Every organization has its own unique data culture and capabilities. Yet each is expected to use technology trends and solutions in the same way as everyone else. Your organization may be built on years of legacy applications, you may have developed a considerable amount of expertise and knowledge, yet you may be asked to adopt a new approach based on a technology trend. On the other hand, you may be on the other side of the spectrum, a digitally native organization built with engineering principles from scratch without legacy systems but expected to follow the same principles as process driven, established organizations. The question is, should we treat these organizations in the same way when it comes to data processing? In this series of blogs and papers this is what we are exploring: how to set up an organization from the first principles from data analyst, data engineering and data science point of view. In reality, there is no such organization that is solely driven by one of these but it is likely to be a combination of multiple types. What type of organization you become is then driven by how much you are influenced by each of these principles. 

When you are considering what data processing technology encompasses, take a step back and make a strategic decision based on your key goals. This can be whether you optimize for performance, cost, reduction in operational overhead, increase in operational excellence, integration of new analytical and machine learning approaches. Or perhaps you’re looking to leverage existing employees’ skills while meeting all your data governance and regulatory requirements. We will be exploring these different themes and will focus on how they guide your decision-making process. You may be coming from technologies which are solving some of the past problems and some of the terminologies may be more familiar, however they don’t scale your capabilities. There is also the opportunity cost of prioritizing legacy and new issues that arise from a transformation effort, and as a result your new initiative can set you further behind on your core business while you play catch up to an ever changing technology landscape. 

Data value chain

The key for any ingestion and transformation tool is to extract data from a source and start acting on it. The ultimate goal is to reduce the complexity and increase the timeliness of the data. Without data, it is impossible to create a data driven organization and act on the insights. As a result, data needs to be transformed, enriched, joined with other data sources, and aggregated to make better decisions. In other words, insights on good timely data mean good decisions.

While deciding on the data ingestion pipeline, one of the best approaches is to look into the volume of data, the velocity of the data, and type of data that is arriving. Other considerations include the number of different data sources you are managing, whether you need to scale to thousands of sources using generic pipelines, whether you want to create one generic pipeline but then apply data quality rules and governance. ETL tools are ideal for this use case as generic pipelines can be written and then parameterized. 

On the other hand, consider the data source. Can the data be directly ingested without transforming and formatting the data? If the data does not need to be transformed and can be ingested directly into the data warehouse as a managed solution. This not only reduces the operational costs but also allows for more timely data delivery. If the data is coming in through an unstructured format such as XML or in a format such as EBCDIC and needs to be transformed and formatted, then a tool with ETL Capabilities can be used depending on the speed of the data arrival. 

It is also important to understand the speed and time of arrival of the data. Think about your SLAs and time durations/windows that are relevant for your data ingestion plans. This would not only drive the ingestion profiles but would also dictate which framework to use. As discussed above, velocity requirements would drive the decision-making process.

Type of Organization

Different organizations can be successful by employing different strategies based on the talent that they have. Just like in sports, each team plays with a different strategy with the ultimate goal of winning. 

Organizations often need to decide on what’s the best strategy to take in respect to data ingestion and processing – whether you need to hire an expensive group of data engineers, or exploit your data wizards and analysts to enrich and transform data that can be acted on, or whether it would be more realistic to train the current workforce to do more functional/high value work rather than to focus on building generally understood and available foundational pieces.

On the other hand, the transformation part of ETL pipelines as we know it, dictates where the load will be. All of these are made a reality in the cloud native world where data can be enriched, aggregated, and joined. Loading data into a powerful and modern data warehouse means that you can already join and enrich the data using ELT. Consequently, ETL isn’t really needed in its strict terms anymore if the data can be loaded directly into the data warehouse.

All of the above was not possible in the traditional, siloed, and static data warehouses and data ecosystems whereby systems would not talk to each other or there were capacity constraints in respect to both storing and processing the data in the expensive Data Warehouse. This is no longer the case in the BigQuery world as storage is now cheap and transformations are now much more capable without constraints of virtual appliances. 

If your organization is already heavily invested into an ETL tool, one option is to use them to load BigQuery and transform the data initially within the ETL tool. Once the as-is and to-be are verified to be matching, then with the improved knowledge and expertise one can start moving workloads into BigQuery SQL, and effectively do ELT. 

Furthermore, if your organization is coming from a more traditional data warehouse that extensively relies on stored procedures and scripting, then the question that one may ask is, do I continue leveraging these skills and expertise and use these capabilities that are also provided in BigQuery? ELT with BigQuery is more natural, similar to what’s already in Teradata BTEQ, Oracle PL/SQL but migrating from ETL to ELT requires changes. This change then enables exploiting streaming use cases, such as real-time use cases in retail. This is because there is no preceding step before data is loaded and made available.

Organizations can be broadly classified under 3 types as Data Analyst Driven, Data Engineering driven, and Blended organization. We will be covering a Data Science driven organization within the Blended category.   

Data Analyst Driven

Analysts understand the business and are used to using SQL/spreadsheets. Allowing them to do advanced analytics through interfaces that they are accustomed to enables scaling. As a result, easy to use ETL tooling to bring data quickly into the target system becomes a key driver. Ingesting data directly from a source or staging area then also becomes critical as it allows analysts to exploit their key skills using ELT and increases timeliness of the data. This is commonplace with traditional EDWs and realized by extended capabilities of using Stored Procedures and Scripting. Data is enriched, transformed, and cleansed using SQL and ETL tools act as the orchestration tools. 

The capabilities brought by cloud computing on separation of data and computation changes the face of the EDW as well. Rather than creating complex ingestion pipelines, the role of the ingestion becomes, bringing data close to the cloud, staging on a storage bucket or on a messaging system before being ingested into the cloud EDW. This then releases data analysts to focus on looking into data insights using tools and interfaces that they are accustomed to. 

Data Engineering / Data Science Driven 

Building complex data engineering pipelines is expensive but enables increased capabilities. This allows creating repeatable processes and scaling the number of sources. Once complemented with cloud it enables agile data processing methodologies. On the other hand, data science organizations allow carrying out experiments and producing applications that work for specific use cases but are not often productionised or generalized. 

Real-time analytics enables immediate responses and there are specific use cases where low latency anomaly detection applications are required to run. In other words, business requirements would be such that it has to be acted upon as the data arrives on the fly. Processing this type of data or application requires transformation done outside of the target.

All the above usually requires custom applications or state-of-the-art tooling which is achieved by organizations that excel with their engineering capabilities. In reality, there are very few organizations that can be truly engineering organizations. Many fall into what we call here as the blended organization.  

Blended org

The above classification can be used on tool selection for each project. For example, rather than choosing a single tool, choose the right tool for the right workload, because this would reduce operational cost, license cost and use the best of the tools available. Let the deciding factor be driven by business requirements: each business unit or team would know the applications they need to connect with to get valuable business insights. This coupled with the data maturity of the organization would be the key to making sure the right data processing tool would be the right fit. 

In reality, you are likely to be somewhere on a spectrum. Digital native organizations are likely to be closer to being engineering driven, due to their culture and business that they are in. However, brick and mortar organizations would be closer to being analyst driven due to the significant number of legacy systems and processes they possess. These organizations are either considering or working toward digital transformation with an aspiration of having a data engineering / software engineering culture like Google. 

The blended organization with strong skills around data engineering, would have built the platform and built frameworks, to increase reusable patterns would increase productivity and then reduce costs. Data engineers focus on running Spark on Kubernetes whereas infrastructure engineers focus on container work. This in turn provides unparalleled capabilities as application developers focus on the data pipelines and even the underlying technologies or platforms changes code stays the same. As a result, security issues, latency requirements, cost demands and portability are addressed at multiple layers. 

Conclusion – What type of organization are you?

Often an organization’s infrastructure is not flexible enough to react to a fast changing technological landscape. Whether you are part of an organization which is engineering driven or analyst driven, organizations frequently look at technical requirements that inform which architecture to implement. But a key, and frequently overlooked, component needed to truly become a data-driven organization is the impact of the architecture on your data users. When you take into account the responsibilities, skill sets, and trust of your data users, you can create the right data platform to meet the needs of your IT department as well as your business.

To become a truly data-driven organization, the first step is to design and implement an analytics data platform that meets your technical and business needs. The reality is that each organization is different and has a different culture, different skills, and capabilities. Key is to leverage its strengths to stay competitive while adopting new technologies when it is needed and as it fits to your organization. 

To learn more about the elements of how to build an analytics data platform depending on the organization you are, read our paper here.

Building a strong brand in today’s hyper-competitive business environment takes vision. It also requires a flexible, easily managed approach to digital asset management (DAM), so marketing professionals and other stakeholders can easily share, store, track, and manipulate assets to build the brand.

Many of today’s leading companies, including JetBlue, Slack, TripAdvisor, Lyft, and HealthONE, rely on Brandfolder to deliver consistent, organized, and efficient brand experiences. Brandfolder provides an easy-to-use platform that can scale across an entire company with little end-user training, empowering customers to distribute digital assets wherever they are needed. Customers also gain much greater insight into how those assets are used, and how to use them more effectively in marketing campaigns and brand messaging.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform.”

—Ajay Rajasekharan, Head of Data Science, Brandfolder

Brandfolder is constantly advancing its development efforts to introduce new data-driven features without complicating the user experience. Big data, artificial intelligence (AI), and machine learning (ML) are key to meeting customers’ unique business needs, and essential for Brandfolder to compete in the fast-moving DAM industry. To enhance these capabilities, Brandfolder sought a public cloud provider that could help it scale its data pipeline cost effectively while providing access to advanced AI technologies.

After graduating from the Techstars startup accelerator program in 2013, Brandfolder tried two other cloud providers before standardizing on Google Cloud Platform (GCP).

“We saw a difference with Google Cloud from the very beginning because the interactions felt like a strategic relationship,” says Jim Hanifen, Head of Product at Brandfolder. “Google gave us startup credits and a lot of face-to-face support, which we hadn’t experienced with other cloud providers. We decided to move our entire infrastructure to Google Cloud Platform.”

Building an ML platform for brand intelligence

After performing an initial lift-and-shift migration of virtual machines (VMs) onto Compute Engine, Brandfolder built an ML platform using GCP managed services to seamlessly deliver its data products. The platform leverages Cloud SQL, Cloud Storage as the data lake, Cloud Dataproc for cloud-native Apache Spark computing clusters, Cloud Composer as the batch job scheduler, Cloud Pub/Sub as the backbone data pipeline, Container Registry to store Docker images, and Google Kubernetes Engine (GKE) as the application orchestrator. Cloud Dataflow brings data into the data lake and into BigQuery for analysis.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform,” says Ajay Rajasekharan, Head of Data Science at Brandfolder, who describes the architecture in a detailed blog. “We simply ingest raw application and event data on one end and output an ML service on the other.”

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost. We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

—Brett Nekolny, Head of Engineering, Brandfolder

For many general use cases, Brandfolder does not need to build custom ML models, and instead relies on pre-trained API models from GCP. For example, it uses Vision API and Video Intelligence API to auto-tag creative assets on import to enable fast, intuitive searches across images and videos. When more product- and brand-specific modeling is required to address unique customer use cases, Brandfolder builds and trains custom ML models using its GCP pipeline or Cloud AutoML, a suite of products built on Google transfer learning and neural architecture search technology. For example, if a Brandfolder customer makes different types of grills, Brandfolder can use AutoML Vision to train a model to recognize the different grills.

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost,” explains Brett Nekolny, Head of Engineering at Brandfolder. “We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

Industry-leading security and performance

Google Cloud’s security model helps Brandfolder give existing and prospective customers peace of mind that their data will be protected. Cloud Identity & Access Management (Cloud IAM) provides enterprise-grade access control, while Cloud Identity-Aware Proxy (Cloud IAP) enables remote users to work more securely without the hassles of a VPN client. GCP also isolates cloud resources into projects, making it easy to assign permissions and keep data and VMs organized and segregated.

“With Google Cloud, everything begins and ends with security, which makes things very easy for us,” says Jim. “If we’re under a security review, we can submit a Google security white paper. If a potential customer has security concerns, we tell them we are hosted on GCP, and those concerns go away.”

To give customers even better application performance for accessing their brand assets, Brandfolder uses Cloud Memorystore, an in-memory data store service for Redis, to cache data and provide sub-millisecond data access for production applications.

“It was much easier for us to use Cloud Memorystore versus running Redis on our compute instances,” says Brett. “The high availability, replication across zones, and automatic failover with no data loss are big for us.”

Global private network interconnects between Google Cloud and the Fastly content delivery network (CDN) dramatically reduce latency, allowing Brandfolder’s customers to deliver and update even very large creative assets quickly around the world.

“What’s beautiful about the relationship between Google and Fastly is that if one of our customers uploads a new version of an asset, we can propagate that out to Fastly, and the new version will automatically show up in all the places where it’s referenced,” says Brett.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter. Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”

—Jim Hanifen, Head of Product, Brandfolder

Improving employee and customer productivity

Brandfolder also uses Google solutions for real-time collaboration and productivity, using G Suite to connect employees with intuitive, cloud-based apps. Teams use Gmail, Calendar, Docs, Drive, Sheets, Slides, and Hangouts Meet every day to move the business forward. Many of Brandfolder’s customers are also G Suite users, and Brandfolder offers a plug-in that allows them to view their creative assets inside of Docs and pull images in as needed. Customers can also log into Brandfolder with their G Suite credentials, making the solution even easier to use.

“We’ve been using G Suite since the beginning, and it’s helped us collaborate efficiently to build a successful, growing company,” says Jim. “Our teams expect to have that kind of close collaboration, and everyone here enjoys the G Suite experience.”

Driving 99 percent annual business growth

With automated tagging and other innovative AI-based features, Brandfolder is helping customers locate and distribute assets faster. As a result, Brandfolder is building customer loyalty and increasing sales, growing its business by 99 percent year-over-year. Since moving to GCP, Brandfolder has been able to scale its analytics and data pipeline 50x without a corresponding increase in costs and has not had to expand its development team.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter,” says Jim. “Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”

Data science can be applied to business problems to improve practices and help to strengthen customer satisfaction. In this blog, we address how the addition of Looker extends the value of your Google Cloud investments to help you understand your customer journey, unlock value from first-party data, and optimize existing cloud infrastructure.    

Data is a powerful tool 

While everyone doesn’t need to understand the nuts and bolts of data technologies, most people do care about the value data can create for them — how it can help them do their jobs better. Within the data space, we are seeing a trend of ongoing failure to make data accessible to “humans” –   the industry still hasn’t figured out how to put data into the hands of people when and where they need it, how they need it. 

What if everyone in your organization could analyze data at scale, and make more-informed, fact-based decisions?   Data and insights derived from data are valuable but only if your users see it.  We think Looker helps solve that. 

Solutions tell a larger story of how it all fits together 

Across all verticals and industries,  businesses benefit from knowing and understanding their customers better. Many business goals are to increase revenue by improving product recommendations and pricing optimization, improving the user experience through targeted marketing and personalization, and reducing churn while improving retention rates.  To help reach  these goals, key strategies should focus on understanding customer needs, their motivations, likes and dislikes, and using all available data – in other words, put yourself in the shoes of your customer.    

Our goal with Looker solutions is to offer the right level of out-of-box support that allows customers to get to value quickly, while maintaining the necessary flexibility. We aim to offer a library of data-driven solutions that accelerate data projects. Many solutions include Looker Blocks (pre-built pieces of code that accelerate data exploration environments) and Actions (custom integrations) that get customers up and running quickly and lets you build business-friendly access points for Google Cloud functionality like BQML, App Engine and Cloud Functions.  

Below, you’ll find a sampling of the newest Looker solutions. 

Listening to customers by looking at the data

Looker’s solution for Contact Center AI (CCAI), helps businesses gain a deeper understanding and appreciation of their customers’ full journey by unlocking insights from all their company’s first-party data. Call centers can converse naturally with customers and deliver outstanding experiences by leveraging artificial intelligence. CCAI‘s newest product —CCAI Insights — reviews conversations support agents are having, finding and annotating the data with the important information, and identifying the calls that need review. We’ve partnered with the product teams at CCAI to build Looker’s Block for CCAI Insights, which sets you on the path to integrating the advanced insights into your first-party data in Looker, overlaying business data with the customer experience.

Businesses can better understand contact center experiences and take immediate action when necessary to make sure the most valuable customers receive the best service. 

Realizing full business value of First-Party data

Looker for Google Marketing Platform (GMP) provides marketers the power to unlock the value of their company’s first-party data to more effectively target audiences.  The Looker Blocks and Actions for GMP offer interactive data exploration, slices of data with built-in ML predictions and activation paths back to the GMP.  This strategic solution continues to evolve with the Looker Action for Google Ads (Customer Match), the Looker Action for Google Analytics (Data Import) and the Looker Block for Google Analytics 4 (GA4).

• The Looker Action for Customer Match allows marketers to send segments and audiences based on first-party data directly into Google Ads. Reach users cross-device and across the web’s most powerful channels such as Display, Video, YouTube, and Gmail.  The entire process is performed within a single screen in Looker, and is able to be completed in a few minutes by a non-technical user. 
• The Looker Action for Data Import can be used to enhance user segmentation and remarketing audiences in Google Analytics by taking advantage of user information accessible in Looker, such as in CRM systems or transactional data warehouses.
• The Looker Block for Google Analytics 4 (GA4) expands the solution’s support with out-of-the-box dashboards and pre-baked BigQuery ML models for the newest version of Google Analytics.The Looker Block offers up reports with flexible configuration capabilities to unlock custom insights beyond the standard GA reporting. Customize audience segments, define custom goals to track and share these reports with teams who do not have access to the GA console.

From clinical notes to patient insights at scale

Taking a look at the Healthcare vertical, the Looker Healthcare NLP API Block serves as a critical bridge between existing care systems and applications hosted on Google Cloud providing a managed solution for storing and accessing healthcare data in Google Cloud. The Healthcare NLP API uses natural language models to extract healthcare information from medical text,  rapidly unlocking insights from unstructured medical text and providing medical providers with simplified access to intelligent insights.  Healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, such as medications, procedures, conditions, clinical history, and begin to link this to other clinical data sources for downstream AI/ML.   

Specifically, the natural language processing (NLP) Patient View (pictured below) allows you to review a single selected patient of interest, surfacing their clinical notes history over time. It informs clinical diagnosis with family history insights, which is not currently captured in claims, and captures additional procedure coding for revenue cycle purposes.

The dashboard below shows the NLP Term View which allows users to focus on chosen medical terms across the entire patient population in the dataset so they can start to view trends and patterns across groups of patients. 

This data can be used to: 

• Enhance patient matching for clinical trials 
• Identify re-purposed medications
• Drive advancements for research in cancer and rare diseases
• Identify how social determinants of health impact access to care

Managing Costs Across Clouds

Effective cloud cost management is important for reasons beyond cost control — it provides you the ability to reduce waste and predictably forecast both costs and resource needs. Looker’s solution for Cloud Cost Management offers quick access to necessary reporting and clear insights into cloud expenditures and utilization. 

This solution brings together billing data from different cloud providers in a phased approach: get up and running quickly with Blocks optimized for where the data is today (Google Cloud, AWS or Azure) as you work towards more sophisticated analysis for cross-platform planning and even cloud spend optimization with the mapping of tags, labels and cost centers across clouds.

The Looker Cloud Cost Management solution provides operational teams struggling to monitor, understand, and manage the costs and needs associated with their cloud technology with a comprehensive view into what, where and why they are spending money.

Making better decisions with Looker-powered data

Leading companies are discovering ways to get value from all of that data beyond displaying it in a report or a dashboard. They want to enable everyone to make better decisions but that’s only going to happen if everyone can ask questions of the data, and get reliable, correct answers without using outdated or incomplete data and without waiting for it. People and systems need to have data available to them in the way that makes the most sense for them at that moment.  

It’s clear that successful data-driven organizations will lead their respective segments not because they use data to create reports, but because they use it to power data experiences tailored to every part of the business, including employees, customers, operational workflows, products and services.   

As people’s way of experiencing data has evolved, more than ever before, dashboards alone are not enough. You can use data as fuel for data-driven business workflows, and to power digital experiences that improve customer engagement, conversions, and advocacy. 

From Nov. 9 – 11th, Looker is hosting its annual conference JOIN, where we’ll be showing new features, including how we help to:

• Build data experiences at the speed of business
• Accelerate the business value with packaged experiences 
• Unleash more insights for more people in the right way – Deliver data experiences at scale

There is no cost to attend JOIN.  Register here and learn how Looker helps organizations build and deliver custom data-driven experiences that goes beyond just reports and dashboards, scales and grows with your business, allows developers to build innovative data products faster, and ensures data reaches everyone.

Do you want to fire up a bunch of techies? Talk about multicloud! There is no shortage of opinions. I figured we should tackle this hot topic head-on, so I recently talked to four smart folks—Corey Quinn of Duckbill Group, Armon Dadgar of Hashicorp, Tammy Bryant Butow of Gremlin, and James Watters of VMware—about what multicloud is all about, key considerations, and why you should (or shouldn’t!) do it.

Five important insights came out of these discussions. If you’re on a multicloud journey or considering one, keep reading.

Do: Choose to do multicloud for the right reasons

Don’t do multicloud because Gartner says so, implores Corey Quinn. Before embarking on a multicloud, define a “why” focused on business value journey, says Armon Dadger. For example, you might want to use services from each public cloud because of their differentiated services, according to Tammy Bryant Butow. Armon also calls out regulatory reasons, existing business relationships, and accommodating mergers and acquisitions. On the topic of M&A, Corey points out that if you acquire a company that uses another cloud, it’s usually expensive and difficult to consolidate. It can be smarter to stay put.

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

Don’t: Over-engineer for workload or data portability

Thinking that you’ll build a system that moves seamlessly among the various cloud providers? Hold up, says our group of experts. Armon points out that aspects of your toolchain or architecture may be multicloud—think of some of your workflows or global network routing—but that shifting workloads or data is far from simple. Corey says that trying to engineer for “write once, run anywhere” can slow you down, and ignores the inherent uniqueness that’s part of each platform. Specifically, Corey calls out the per-cloud stickiness of identity management, security features, and even network functionality. And data gravity is still a thing, says James, that causes some to dismiss multicloud outright.

If you’re using multiple public clouds, you take advantage of the distinct value each offers, Armon says. Use native cloud services where possible so that you see the benefits from useful innovations, built-in resilience, and baked-in best practices. The value from that cloud-infused workload may outweigh the benefits of seamless portability.

https://youtube.com/watch?v=B1VH56_L8f8%3Fenablejsapi%3D1%26

Do: Recognize different stakeholder interests and needs

James smartly points out that many multicloud debates happen because people are arguing from different perspectives. Context matters. If you’re an infrastructure engineer who invests heavily in a given cloud’s identity and access management model, multicloud looks tricky. Or if you’re a data engineer with petabytes of data homed in a particular cloud, multicloud may look unrealistic. James highlights that many developers default to multicloud because their local tools—where all the work happens—are multicloud. A developer’s IDE and preferred code framework(s) aren’t tied to any given cloud. Be aware that groups within your organization will come at multicloud from distinct directions. And this may impact your approach!

https://youtube.com/watch?v=I9sqXDqkKBM%3Fenablejsapi%3D1%26

Don’t: Go it alone

Corey talks about the importance of asking others what worked, and what didn’t. Tammy offers her best practices around sharing results from experiments. It’s about sharing knowledge and tapping into it for community benefit. Others have probably tried what you’re trying, and can help you avoid common pitfalls. If you’ve just made an architectural choice that didn’t work out, share it, and help others avoid the pain. 

Read research from analysts, go to conferences or watch videos to observe case studies, and join online communities that offer a safe place to share mistakes and learn from others.

https://youtube.com/watch?v=mrSb5vqOfuI%3Fenablejsapi%3D1%26

Do: Experiment first using techniques like multi-region deployments

If you think you can operate systems across clouds, how about you first try doing it across regions in a specific cloud, suggests Corey. Getting a system to properly work across cloud regions isn’t trivial, he says, and that experience can help you uncover where you have architectural or operational constraints that will be even worse across cloud providers.

This is great guidance if your multicloud aspirations involve using multiple clouds to power one application—versus the more standard definition of multicloud where you use different clouds for different applications—but can also surface issues in your support process or toolchain that fail when faced with distributed systems. Start with muti-region deployments and chaos engineering experiments before aggressively jumping into multicloud architectures.

The Google Cloud take

Do the things above. It’s great advice. I’ll add three more things that we’ve learned from our customers.

1. Don’t fear multicloud. You’re already doing it. You don’t single-source everything. As Corey mentioned, you probably already have one cloud for productivity tools, another for source code, another for cloud infrastructure. You’ll use software and application services from a mix of providers for a single app. You have that experience in your team and have been doing that for decades. What people do rightly worry about is using more than one infrastructure service beneath an application, as that can introduce latency, security, and logistical hurdles. Make sure you know which model your team is considering.
2. Embrace the right foundational components, including Kubernetes. Will everything run on Kubernetes? Of course not. Don’t try to do that. But it also represents the closest thing we have to a multicloud API. Companies are using Kubernetes to stripe a consistent experience across clouds. And this isn’t just to orchestrate containers, but also to manage infrastructure and cloud-native services. Also, consider where you need other fundamental consistency across clouds, including areas like provisioning and identity federation.
3. Use Google Cloud as your anchor. Here’s a fundamental question you have to decide for yourself: Are you going to bring your on-premises technology and practices to the cloud, or bring cloud technology and practices on-prem? We sincerely believe in the latter. Anchor to where you’re trying to get to. We offer Anthos as a way to build and run distributed Kubernetes fleets in Google Cloud and across clouds. By using a cloud-based backplane instead of an on-prem one, you’re offloading toil, leveraging managed services for scale and security, and introducing modern practices to the rest of your team.

We learned a lot about multicloud through these discussions, and it seems like others did too. That’s why we’re going to do a second round of interviews with a new crop of experts so that we can keep digging deeper into this topic. Stay tuned!