Data Studio, a free data visualization, and reporting product are now generally available to the public as on Thursday, September 20, along with Cloud Dataprep.
The program was in beta since 2016 and is now part of the recently formed Google Marketing Platform, and closely tied to Google Cloud.
According to Google, its mission is to empower people, whether advertising or marketing or business analyst to identify and share insights from their data.
The platform offers “seamless real-time collaboration” with others within the organization. It’s used by “millions of people to transform their data into powerful stories that surface key business insights,” says Google.
Data Studio has “rapidly evolved in the last twelve months” with stability and a slew of new key features released this past summer.
Most recently, the platform introduced new ways to experiment, combine, and share data visualizations using Explorer, Data Blending and the Report Gallery.
Here is a GIF animation of the Data Studio Explorer:
Google stresses that Data Studio’s usefulness comes from getting insights from large number of data sources without needing to write any code and visualizing data from several sources.
To this end, the company has created capabilities, such as community connectors, which enable connection to different data sources, including Google Cloud data sources and Google advertising and marketing data sources.
Google Cloud data sources, includes BigQuery, Sheets, Cloud SQL, Cloud Spanner, and Cloud Storage. While Google advertising and marketing data sources, include Google Analytics, Display & Video 360, YouTube Analytics, and Google Ads.
Additionally, the platform supports over five-hundred other data sources “both Google and non-Google.”
And, custom report templates enable customers to build custom reports and easily share solutions.
This should help marketers and other professions with creating and sharing comprehensive reports relatively quickly, which “inspire smarter business decisions”.
This is proven by the fact that in a short time span, “thousands of companies around the world, including leading brands, choose Data Studio for their cross-platform marketing reporting.”
The service is free to use by visiting datastudio.google.com.
Google Cloud Dataprep, a fully managed data preparation product powered by Trifacta, which leverages cloud data analytics tools like BigQuery and Google Cloud Dataflow launching generally today with a number of new features.
First up, a new look landing page for Cloud Dataprep with easy onboarding experience updated to show recent activity.
Here is a screenshot of the new look users interface (UI) of Cloud Dataprep:
Next up, team-based data preparation allows collaboration on data preparation. Teams can share flows, collaborate on recipes in real-time, re-using samples, and review an audit trail to see who did what and when.
Lastly, business analysts can now use Google Sheets-like pivots and unions, improved source target schema matching, and parameter-based dataset processing.
Refer the following hot to guides for easily getting started exploring, cleaning, and enriching data here.
Other products that reached the general availability today is “Cloud Memorystore for Redis.”
Cloud Memorystore, a fully managed in-memory data store service automates complex tasks, like provisioning, scaling, failover, and monitoring.
And, due to its compatibility with the open source Redis protocol, “you can migrate applications to GCP with zero code changes.”
Also, the service is now expanded to Tokyo, Singapore and Netherlands regions.
In the animation below you can see “create an instance”:
“Access Transparency,” a logs product that offers visibility into manual, targeted access to data is now generally available for following six Google Cloud Platform (GCP) services:
“Cloud Storage, Compute Engine, App Engine, Persistent Disk, Cloud IAM, and Cloud KMS. ”
The service introduces improvements in log quality, as well as the ability to view the employing entity in addition to the location of the data accessors, Google said.
You can view Access Transparency logs right alongside other critical information in Stackdriver Logging and export them into Cloud Storage, BigQuery and Cloud Pub/Sub for retention or further analysis.
“Cisco Hybrid Cloud Platform for Google Cloud” is also become generally available.
Here are some ways businesses can benefit::
- Accelerate on-prem app modernization using a Kubernetes-based container strategy that’s consistent with cloud-native technology, including GKE. Cisco will provide a turnkey solution that is cloud-ready for Kubernetes and containers, as well as management tools to enforce security and consumption policies.
- Ease services management. Istio’s open-source, container- and microservice-optimized technology offers developers a uniform way to connect, secure, manage and monitor microservices across clouds through service-to-service level mTLS access control.
- Quickly and more securely connect on-prem workloads to the cloud. API management through Apigee enables legacy workloads running on-prem to connect to the cloud through APIs. With Apigee, enterprises can expose legacy, on prem services as secure APIs to developers who can then easily incorporate these services into their modern application.
- Take advantage of integrated security and support. Customers can extend their existing Cisco security policies and monitoring to the cloud and be assured of joint coordinated technical support from Cisco and Google Cloud.
“Cloud Bigtable regional replication” now generally available provides a primary–primary replication between clusters in different zones within a single GCP region.
Using Cloud Bigtable regional replication, you can:
- Improve availability for both reads and writes with a multi-cluster routing policy, and increase availability SLA to 99.95%
- Isolate serving applications from batch analytics with single-cluster routing policies to offer each class of application with its own cluster
- Increase analytics throughput with an additional replica cluster, which can be scaled independently of the serving cluster
- Provide near-real-time backups in case of a zonal failure
The general availability of “Cloud Text-to-Speech, ” which also now offers multilingual access to DeepMind WaveNet voices and speaker optimization was announced.
Cloud Speech-to-Text now offers a wider feature-set and address needs around availability and reliability:
- Now supports 14 languages and variants, with 56 total voices including 30 standard voices, and 26 WaveNet voices.
- Audio Profiles (beta) let optimize Cloud Text-to-Speech for playback on different types of hardware.
- Separate different speakers with speaker diarization and multi-channel recognition
- Build apps that can accept multiple languages with language auto-detect
- Word-level confidence scores allow developers to build apps that can highlight specific words, and then depending on the score, write code to prompt users to repeat those words as needed.
In other related news, Google Cloud introduced its revamped code search feature “Cloud Source Repositories” in beta with a new user interface and semantic code search capabilities.
Google says it’s powered by the “same underlying code search infrastructure” that they internally use every day to do code searches.
And, since it uses “document indexing and retrieval technologies” used for Google Search, results are delivered faster whether “you host code in Cloud Source Repositories or mirror your code from the cloud versions of GitHub or Bitbucket,” says Google.
Below are some other services from Google Cloud Platform (GCP) that integrates with Cloud Source Repositories:
- Configuring Cloud Build to automatically deploy a new build to App Engine when a commit lands in a branch
- Using version control for Cloud Functions
- Inspecting the state of your applications stored in Cloud Source Repositories in real-time using Stackdriver Debugger
- Publishing commit events to Cloud Pub/Sub to integrate with any third-party tool of your choice
Cloud Source Repositories are a free to try at no cost with GCP via this free tier.
Following the success of 5-course specialization “Machine Learning with TensorFlow on Google Cloud Platform” in just 3–months since it’s launch in May, this year, Google announced a follow-on specialization dubbed, “Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization on Coursera.”
It consists of 5 courses:
- End-to-end machine learning with TensorFlow on Google Cloud Platform: A fast, fully hands-on recap of the key lessons in the first specialization.
- Production Machine Learning Systems: This course provides an in-depth exploration of the various considerations and patterns that underlie the design of such systems.
- Image Understanding with TensorFlow on Google Cloud Platform: This course has you building increasingly sophisticated image models.
- Sequence Models for Time Series and Natural Language Processing: Here, you’ll build recurrent neural networks and encoder-decoder models to solve machine learning problems on time series predictions and natural language problems such as text classification, text summarization, and question-answering.
- Recommendation Systems with TensorFlow on Google Cloud Platform: The last course of the specialization teaches you to build sophisticated models for personalization.
Google is offering one-month free access to this specialization on Coursera. Check it out over here.
Google Cloud also announced “Container Registry vulnerability scanning” in beta,
This feature is designed to “automatically detect known security vulnerabilities during the early stages of the CI/CD process and prevent the deployment of vulnerable images,” Google says.
Google says any container images built using Cloud Build are now automatically scanned for OS package vulnerabilities when images are pushed to Container Registry once the Container Analysis API is enabled.
In addition, Vulnerability scanning is also integrated with a deploy-time security control “Binary Authorization,” that ensures only trusted container images are deployed on Kubernetes Engine without any manual intervention.
Here is a working diagram of Vulnerability scanning:
Google notes, that the Container Registry vulnerability scanning findings will be available in Cloud Security Command Center alongside other security findings, including those from vendors such as Aqua or Twistlock.
Some benefits of vulnerability scanning, include:
- Perform deep security scans within CI/CD pipeline using a simple API call, the gcloud command line, or the Cloud Console UI.
- Address security early: Package vulnerabilities for Ubuntu, Debian, and Alpine are identified right during the application development process, “with support for CentOS and RHEL on the way.”
- Hook into an extensible architecture CI/CD tools using Pub/Sub notifications and Cloud Functions.
- Lock down production environments: By integrating Binary Authorization and Container Registry vulnerability scanning, you can gate deployments based on vulnerability scanning findings as part of the overall deploy policy.
- Get detailed insights such as severity, CVSS score, packages, and whether a fix is available.
For more details, check out Container Registry vulnerability scanning overview, and or watch this video:
In a post today, Google Cloud explained how “Ibis with BigQuery” provides an elegant and flexible Python interface for composing SQL queries.
Ibis, is a Python analytics library which provides the convenience of pandas’ APIs with the scalability of analytic SQL engines like BigQuery.
“It does this in a type-safe way, letting you build analytics expressions that compile to SQL and run on your favorite large-scale SQL engine. When you execute Ibis expressions, they turn into pandas DataFrames, which gives you access to the ecosystem of Python data libraries once you don’t need the scale that distributed SQL provides,” the team explained.
BigQuery and Ibis comprise an extremely powerful combination.
Another post explains how BigQuery Ethereum Public Dataset that contains the Ethereum blockchain data is built.
This includes the primary data structures—blocks, transactions—as well as high-value data derivatives—token transfers, smart contract method descriptions. Check it out over here.
Google Kubernetes Engine (GKE), which is designed to help run containerized enterprise machine learning (ML) workloads recently announced the general availability of NVIDIA Tesla V100 GPUs with NVLink on GKE.
This features, like the K80, P100 and P4 GPUs, speeds up many CUDA-powered compute and HPC workloads, without having to manage hardware or even VMs.
A step further, Google-designed Cloud TPUs are now publicly available in beta on GKE.
In addition, GKE also supports Preemptible Cloud TPUs that are priced 70% lower than the standard price of Cloud TPUs.
A new tutorial published shows how to use Apache Hive on Cloud Dataproc in an efficient and flexible way by storing Hive data in Cloud Storage and hosting the Hive metastore in a MySQL database on Cloud SQL.
Head over here to access the tutorial.
Other announced on GCP includes:
- General availability of Cloud Functions, that allows developing event-driven cloud apps quickly and flexibly without having to worry about the underlying infrastructure.
- Introduced App Engine Second Generation runtimes and Python 3.7.
- Alos, introduced headless Chrome support in Cloud Functions.
- Introduced Cloud HSM beta for hardware crypto key security, a new managed cloud-hosted hardware security module (HSM) service, lets you host encryption keys and perform cryptographic operations in a FIPS 140-2 Level 3 certified hardware boundary.
- A new container security feature: Binary Authorization, which helps ensure only trusted workloads are deployed to Kubernetes Engine.
- The capabilities of Velostrata, a cloud migration technology: Performing VM mass migrations to Google Cloud with Velostrata.