Editor’s note: The video platform Vimeo leverages managed database services from Google Cloud to serve up billions of views around the world each day. Here’s how they deliver a consistent user experience across all their content.

Vimeo is the world’s leading video software solution, powering hundreds of thousands of new uploads every day and billions of views. At our scale, it’s paramount that we retain a seamless user experience across all of the videos hosted, shared, and viewed on our platform. With managed database services from Google Cloud, we’ve been able to deliver a consistent and reliable user experience no matter where our users and their audiences are.

https://youtube.com/watch?v=ggpAUgEL2iE%3Fenablejsapi%3D1%26

Outgrowing the data center and gaining more scale

Our first exposure to Google Cloud was the excellent performance between Google Compute Engine (GCE) and Google Cloud Storage (GCS), which we used to design a really high quality playback and upload environment for our customers’ videos. At first we were simply creating cloud-based packaging and delivery of their content, but the bulk of our application ran on an on-premises MySQL database. Seeing the gains from our initial investment in GCE and GCS, we decided to fully migrate to Google Cloud. 

We started by lifting and shifting our workloads from our on-premises deployments to GCE. But we quickly realized we had to redesign our application to deliver higher availability and responsiveness for an exceptional user experience, which led us to Cloud Spanner, a distributed SQL database management and storage service. With Spanner, we found all the benefits of a relational semantics database with global scale, allowing us to add more nodes with the push of a button. 

Today we have a multi-region instance with 99.999% uptime, so we’re confident it can handle our video workloads. We use about 16 Spanner nodes to cover approximately 50.8 billion rows and about 4.5 terabytes of storage/disk space. And with the launch of Video Library, we have the scale we need to fundamentally change the way organizations handle content. 

Using Spanner as a time machine during migration

During the initial migration, we wanted to ensure an accurate data transfer with as few errors or bugs as possible. When we moved data from MySQL tables into Spanner, we ran spot analyses to check for potential problems. If the data didn’t match, Spanner allowed us to revert to the original in a single click. Through Spanner we could check stale timestamps or past users. It almost functioned like a time machine by offering point-in-time recovery functionality.

Serving up any video at any time

We work with a long tail of content. Depending on how popular a video is, we take a different approach to storing it. We cache the most popular material so it’s immediately accessible and use Cloud Spanner to ensure it’s highly available across multiple regions. On the flip side, we use Cloud SQL for some lower volume satellite services that support other components of the application that don’t require the same guarantees or performance that Cloud Spanner provides.  

As our millions of users continue to upload their own videos, Spanner also acts as a video metadata indexing and cataloging service. When we need to play back a particular video, we can quickly and easily find it in a sea of content. It’s crucial that our database is highly available and reliable so the playback experience is seamless for our users. 

To learn more about Vimeo, visit our site.

Learn more about how your organization can use Cloud Spanner.

As Google Cloud’s fully managed relational database that offers near unlimited scale, strong consistency, and availability up to 99.999%, Cloud Spanner powers applications at any scale in industries such as financial services, games, retail, and healthcare. 

When you set up a Spanner instance, you can choose from two different kinds of configurations: regional and multi-regional. Both configuration types offer high availability, near unlimited scale, and strong consistency. Regional configurations offer 99.99% availability and can survive zone outages. Multi-regional configurations offer 99.999% availability and can survive two zone outages and entire regional outages.

Today, we’re announcing a number of significant enhancements to Spanner’s regional and multi-regional capabilities: 

• Configurable read-only replicas let you add read-only replicas to any regional or multi-regional Spanner instance to deliver low latency reads to clients in any geography
• Spanner’s zero-downtime instance move service gives you the freedom to move your production Spanner instances from any configuration to another on the fly, with zero downtime, whether it’s regional, multi-regional, or a custom configuration with configurable read-only replicas 
• We’re also dropping the list prices of our nine-replica global multi-regional configurations nam-eur-asia1 and nam-eur-asia3 to make them even more affordable for global workloads

Let’s take a look at each of these enhancements in a bit more detail. 

Configurable read-only replicas

One of Spanner’s most powerful capabilities is its ability to deliver high performance across vast geographic territories. Spanner achieves this performance with read-only replicas. As its name suggests, a read-only replica contains an entire copy of the database and it can serve stale reads without requiring a round trip back to the leader region. In doing so, read-only replicas deliver low latency stale reads to nearby clients and help increase a node’s overall read scalability.

For example, a global online retailer would likely want to ensure that its customers worldwide can search and view products from its catalog efficiently. This product catalog would be ideally suited for Spanner’s nam-eur-asia1 multi-region configuration, which has read/write replicas in the United States and read-only replicas in Belgium and Taiwan. This would ensure that customers can view the product catalog with low latency around the globe.

Until today, read-only replicas were available in several multi-region configurations: nam6, nam9, nam12, nam-eur-asia1, and nam-eur-asia3. But now, with configurable read-only replicas, you can add read-only replicas to any regional or multi-regional Spanner instance so that you can deliver low-latency stale reads to clients everywhere. 

To add read-only replicas to a configuration, go to the Create Instance page in the Google Cloud console. You’ll now see a “Configure read-only replicas” section. In this section, select the region for the read-only replica, along with the number of replicas you want per node, and create the instance. It’s as simple as that! 

The following snapshot shows how to add a read-only replica in us-west2 (Los Angeles) to the nam3 multi-regional configuration.

As we roll out configurable read-only replicas, we do not yet offer read-only replicas in every configuration/region pair. If you find that your desired read-only replica region is not yet listed, simply fill out this request form.

Configurable read-only replicas are available today for $1/replica/node-hour plus storage costs. Full details on pricing are available at Cloud Spanner pricing. 

Also announcing: Spanner’s zero-downtime instance move service

Now that you can use configurable read-only replicas to create new instance configurations that are tailored to your specific needs, how can you migrate your current Spanner instances to these new configurations without any downtime? Spanner database instances are mission critical and can scale to many petabytes and millions of queries per second. So you can imagine that moving a Spanner instance from one configuration to another — say us-central1 in Iowa to nam3 with a read-only replica in us-west2 — is no small feat. Factor in Spanner’s stringent availability of up to 99.999% while serving traffic at extreme scale, and it might seem impossible to move a Spanner instance from us-central1 to nam3 with zero downtime.

However, that’s exactly what we’re announcing today! With the instance move service, now generally available, you can request a zero-downtime, live migration of your Spanner instances from any configuration to any other configuration — whether they are regional, multi-regional, or custom configurations with configurable read-only replicas. 

To request an instance move, select “contact Google” in the Edit Instance of the Google Cloud Console and fill out the instance move request form. Once you make a move request, we’ll contact you to let you know the start date of your instance configuration move, and then move your configuration with zero downtime and no code changes while preserving the SLA guarantees of your configuration. 

When moving an instance, both the source and destination instance configurations are subject to hourly compute and storage charges, as outlined in Cloud Spanner pricing. Depending on your environment, instance moves can take anywhere from a few hours to a few days to complete. Most importantly, during the instance move, your Spanner instance continues to run without any downtime, and can continue to rely on Spanner’s high availability, near unlimited scale, and strong consistency to serve your mission-critical production workloads. 

Price drops for global 9-replica Spanner multi-regional configurations

Finally, we’re also pleased to announce that we’re making it even more compelling to use Spanner’s global configurations of nam-eur-asia1 and nam-eur-asia3 by dropping the compute list price of these configurations from $9/node/hour to $7/node/hour. With write quorums in North America and read-only replicas in both Europe and Asia, these configurations are perfectly suited for global applications with strict performance requirements and 99.999% availability. And now, they’re even more cost-effective to use!

Learn more 

• If you are new to Spanner, try Spanner at no charge with a 90-day free trial instance.
• Learn more about multi-regional Spanner configurations by reading Demystifying Cloud Spanner multi-region configurations

As a leading French agricultural cooperative, Groupe Dauphinoise places collaboration at the heart of its philosophy. Working with farmers in the Rhone-Alpes region, Groupe Dauphinoise takes on a diverse range of activities from agricultural production to research and development to running retail outlets.

As its operations expanded and strained its existing infrastructure, Groupe Dauphinoise saw the opportunity to upgrade its technology solutions and adopted G Suite, Chrome devices and ultimately Google Cloud Platform (GCP).

“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream,” says Sylvain Claudel, Head of IT at Groupe Dauphinoise.

“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream.”

—Sylvain Claudel, Head of IT, Groupe Dauphinoise

Flexible workforce, stable infrastructure

Many of Groupe Dauphinoise’s 1,500 employees spend their days out of the office. Five years ago, with the help of Google partner GoWizYou, the company transitioned to G Suite to improve the flexibility and collaboration of its staff.

As Groupe Dauphinoise began to expand and collect more data, the company reached the limits of its on-premise infrastructure. Adding new storage was not a simple matter. Acquiring, configuring and synchronising new servers costs Groupe Dauphinoise time as well as money. In addition, with all its servers stored in a single room, security was a concern. Groupe Dauphinoise needed a new infrastructure.

Google Cloud Platform was the only solution in mind after Groupe Dauphinoise’s experience with G Suite and Chromebooks. Disruption was kept to a minimum thanks to Google’s licensing agreements with Microsoft products, allowing the company to migrate without affecting its operations.

After migrating its infrastructure to Compute Engine, Groupe Dauphinoise can let Google look after the security and maintenance. Cloud IAM makes it easy for Groupe Dauphinoise to hand out permissions to sensitive resources across a number of sites with maximum security and minimum fuss. The company placed its archives in Cloud Storage, while Cloud SQL allows it to continue to make use of its MySQL databases without disrupting the day to day business. Meanwhile, BigQuery provides Groupe Dauphinoise with the raw power to analyse large datasets quickly.

“Our company is growing and we have more and more data to collect and analyze like sales data, weather patterns or production numbers. With GCP, we have more than a single on-premise data store, so our disaster recovery plan is much more flexible. Compute Engine allows us to add more storage quickly and easily, without having to spend days synchronizing data and installing new servers. We have improved security and maintained the stability of our infrastructure while keeping costs down,” says Sylvain.

Safeguarding the present, looking to the future

With Google Cloud Platform, Groupe Dauphinoise has expanded and secured its infrastructure without sinking costs into on-premise servers or DevOps staff. Its investment in Chrome devices and adoption of G Suite mean that its mobile workforce can fully reap the benefits of a cloud-based infrastructure while dramatically cutting the cost of hardware. Meanwhile, working with a 200 million line table of sales data in Google BigQuery, the cooperative found that queries ran ten times faster than with its previous database provider. Groupe Dauphinoise is experimenting with Google BigQuery to expand its BI capabilities. Products like Google BigQuery help Groupe Dauphinoise grow its business, safe in the knowledge that its infrastructure is stable and secure.

“The amount of data we collect is growing very quickly. We need to break our rules and evolve from the mindset that we had with on-premise infrastructure and our old databases. We can now look at collecting more customer fidelity data, or big data for our farmers. With our data and infrastructure in Google’s care, we can concentrate on growing our customer base instead of our IT department!” says Sylvain.

Editor’s note: In Today’s guest post we hear from beauty leader L’Oréal about their approach to building a modern data platform on fully managed services: managing the ingest of diverse datasets into BigQuery with Cloud Run, and orchestrating transformations into relevant business domain representations for stakeholders across the organization. Learn more about how businesses have benefited from Cloud Run in Forrester’s report on Total Economic Impact.

L’Oréal was born out of science. For over 100 years, we have always shaped the future of beauty, and taken its eternal quest to new horizons. This has earned us our current position as the world’s uncontested beauty leader (~€ 32 B annual sales in 2021), present in 150 countries with over 85,000 employees.

Today, with the power of our game-changing science, multiplied by cutting-edge technologies, we continue our lifelong journey of shaping the future of beauty.

As a Beauty Tech company, we leverage our decades-long heritage of rich data assets to empower our decision-making with instant, sophisticated analysis.

Because we oversee global brands, which must adapt to local requirements, we need to maintain a deep understanding of what a brands’ data represents, while managing disparate legal and regulatory requirements for different countries. Our end goal is to run a safe, compliant and sustainable data warehouse as efficiently and effectively as possible.

We sync and aggregate internal and external data from a wide variety of sources across organizations and retail stores. This made the management of our data warehouse infrastructure used to be very complex and hard to manage before Google Cloud. L’Oréal’s footprint was so large that we once found it impossible to have a standardized method to handle data. Every process was vendor-specific, and the infrastructure was brittle. We went looking for a solution to our complex data infrastructure needs, and defined the following non-negotiable principles:

• No Ops: The job of a developer at L’Oréal is not to manage servers. We need an elastic infrastructure that scales on demand, so that our developers can focus on delivering customized and inclusive beauty experiences to all consumers, rather than focusing on managing servers.
• Secure: We have strict security and compliance requirements which vary by country, and we employ a zero-trust security strategy. We must keep both our own internal data and customer data safe and encrypted.
• Sustainable : Our data lives in multiple environments, including on-prem data centers and public cloud services. We must be able to securely access and analyze this data while minimizing the complexity and environmental impact of moving and duplicating data.
• End-to-end supervision: Because developers shouldn’t be managing servers, we need a “single pane of glass” dashboard to monitor and triage the system if something goes wrong.
• Easy-to-deploy: Deploying code safely should not compromise velocity. We are constantly developing innovations that push the boundaries of science and reinvent beauty rituals. We need integrated tools to make our code deployment process seamless and safe.
• Event-driven architecture: Our data is used globally by research, product, business and engineering teams with high expectations on data quality and timeliness. Many of our internal processes and analysis are based on near real-time data.
• Data products delivered “as a service”: We want to empower our employees to drive business value at record speed. To that end, we need solutions that enable us to remove the developers from the critical path of solution delivery as much as possible.
• Extract-load-transform (ELT): Our goal is to implement the pattern to load data as soon as possible into the data warehouse to take advantage of SQL transformations.

After considering multiple vendors on the market, with these principles in mind, we landed on end-to-end Google Cloud serverless and data tooling. We were already using Google Cloud for a few processes, including BigQuery, and loved the experience.

We’ve now expanded our use of Google Cloud to fully support the L’Oréal Beauty Tech Data Platform.

L’Oréal’s Beauty Tech Data Platform incorporates data from two types of sources: directly via API, which is data that adapts easily to our schema and is inserted directly into BigQuery, and bulk data from integrations, which require event-driven transformations using Eventarc mechanisms. These transformations are performed in Cloud Run and Cloud Functions (2nd gen), or directly in SQL. With Google Cloud, we can adapt very quickly.

Today, we currently have 8500 flows for ~5000 users using the native zero-trust capabilities offered by Google Cloud. Indeed, the flows come from Google Cloud and other third-party services.

BigQuery enabled us to adopt standard SQL as our universal language in our data warehouse and meet all expectations for queries and reporting. We were also able to load original data using features like federated queries, and efficiently transitioned from ETL to ELT data ingestion by handling semi-structured data with SQL. This approach of loading original data from sources into BigQuery with non-destructive transformations allows us to reprocess data for new use-cases easily, directly within BigQuery.

Our applications are hosted on multiple environments – on-premises, in Google Cloud, and in other public clouds. This made it difficult for our data engineers and analysts to natively analyze data across clouds until we started using BigQuery Omni. This capability of BigQuery allowed us to globally access and analyze data across clouds through a single pane of glass using the native BigQuery user interface itself. Without BigQuery Omni, it would’ve been impossible for our teams to natively do cross-cloud analytics. Moreover, it eliminated the need for us to move sensitive data, which is not only expensive because of local tax and subsea transport, but also incredibly risky – sometimes even forbidden – because of local regulations.

Today Google Cloud powers our Beauty Tech Data Platform, which stores 100TB of production data in BigQuery and processes 20TB of data each month. We have more than 8000 governed datasets, and 2 millions of BigQuery tables coming from multiple data sources such as Salesforce, SAP, Microsoft, and Google Ads.

For more complex transformations where custom and specific libraries are required, Cloud Workflows help us to manage the complexity very efficiently by orchestrating steps in containers through Cloud Run, Cloud Functions and even BigQuery jobs — the most used way to transform and add value to the L’Oréal data.

Additionally, by using BigQuery and Google Cloud’s serverless compute for API ingestion, bulk data loading, and post-loading transformations, we can keep the entire system in a single boundary of trust at a fraction of the cost. With ingest, queries, and transformations all being fully elastic and on-demand, we no longer have to perform capacity planning for either the compute or analytics components of the system. And of course these services’ pay-as-you-go model perfectly aligns with L’Oréal’s strategy of only paying for something when you use it.

Google Cloud fulfilled the requirements of our Beauty Tech Data Platform. And as if offering us a no-ops, secure, easy-to-deploy, custom-development free, event-based platform with end-to-end supervision wasn’t enough, Google Cloud also helped us with our sustainability efforts.

Being able to measure and understand the environmental footprint of our public cloud usage is also a key part of our sustainable tech roadmap. With Google Cloud Carbon Footprint, we can easily see the impact of our sustainable infrastructure approach and architecture principles. Our Beauty Tech platform is a strategic ambition for L’Oréal: inventing the beauty products of the future while becoming the company of the future.

Sustainable tech is an imperative and a very important step towards this ambition of creating responsible beauty for our consumers, and sustainable-by-design tech services for our employees. We all have a role to play, and by joining forces, we can have a positive impact.

Google Cloud’s data ecosystem and serverless tools are highly complementary, and made it possible to build a next-generation data analytics platform that met all our needs.

Get started using serverless and BigQuery together on Google Cloud today.

Over the past few decades, organizations have mastered the science of data warehousing. They have increasingly applied descriptive analytics to large quantities of stored data, gaining insight into their core business operations. Conventional Business Intelligence (BI), which focuses on querying, reporting, and Online Analytical Processing, might have been a differentiating factor in the past, either making or breaking a company, but it’s no longer sufficient.

Today, not only do organizations need to understand past events using descriptive analytics, they need predictive analytics, which often uses machine learning (ML) to extract data patterns and make probabilistic claims about the future. The ultimate goal is to develop prescriptive analytics that combine lessons from the past with predictions about the future to automatically guide real-time actions.

Traditional data warehouse practices capture raw data from various sources, which are often Online Transactional Processing (OLTP) systems. Then, a subset of data is extracted in batches, transformed based on a defined schema, and loaded into the data warehouse. Because traditional data warehouses capture a subset of data in batches and store data based on rigid schemas, they are unsuitable for handling real-time analysis or responding to spontaneous queries. Google designed BigQuery in part in response to these inherent limitations.

Innovative ideas are often slowed by the size and complexity of the IT organization that implements and maintains these traditional data warehouses. It can take years and substantial investment to build a scalable, highly available, and secure data warehouse architecture. BigQuery offers sophisticated software as a service (SaaS) technology that can be used for serverless data warehouse operations. This lets you focus on advancing your core business while delegating infrastructure maintenance and platform development to Google Cloud.

BigQuery offers access to structured data storage, processing, and analytics that’s scalable, flexible, and cost effective. These characteristics are essential when your data volumes are growing exponentially—to make storage and processing resources available as needed, as well as to get value from that data. Furthermore, for organizations that are just starting with big data analytics and machine learning, and that want to avoid the potential complexities of on-premises big data systems, BigQuery offers a pay-as-you-go way to experiment with managed services.

With BigQuery, you can find answers to previously intractable problems, apply machine learning to discover emerging data patterns, and test new hypotheses. As a result, you have timely insight into how your business is performing, which enables you to modify processes for better results. In addition, the end user’s experience is often enriched with relevant insights gleaned from big data analysis, as we explain later in this series.

The migration framework

Undertaking a migration can be a complex and lengthy endeavor. Therefore, we recommend adhering to a framework to organize and structure the migration work in phases:

1. Prepare and discover: Prepare for your migration with workload and use case discovery.
2. Assess and plan: Assess and prioritize use cases, define measures of success, and plan your migration.
3. Execute: Iterate the following steps for each use case:
   1. Migrate (offload): Migrate only your data, schema, and downstream business applications.
   2. Migrate (full): Alternatively, migrate the use case fully end-to-end. The same as Migrate (offload), with the addition of the upstream data pipelines.
   3. Verify and validate: Test and validate the migration to assess return on investment.

The following diagram illustrates the recommended framework and shows how the different phases are connected:

For a deeper understanding, read Migrating data warehouses to BigQuery: Introduction and overview