So, you want to store objects on the cloud? But you’re really new to Google Cloud or Cloud Storage and would like someone to walk you through the process step by step?

Today is your lucky day! 

I will help you understand the big steps involved in setting up and using Cloud Storage. For a walkthrough of all the steps involved that even includes highlighting of things to select so you don’t spend all your time playing “Where’s Waldo” on the page looking for that one button, click try the tutorial!

Creating buckets, storing objects in them, and adjusting permissions may feel challenging at first, but after you’ve walked through these steps, you’ll be able to create and use the Cloud Storage your project needs.

Resource Hierarchy

Projects contain all of the related parts of your application. Inside projects, you may have buckets, the top level containers in Cloud Storage. Files and folders are stored in buckets. You can grant access to a bucket, folder, or file using Identity and Access Management.

You can do all of that in this interactive tutorial. So let’s get started. I suggest you follow along in the tutorial so you can see the specific keys and screens as I describe them.

Creating a project

Everything in your application will be in a project, even if the only service you are using is storage. Projects need to be associated with a billing account, so if you don’t have one, consider signing up for the Free Trial. You’ll start in the Cloud Console.

Creating a bucket

Once you have a project, you can use storage by navigating to the Cloud Storage page by using either the Navigation menu at the top left of the console or by searching for “Cloud Storage” using the search box at the top of the console.

At the Buckets page of Cloud Storage, you’ll be able to create a bucket. The name of your bucket needs to be globally unique; that is no other bucket in Google Cloud Storage can have the same name. This is because if you or your organization allow your bucket to be accessible on the Internet, it will be at the URL https://storage.googleapis.com/<bucket name>. You can also consider implementing a security feature to prevent data exfiltration, organization restriction headers when you’re finished creating your bucket.. Pick a region close to you for your bucket. In some cases it may make sense to use multi-region or dual-region, but the details are more than I want to get into here.  Don’t worry; you can find lots more information on them! Additionally, I won’t go into the details about selecting the appropriate storage class based on access frequency and longevity, but more information can be found here as well our recently introduced Autoclass tiering feature..

Since buckets can be accessed from the Internet, you need to be careful to only make the things you want to be public, public. The default value is to keep your data off of the Internet, so when you create the bucket, there’s an option to “Enforce public access prevention on this bucket” that is already selected. Since we’ll be making items in this bucket public later on, clear the option before confirming the bucket information. Don’t worry; you’ll see how to set that option back later.

Take a look at the Bucket detail page. There are tabs to see the objects in the bucket, the permissions on the bucket, and much more. There are commands to create folders and to upload files and folders.  We’ll do some of this in the next section. You can even find out much more from the Learn option in the upper right.

Adding items to the bucket

Find a file you’d like to upload to this bucket, remembering it will be made public to the Internet later. If you don’t have anything available, there’s a picture of a cat (of course, it’s a cat picture, this is the Internet!) in the interactive tutorial.

Go to the bucket details for your bucket. Using the “Upload files” button, you’ll just upload the file to the bucket by selecting the file from your computer.   We won’t get into service classes here, but when you’re ready, there are links in the references.

Of course, if you have a lot of files in a bucket, you probably will want some sort of organization. You can do this by creating folders in your bucket with the “Create Folder” option. Once you’ve done that, you can go to the overflow menu (three vertical dots at the end line with the object information) and select Move. Remember, if you need help finding this, the interactive tutorial will point it out. 

Making a bucket public 

No surprise, the Bucket details hold lots of information about your bucket. You’ll change the bucket’s permissions in the Permission tab. (I hope that’s not a surprise.) You can then give access to individuals, groups, or all users. For this tutorial, follow the steps to grant access to allUsers. There are a variety of different levels of access you can grant to a user. In this case, just grant the ability to view the object using Storage Object Viewer.

Then go into the Objects tab to find the URL for the object you uploaded. Copy that URL and ensure you can indeed access the object from another tab or incognito window.

Now that you’ve done that, go back to the Permission tab for the bucket and select Prevent Public Access to secure the bucket again.

So, in a nutshell, to make all files in a bucket public, in the Permissions tab:

• Uncheck the option “Remove Public Access Prevention”
• Use Grant Access to give allUsers the Storage Object Viewer role in Cloud Storage.

To make all files in a bucket private, in the Permissions tab select Prevent Public Access.

Cleaning up

There is a charge to keep items in Cloud Storage, so you’ll probably want to delete the object you uploaded by selecting it and pressing Delete. You can delete the bucket in a similar way. The tutorial has details, of course.

While there are lots more details about Cloud Storage, you’ve got the big picture now. Go do something creative and useful with it!

So what now?

If you haven’t walked through the interactive tutorial, give it a try!

The last few years have underscored the importance of speed in bringing new drugs and medical devices to market, while ensuring safety and efficacy. Over this time, healthcare and life sciences organizations have transformed the way they research, develop, and deliver patient care by embracing agility and innovation.

Now, the industry is set to reap the benefits of cloud technology and overcome the existing barriers to innovation.

What’s holding back innovation?

Costly clinical trials: The process of trialing and developing new drugs and devices is still long and costly, with more than 1 in 5 clinical trials failing due to a lack of funding.1 The high failure rate comes as no surprise when you consider the average clinical trial costs $19 million and takes 10-15 years (through all 3 phases) to be approved.2

Stringent security requirements: Pre-clinical R&D and clinical trials use large volumes of highly sensitive patient data – making the life sciences industry one of the top sectors targeted by hackers.3 On top of this, the FDA and other regulatory bodies have strict requirements for medical device cybersecurity.

Unpredictable supply chains: Global supply chains are becoming increasingly complex and unpredictable. This can be brought on by anything from supply shortages, to geo-political events, and even bad weather. Making things worse is the lack of visibility into medical shipment disruptions – so when disaster strikes you’re often caught off guard.

Google Cloud for life sciences

At Alphabet, we’ve made significant investments in healthcare and life sciences, helping to tackle the world’s biggest healthcare problems, from chronic disease management, to precision medicine, to protein folding.

Together with Google, you can transform your life sciences organization and deliver secure, data-driven innovation across the value chain.

• Accelerate clinical trials to deliver life-saving treatments faster and at less cost. Clinical trials require relevant and equitable patient cohorts that can produce clinically valid data. Solutions like DocAI can enable optimal patient matching for clinical trials, helping organizations optimize clinical trial selection and increase time to value. How that patient data is collected is also important. Collection in a physician’s office captures a snapshot of the participant’s data at one point in time and doesn’t necessarily account for daily lifestyle variables. Fitbit, used in more than 1,500 published studies–more than any other wearable device–can enrich clinical trial endpoints with new insights from longitudinal lifestyle data, which can help improve patient retention and compliance with study protocols. We have introduced Device Connect for Fitbit, which empowers healthcare and life sciences enterprises with accelerated analytics and insights to help people live healthier lives. We are able to empower organizations to improve clinical trials in key ways:

1. Enable clinical trial managers to quickly create and launch mobile and web RWE collection mechanism for patient reported outcomes
2. Enable privacy controls with Cloud Healthcare Consent API and, as needed, remove PHI using Cloud Healthcare De-identification API
3. Ingest RWE and data into BigQuery for analysis
4. Leverage Looker to enable quick visualization and powerful analysis of a study’s progress and results

• Ensure security and privacy for a safe, coordinated, and compliant approach to digital transformation. Google Cloud offers customers a comprehensive set of services including pioneering capabilities such as BeyondCorp Enterprise for Zero Trust and VirusTotal for malicious content and software vulnerabilities; Chronicle’s security analytics and automation coupled with services such as Security Command Center to help organizations detect and protect themselves from cyber threats; as well as expertise from Google Cloud’s Cybersecurity Action Team. Google Cloud also recently acquired Mandiant, a leader in dynamic cyber defense, threat intelligence and incident response services.

• Optimize supply chains and enhance your data to prepare for the unpredictable. With a digital supply chain platform, we can empower supply chain professionals to solve problems in real time including visibility and advanced analytics, alert-based event management, collaboration between teams and partners, and AI-driven optimization and simulation.

Ready to learn more? We’ll be taking a deep dive into each of the challenges outlined above in our life sciences video series. Stay tuned.

1. National Library of Medicine
2. How much does a clinical trial cost?
3. Life Sciences Industry Becomes Latest Arena in Hackers’ Digital Warfare

Editor’s note: To launch a new mobile payment platform, Mercari needed a database solution strong on scalability, availability, and performance. Here’s how Cloud Spanner delivered those results. 

E-commerce companies need to connect customers to their services securely, reliably, with zero downtime. When Mercari, Inc. launched a new mobile payment platform, we chose Cloud Spanner as part of our data portfolio, which provided us with easy scalability to handle millions of new users, a fully managed service that minimized overhead costs, and deep integration with other Google Cloud services. 

Mercari, Inc., Japan’s largest C2C marketplace, launched its app in 2013, which allows 18.2million monthly users to easily and securely buy new and used items. Mercari expanded into the United States and in 2014 and launched Merpay, a mobile payment service that can be used through Mercari in Japan in 2017. With more than 85 million users, Merpay is now accepted at 1.8 million merchants and e-commerce sites in Japan, supporting payments via the DOCOMO ID contactless system and QR code.

Prioritizing availability and scalability

When we started building Merpay, we were looking for a new database. In the past, Mercari had used MySQL with bare metal hardware. Because of the amount of data, we required additional expertise to manage and maintain the hardware, software and MySQL implementation. Having built much of the microservices architecture for our Mercari app using Google Kubernetes Engine (GKE), it was natural to look at Google Cloud’s managed services when deciding upon our new database infrastructure for Merpay. 

For the database, we were focusing especially on requirements around availability, scalability, and performance. To do a single payment transaction, there were multiple steps each with writes, requiring high-write-throughput and low-latency from the database. Needing a solution that would support reliable payment processing 24/7/365, we chose Spanner, which offers up to 99.999% availability with zero downtime for planned maintenance and schema changes. 

We worked closely with Google Cloud’s Premium Support, Technical Account Management (TAM), and Strategic Cloud Engineer and Cloud Consultant teams to implement Spanner, including separating the payment processing— originally one of the functions in Mercari—as a microservice.

A nod to easier nodes and better scale

After launch, the number of Merpay users reached 2 million in only a few months. We had 45 Spanner nodes at the time of launch of Merpay and about 50 nodes four months later. Because we’d optimized the application side during those four months, we didn’t have to add many nodes to keep up with the growing traffic. 

Whenever we needed to scale up the serving and storage resources in our instances, Spanner made it easy to increase nodes as needed in the Cloud Console. To optimize costs, it’s easy to add nodes during a marketing campaign and remove them afterward. Even if the traffic count is different from expected, we can change the number of nodes immediately. That’s one incredible convenience of Cloud Spanner.

Building powerful pipelines

Our data pipelines are used for KPI analytics, fraud detection, credit scoring, and customer support use cases. Because Cloud Spanner integrates so easily with other Google Cloud data services, the data in Spanner is easily accessible for analytics. From the Spanner database for each microservice, we create both batch and streaming data pipelines using Pub/Sub and Dataflow, as well as Apache Flink, and load the data into BigQuery and Google Cloud Storage. We’re able to quickly aggregate the data required for data platforms such as BigQuery and Cloud Storage. The original data is stored in Spanner and managed by microservices, but analysis through BigQuery is centrally managed by the Data Platform team. This team determines the confidentiality level of data and centrally manages BigQuery permissions using Terraform. By centrally managing data access permissions on the data platform rather than on individual microservices, we’re able to set appropriate security for individual users.

Our full data portfolio also includes Looker, which we use for product analysis, accounting analysis, test performance visualization, operation monitoring, development efficiency analysis, and HR analysis. We also use Dataproc, Cloud Composer, Data Catalog, and Data Studio. The Dataflow template created for the pipeline is also published as OSS. 

With Spanner and other Google Cloud services, our Merpay platform is flexible, secure, scalable and highly available. As Spanner eliminates overhead, we can devote engineering resources to developing new tools and solutions for our customers. We’re looking ahead at providing cryptocurrency service, for example, and are now working on a project to migrate Mercari’s monolithic system that is still on premises entirely over to Google Cloud.

Read more about Mercari and Merpay. Or check out our recent blog: three reasons to consider Cloud Spanner for your next project.

Customers tell us that sharing and exchanging data with other organizations is a critical element of their analytics strategy, but it’s hamstrung by unreliable data and processes, and only getting harder with security threats and privacy regulations on the rise. 

Furthermore, traditional data sharing techniques use batch data pipelines that are expensive to run, create late arriving data, and can break with any changes to the source data. They also create multiple copies of data, which brings unnecessary costs and can bypass data governance processes. These techniques do not offer features for data monetization, such as managing subscriptions and entitlements. Altogether, these challenges mean that organizations are unable to realize the full potential of transforming their business with shared data.

To address these limitations, we are introducing Analytics Hub, a new fully managed service, available in Q3, in preview, that helps you unlock the value of data sharing, leading to new insights and increased business value. With Analytics Hub you get:

• A rich data ecosystem by publishing and subscribing to analytics-ready datasets. 
• Control and monitoring over how your data is being used, because data is shared in one place.
• A self-service way to access valuable and trusted data assets, including data provided by Google. For example, a unique dataset from Google Search Trends will be available, that you can query and combine with your own data.
• An easy way to monetize your data assets without the overhead of building and managing the infrastructure. 

Built on a decade of cross-organizational sharing

While Analytics Hub is a new service, it builds on BigQuery, Google’s petabyte-scale, serverless cloud data warehouse. BigQuery’s unique architecture provides separation between compute and storage, enabling data publishers to share data with as many subscribers as you want without having to make multiple copies of your data. With BigQuery, there are no servers to deploy or manage, which means that data consumers get immediate value from shared data. Data can be provided and consumed in real-time using the streaming capabilities of BigQuery and you can leverage the built in machine learning, geospatial, and natural language capabilities of BigQuery or take advantage of the native business intelligence support with tools like Looker, Google Sheets, and Data Studio.

BigQuery has had cross-organizational, in-place data sharing capabilities since it was introduced in 2010. We took a look at usage metrics in BigQuery and found that over a 7 day period in April, we had over 3,000 different organizations sharing over 200 petabytes of data. These numbers don’t include data sharing between departments within the same organization.

As you can see, data sharing in BigQuery is already popular. But we want to make it easier and even more scalable.

Raising the bar on data sharing 

To make data sharing easier and more scalable in BigQuery, Analytics Hub introduces the  concepts of shared datasets and exchanges. As a data publisher, you create shared datasets that contain the views of data that you want to deliver to your subscribers. Next, you create exchanges, which are used to organize and secure shared datasets. By default, exchanges are completely private, which means that only the users and groups that you give access to can view or subscribe to the data. You can also create internal exchanges or leverage public exchanges provided by Google. Finally, you publish shared datasets into an exchange to make them available to subscribers. 

Data subscribers search through the datasets that are available across all exchanges for which they have access and subscribe to relevant datasets. This creates a linked dataset in their project that they can query and join with their own data. Subscribers pay for the queries that they run against the data while the publisher pays for the storage of the data. Data providers can add new data, new tables, or new columns to the shared dataset and these will be immediately available to subscribers. In addition, the publisher can track subscribers, disable subscriptions, and see aggregated usage information for the shared data. 

Analytics Hub makes it easy for you to publish, discover, and subscribe to valuable datasets that you can combine with your own data to derive unique insights. Here are some types of data that will be available through Analytics Hub:

• Public datasets: Easy access to the existing repository of over 200 public datasets, including data about weather and climate, cryptocurrency, healthcare and life sciences, and transportation. 
• Google datasets: Unique, freely-available datasets from Google. One example of this is the COVID-19 community mobility dataset. Another example is the forthcoming Google Trends dataset, which will provide the top 25 search terms and top 25 rising search terms over a 5 year window in 210 distinct locations in the US. Trends data can be used by everyone in the organization to gain insights into what customers care about.
• Commercial (paid for) datasets: We are working with leading commercial data providers to bring their data products to Analytics Hub. If you are interested in delivering your data via Analytics Hub, we’re also introducing Data Gravity, an initiative that provides storage benefits and new distribution paths for data published through Analytics Hub. 
• Internal datasets: We know that data sharing can be challenging in larger organizations. Analytics Hub can be used for internal data, for example, to share standardized customer demographics with your sales engineering and data science teams.

Customers and partners using Analytics Hub

“Google Search Trends data has always been an important tool for our WPP agency data teams. At WPP we believe that data variety is a superpower which is why we are excited to use the new Trends dataset availability within BigQuery, plus the launch of Analytics Hub. The best creativity in the world is informed by data insights, and influenced by what people search for, so the operational efficiencies we’ll gain via the Analytics Hub and the insights we can drive with Trends data are just phenomenal.”
—Di Mayze Global Head of Data and AI, WPP

“Equifax Ignite is our shared data analytics environment within our Equifax data fabric. We are excited to partner with Google to leverage Analytics Hub and BigQuery to deliver data to over 400 statisticians and data modelers as well as securely sharing data with our partner financial institutions.”
—Kumar Menon, SVP Data Fabric and Decision Science, Equifax

“The flow of data and insights between our teams at Deloitte and our clients is paramount for building truly transformational data cultures. With its purpose-built architecture for secure data exchanges and sharing analytics resources, Google Cloud’s Analytics Hub can help provide significant operational efficiencies for how Deloitte teams support our clients’ data-driven initiatives within their industry ecosystems. It will also help minimize the worries about scale, privacy and security, or the administrative burden associated with each.”
—Navin Warerkar, Managing Director, Deloitte Consulting LLP, and US Google Cloud Data & Analytics GTM Lead

“Crux Informatics is proud to partner with Google to support the launch of Analytics Hub, removing friction for those who need access to analytics-ready data. With thousands of datasets from over 140 sources, Crux Informatics will accelerate access to data on Analytics Hub and together provide a more efficient and cost effective solution to deliver datasets in Google Cloud’s ecosystem.”
—Will Freiberg, CEO, Crux Informatics

Next steps for Analytics Hub

This is just the beginning for Analytics Hub. As we get to preview and general availability, we will be adding additional capabilities, including workflows for publishing and subscribing, publishing analytics assets (Looker Blocks, Data Studio reports, Connected Google Sheets) along with the shared data, the ability for data publishers to specify query restrictions on the usage of their data, and making it easy for data publishers to create sandbox environments for subscribers to work with their data, even if they are not yet on Google Cloud. We will provide features in Analytics Hub for monetization of data, including managing subscriptions, data entitlements, and billing.

Please sign up for the preview, which is scheduled to be available in the third quarter of 2021. In the meantime, you can learn more about BigQuery and how to leverage its built-in data sharing capabilities. Please go to g.co/cloud/analytics-hub to register your interest in Analytics Hub.