs vs. CPUs. See how to do it with NVIDIA, XGBoost and Dataproc for ML model building.

Articles, Tutorials

Infrastructure, Networking, Security, Kubernetes

Automate Security on GCP with Event Threat Detection - Leverage GCP Cloud Functions and Event Threat Detection to automate your cloud security response.

Deploying Your Applications Using Codefresh, Google Cloud Platform, and Google Kubernetes Engine - An example of using Codefresh for automating the process of getting your code built, tested, and deployed on Google Kubernetes Engine.

How to Terraform With Jenkins and Slack on Google’s Cloud Platform - Integrating Terraform deployments with Jenkins and Slack on Google Cloud.

Protection from Container Malware with Anthos - Examing recent malware incidents and how Anthos GKE offers several security features that can be used to protect enterprises from such threats.

Dry run mode for Service Perimeters - In dry run mode, requests that violate the perimeter policy are not denied, only logged. Dry run service perimeters are used to test perimeter configuration and to monitor usage of services without preventing access to resources.

VPC accessible services - To define the services that can be accessed from a network inside your service perimeter, use the VPC accessible services feature. The VPC accessible services feature limits the set of services that are accessible from network endpoints inside your service perimeter.

App Development, Serverless, Databases, DevOps

API design: Understanding gRPC, OpenAPI and REST and when to use them - When designing a modern API, learn when to use RPC (gRPC), OpenAPI or REST.

Converting Audio Recorded Speech to Text using Google’s Speech-to-Text API, Drive, Cloud Storage, and Colaboratory - Code samples in Python to do Speech to Text conversion.

Firebase Authentication with Firestore database - How to authenticate a user using Firebase Authentication and create the corresponding document in Cloud Firestore.

Host a Static Website on GCP With Loadbalancer and CDN - The article demonstrates how to set up a static website in Cloud Storage with Load Balancer and CDN.

Your private PDF merge service - This post walks you through packaging an efficient Linux command to merge PDF files into a web app and hosting it on Cloud Run.

A Healthy Dialogflow Part II: Development, Deployment, and Analysis - Developing Cloud Function for Dialogflow chatbot.

How to deploy ASP.NET Core 3.x to Google App Engine - Detailed article about deploying ASP.NET Core 3 web application on App Engine Flex.

Make uploads fail-proof in Cloud Storage - The article explains how to utilize resumable uploads with custom URLs.

Cloud Spanner Emulator - A local, in-memory, high-fidelity emulator of the Cloud Spanner service.

Big Data, Analytics, ML&AI

How to run a Java 11 Spark Job on Google Cloud Dataproc - This tutorial shows how to set up Google Cloud Dataproc Spark jobs to run software compiled in Java 11.

Ibis: A Python Data Analysis Framework for Development and Production - An example of using Ibis (Python Data Analysis Productivity Framework) with BigQuery.

How do I move data from MySQL to BigQuery? - See how to perform MySQL data migration to cloud with this change data capture (CDC) example. This helps move data into cloud data warehouse BigQuery.

Migrating Hive ACID tables to BigQuery - This blog post explores the impact the Hive transactional tables have on the migration process to BigQuery and identifies the steps to take to avoid them.

BigQuery Materialized Views and Why You Should be Using Them - TL;DR BigQuery materialized views are great. You should use them!

BigQuery dbt: modern problems require modern solutions - The article explains how dbt tool can be used to easy BigQuery workflows, deployments etc.

Creating, Hosting & Inferencing Machine Learning Model using Google Cloud Platform AutoML - End to end example of using Cloud AutoML.

AI Platform Optimizer - AI Platform Optimizer is a black-box optimization service that helps you tune hyperparameters in complex machine learning models.

Developing interactively with Apache Beam notebooks - Using the Apache Beam interactive runner with JupyterLab notebooks lets you iteratively develop pipelines, inspect your pipeline graph, and parse individual PCollections in a read-eval-print-loop (REPL) workflow.

Various

How Thomas Kurian’s ‘Quite Simple’ Strategy Is Transforming Google Cloud - Thomas Kurian has upped Google’s cloud game with hot new technology, a razor-sharp vertical market focus and big partner investments.

Taking Google Cloud Professional Data Engineer Certification in 2020 - Tips to pass the Data Engineer certification exam.

Google Cloud Platform Custom Model Upload , REST API Inference and Model Version Monitoring - End to end example of using Cloud AutoML.

Slides, Videos, Audio

GCP Podcast - #215 SAP with Thomas Jung and Lucia Subatin.

Kubernetes Podcast - #98 Cassandra, with Sam Ramji.

Apache Airflow: Leverage Google Cloud to Build Highly Scalable Airflow Celery Infra

Releases

AppEngine Standard Python - Updated Python SDK to version 1.9.90.

BigQuery - BigQuery Reservations is now Generally Available (GA). Scheduling queries no longer requires the bigquery.transfers.update permission. BigQuery materialized views are now available as a beta release.

BigTable - Key Visualizer for Cloud Bigtable is now integrated into the Google Cloud Console.

CDN - TLS v1.3 is now enabled by default for all external HTTPS load balancers, SSL proxy load balancers, and Cloud CDN.

Cloud Composer - Private IP Composer environments are now generally available (GA). Support for Shared VPC networks is now generally available (GA).

Config Connector - Add the CloudBuildTrigger resource Add the SourceRepoRepository resource. miscellaneous bug fixes and improvements.

Cloud Dataflow - Dataflow now provides beta support for Flex Templates. Dataflow now provides beta support for Interactive Notebooks. Dataflow now supports Dataflow Shuffle, Streaming Engine, FlexRS, and the following regional endpoints in GA: us-east4 (Northern Virginia) europe-west2 (London) europe-west3 (Frankfurt).

Cloud Healthcare API - It is now possible to export resource changes to BigQuery each time a FHIR resource is created, updated, patched, or deleted in a FHIR store, using the new field for a store, streamConfigs. You can now configure the HL7v2 store with multiple Pub/Sub topics and use filters to send notifications to different Pub/Sub topics. General availability release Cloud Healthcare API is generally available with release version v1.

Google Kubernetes Engine - Ingress access logging is now a configurable feature called `logging` in versions 1.16.8-gke.10 and later. HTTP access logging for newly created Ingress resources is being deprecated across various GKE versions on May 12th, 2020. Google Kubernetes Engine will gradually upgrade clusters in the regular channel to GKE 1.16 beginning on or after April 13, 2020. GKE cluster versions have been updated as detailed in the following sections. The following Kubernetes versions are now available for new clusters and for opt-in master upgrades and node upgrades for existing clusters. No channel 1.15.11-gke.5 is now available. Due to the recent Windows Server security update provided by Microsoft in February 2020, a container incompatibility issue was introduced. As a consequence of Microsoft's security update, your workloads may end up in a failed state due to broken compatibility if the host Windows Server image has the security update and the container base image does not have the update. We have turned off auto-upgrade on the impacted GKE clusters to prevent this compatibility issue from affecting your workloads. The security update will be available in the rapid channel in GKE starting April 6, 2020. We strongly recommend you to rebuild your container images with the base Windows images that include Windows Updates from March 2020, then manually upgrade your node pool to the latest GKE version. Incompatibility issues such as this one are a rare occurrence as it is against Microsoft's typical guidance for the security updates. If you have any questions or require assistance, please email us or contact Google Cloud Support.

Google Kubernetes Engine Rapid - 1.16.8-gke.8 is now available in the Rapid release channel.

Load Balancing - Backend services documentation is updated through the Cloud Load Balancing doc set. TLS v1.3 is now enabled by default for all external HTTPS load balancers, SSL proxy load balancers, and Cloud CDN.

Resource Manager - The Organization Policy Service resource locations constraint has launched for general availability.

Security Command Center - Security Health Analytics is now in general availability.

AI Platform Prediction - If you deploy a model version for online prediction that uses runtime version 2.1 with a GPU, AI Platform Prediction now correctly uses TensorFlow 2.1.0 to serve predictions.

AI Platform Training - You can now specify virtual machine instances with the evaluator task type as part of your training cluster for distributed training jobs. The maximum running time for training jobs now defaults to seven days. Runtime version 2.1 now includes scikit-learn 0.22.1 instead of scikit-learn 0.22.