If you’re self-managing relational databases such as MySQL, PostgreSQL or SQL Server, you may be thinking about the pros and cons of cloud-based database services. Regardless of whether you’re running your databases on premises or in the cloud, self-managed databases can be inefficient and expensive, requiring significant effort around patching, hardware maintenance, backups, and tuning. Are managed database services a better option?

To answer this question, Google Cloud sponsored a business value white paper by IDC, based on the real-life experiences of eight Cloud SQL customers. Cloud SQL is an easy-to-use, fully-managed database service for running MySQL, PostgreSQL and SQL Server workloads. More than 90% of the top 100 Google Cloud customers use Cloud SQL.

The study found that migration to Cloud SQL unlocked significant efficiencies and cost reductions for these customers. Let’s take a look at the key benefits in this infographic.

A deeper dive into Cloud SQL benefits

To read the full IDC white paper, you can download it here: The Business Value of Cloud SQL: Google Cloud’s Relational Database Service for MySQL, PostgreSQL, and SQL Server, by Carl W. Olofson, Research Vice President, Data Management Software, IDC and Matthew Marden, Research Vice President, Business Value Strategy Practice, IDC.

Looking for commentary from IDC? Listen to the on-demand webinar, How Enterprises Have Achieved Greater Efficiency and Improved Business Performance using Google Cloud SQL, where Carl Olofson discusses the downsides of self-managed databases and the benefits of managed services like Cloud SQL, including the cost savings and improved business performance realized by the customers interviewed in the study.

Getting started

You can use our Database Migration Service for an easy, secure migration to Cloud SQL. Since Cloud SQL supports the same database versions, extensions and configuration flags as your existing MySQL, PostgreSQL and SQL Server instances, a simple lift-and-shift migration is usually all you need. So let Google Cloud take routine database administration tasks off your hands, and enjoy the scalability, reliability and openness that the cloud has to offer.

Start your journey with a Cloud SQL free trial.

Sabre is an innovative software and technology company that leverages highly scalable databases and artificial intelligence (AI) to power travel industry partners around the globe. Our company processes more than 12 billion shopping requests and serves over 1 billion travelers every year.

The companies that partner with us include airlines, travel websites, agencies, and hotels. We are distinguished by a long history of integrating cutting edge technology into our operations. Our earliest innovations include building one of the first transaction processing systems in existence and a travel distribution network that predates the Internet! Through our next generation of AI-powered solutions, we are focused on optimizing the retailing, distribution, and fulfillment experience of the travel industry.

In my role as chief architect for Sabre Labs, I spearhead the long-term technology choices we are making to enhance the development, deployment, and operation of our software. We’ve embarked on a multi-year strategy to transform our technology and solutions. Rearchitecting our infrastructure to become fully cloud native is a central tenet of this transformation. In these early iterations of our plan, we have seen the significant impact of machine learning and its potential in bringing truly personalized travel experiences to customers. As part of this effort, we’ve established a 10-year partnership with Google to help accelerate our transformation and bring innovation to the travel industry.

Database choices require tradeoffs

The complexity and scale of the travel industry places high demands on the cloud services we utilize. We tend to place more emphasis on how a particular cloud service will impact an application’s reliability, performance, or development time, rather than choosing a service purely for its functionality. When it comes to databases, this can often mean making a tradeoff between latency and consistency.

The tradeoff exists for any database that serves multiple copies of its data in different availability zones or geographic regions for reliability. A database designed to ensure that everyone sees a consistent view of the latest data might update those copies synchronously using a consensus algorithm, which affects how quickly the data can be served. On the other hand, a database that’s optimized for faster data serving might update each copy asynchronously and not guarantee consistent reads across records.

Cloud Spanner and Bigtable–two of Google Cloud’s managed databases–are both highly effective services, and each one could support many of our travel applications. But as you will see, the latency vs. consistency tradeoff made it clear which one was best suited for two of our most critical cases.

Google Cloud Spanner facilitates strong, global consistency for Sabre

An airline’s reservation database stores a passenger’s booking information, seat selection, tickets, special requests, and other critical information about their trip. As a result, this data sits at the consistency end of that consistency/latency spectrum. Sabre typically processes thousands of reservation updates per second on behalf of our carrier customers. An airline’s reservation database must be served from many availability zones (and data is replicated across these availability zones) so that it remains available in the event of an outage. It also requires ACID properties for transactional updates across records since airlines often make changes to multiple passengers and multiple flights at the same time.

We needed a system that can handle bursts of concurrent updates, as would occur during a snowstorm when hundreds of thousands of passengers might be automatically moved to alternate flights. Spanner is a great fit for the reservations case because of its unique consistency guarantees. It processes over 1 billion requests per second at peak and provides five 9s SLA (99.999 percent) to support our applications. Spanner also helps us maintain compliance, business continuity, redundancy, and reliability using the same secure-by-design infrastructure, built-in data protection and replication, and multi-layered security that are essential to our Google Cloud workloads.

Spanner’s client libraries also provide built-in mechanisms to handle retrying in the event of write conflicts with another transaction and allow developers to choose stale reads in read-only transactions for improved performance. Of course, consistency across multiple zones or regions doesn’t come for free. It means higher write latencies than if we wrote to a comparable database running in a single availability zone, but for an application that manages flight reservations, it’s a tradeoff that makes sense.

Bigtable provides predictable, low latency at scale

Our flight shopping systems sit at the other end of the latency/consistency spectrum. Sabre’s shopping engine generates millions of itineraries per second on behalf of travelers using mobile apps, third-party travel websites, and airline call centers. Each itinerary requires significant compute resources to calculate: We need to find which combinations of flights make sense and evaluate complex rules about their availability and pricing. Users are typically less patient while searching for a flight than they are when booking it, so we needed a low latency solution.  But we can cache many of these shopping results to reduce our compute usage. For instance we can decide how long to cache results based on factors like how far the flight results are from the departure. 

Bigtable makes an excellent choice for this shopping cache. It’s a NoSQL database service built to handle high-throughput, low-latency applications, with more than 10 exabytes of data under management. Bigtable’s unique latency properties—such as predictability and single-digit millisecond response time even for multi-petabyte tables—enable us to serve large volumes of shopping results cost effectively, while providing low response times to travelers.

Google Cloud supports a focus on innovation

Managed databases like Bigtable and Spanner are a significant part of Sabre’s cloud strategy. The combination of unique tools like Key Visualizer for Bigtable and Spanner as well as integration with other Google Cloud services like Cloud IAM, Cloud Monitoring, and now Datastream, make the experience of operating managed databases with Google Cloud much easier than it is with several of their self-hosted counterparts. As a result of their granular pricing models and ability to be deployed and automated by SREs, the managed databases we use also end up with a lower total cost of ownership.

We’re particularly excited about a few recent database-related announcements from Google Cloud. Bigtable’s SLA update gives us more concrete expectations in terms of multi-cluster, multi-region uptime. Spanner’s change to provisioning in Processing Units increases its cost efficiency when deploying in non-production environments where we may need many isolated instances, but won’t come close to the limits of a single node. In those cases, Spanner instances may now be configured in one-tenth of a node increments.

Our cloud transformation depends on having a choice of databases for different use cases, trade offs, and migration schedules. In addition to managed databases, we expect to use self-hosted databases, database solutions available in Cloud Marketplace, and transitional services like Cloud SQL for a few more years. In an industry as demanding as travel, accelerating our most critical applications using technology unique to Google Cloud means less time spent optimizing latency and consistency, and more time spent innovating.

Learn more about how your organization can use Bigtable and Spanner.

Database disaster recovery (DR) planning is an important component of a bigger DR plan, and for enterprises using Microsoft SQL Server on Compute Engine, it often involves critical data.

When you’re architecting a disaster recovery solution with Microsoft SQL Server running on Google Cloud Platform (GCP), you have some decisions to make to build an effective, comprehensive plan. 

Microsoft SQL Server includes a variety of disaster recovery strategies and features, such as Always On availability groups or Failover Cluster Instances.

And Google Cloud is designed from the start for resilience and availability. There are several types of data centers available within GCP where you can map SQL Server’s availability features based on your specific requirements: zones and regions. Zones are autonomous data centers co-located within a GCP region. These regions are available in different geographies such as North America or APAC. 

However, there is no single disaster recovery strategy to map Microsoft SQL Server DR features to Google Cloud’s data center topology that satisfies every possible combination of disaster recovery requirements.

As a database architect, you have to design a custom disaster recovery strategy based on your specific use cases and requirements.

Our new Disaster Recovery for Microsoft SQL Server solution provides information on Microsoft’s SQL Server disaster recovery strategies, and shows how you can map them to zones and regions in GCP based on your business’s particular criteria and requirements. One example is deploying an availability group within a region across three zones (shown in the diagram below). 

For successful DR planning, you should have a clear conceptual model and established terminology in place. In this solution, you’ll find a base set of concepts and terms in context of Google Cloud DR. This includes defining terms like primary database, secondary database, failover, switchover, and fallback.

You’ll also find details on recovery point objective, recovery time objective and single point of failure domain, since those are key drivers for developing a specific disaster recovery solution.

Building a DR solution with Microsoft SQL Server in GCP regions
To get started with implementing the availability features of Microsoft SQL Server in the context of Google Cloud, take a look at this diagram, which shows the implementation of an Always On availability group in a GCP region, using several zones:

In the new solution, you’ll see other availability features, like log shipping, along with how they map to GCP. In addition, features in Microsoft SQL Server that are not deemed availability features—like server replication and backup file shipping—can actually be used for disaster recovery, so those are included as well. 

Disaster recovery features of Microsoft SQL Server do not have to be used in isolation and can be combined for more complex and demanding use cases. For example, you can set up availability groups in two regions with log shipping as the transfer mechanism between the regions.

Disaster Recovery for Microsoft SQL Server also describes the disaster recovery process itself, how to test and verify a defined disaster recovery solution, and outlines a basic approach, step-by-step. 

Editor’s note: Today we’re hearing from Ryan Mason, Director, Head of DTC Growth & Strategy, at alcoholic beverage firm, Constellation Brands on the company’s shift to Direct-to-Consumer (DTC) sales and how Google Cloud’s powerful technology stack helped with this transformation. 

It’s no secret that consumer businesses have been up-ended in a lasting manner after 18 months of the pandemic. Consumers have been forced to shop differently over the past year – and as a result, they’ve evolved to be more comfortable with online spending and have grown to expect a certain level of convenience. While the e-commerce share of consumer sales has grown steadily over the past decade, the pandemic was the catalyst for the famous “10 years of growth in 3 months” which many argue is here to stay. 

Facing this reality head-on,  we placed a new emphasis on Direct-to-Consumer (DTC) with our acquisition of Empathy Wines, a DTC-native wine brand that sells directly to consumers via e-commerce. To accelerate our innovation in the DTC space, we added headcount and new functions to the existing Empathy team and empowered the newly-minted DTC group to apply their digital commerce operating model across the rest of the wine and spirits portfolio, which includes Robert Mondavi Winery, Meiomi Wines, The Prisoner Wine Company, High West Whiskey, and more. 

One pandemic and one year later, DTC sales have surged in the wine and spirits category with Constellation positioned as a leader armed with a unique and powerful cloud technology stack, best-in-class e-commerce user experiences, modernized fulfillment solutions, and data-driven growth marketing. 

Benefits of Going DTC 

A report from McKinsey estimates that the strategic business shift to DTC has been accelerated by two years because of the pandemic and argues that consumer brands that want to thrive will need to aim for a 20% DTC business or higher, which is already taking shape in the market: Nike’s direct digital channels are on track to make up 21.5% of the total business by the end of 2021, up from 15.5% in the last fiscal year, and Adidas is aiming for 50% DTC by 2025. But outside of the clear revenue upside, the auxiliary benefits of going DTC are robust.

For Constellation Brands, each of these four pillars ring true, and our shift toward DTC is as much about margin accretion and revenue mix management as it is about consumer insights and data. The added complexities of the alcohol space add wrinkles to our DTC approach and manifest in many areas like consumer shopping preference, shipping and logistics hurdles, and more. In order to win share early and continue to lead the category, we recognized the need to harness the immense amount of first-party data to power impactful and actionable insights. 

Our DTC technology architecture has fostered a value chain that is completely digitized: website traffic, marketing expenditures, tasting room transactions, e-commerce transactions, logistics and fulfillment events, cost of goods sold (COGS) and margin profiles, etc. are recorded and stored in a data warehouse in real time.  For the first time, at any given moment, we can easily and deterministically answer complex business questions like “what is the age and gender distribution of my customers from Los Angeles who have purchased SKU X from Brand.com Y in the last 6 months? What is the cohort net promoter score? Did that increase after we introduced same-day shipping in this zip code? By how much?” 

The ability to answer these questions and understand the root causes allows us to stay nimble with product offerings and iterate marketing strategies at the speed of consumer preference. Further, it enables us to optimize our omnichannel presence in the same manner by leaning on DTC consumer insights to develop valuable strategies with key wholesale distribution partners and 3-Tier eCommerce partners like Drizly and Instacart. At its core, Constellation’s DTC practice is designed to be the consumer-centric “tip-of-the-spear” responsible for generating insights from which all sales channels, including wholesale, can benefit. 

Constellation’s DTC technology approach prioritizes consumer-centricity and insights generation

We have taken a modern approach to building a digital commerce technology stack, leveraging a hub-and-spoke model built around Shopify Plus and other key emergent technology providers like email provider Klaviyo, loyalty platform Yotpo, Net Promoter Score measurer Delighted, Customer Service module Gorgias, payments processor Stripe, event reservations platform Tock, and many more. For digital marketing and analytics, we use Google Cloud and Google Marketing Platform, which includes products like Analytics 360, Tag Manager 360, and Search Ads 360.

To help gather, organize, and store all of the inbound data from the ecosystem, we partnered with SoundCommerce, a data processing platform for eCommerce businesses. Together with SoundCommerce, we are able to automate data ingestion from all endpoints into a central data warehouse in Google BigQuery. With BigQuery, our data team is able to break data silos and quickly analyze large volumes of data that help unlock actionable insights about our business.  BigQuery itself allows for out-of-the-box predictive analytics using SQL via BigQuery ML, and a key differentiator for us is that all Google Marketing Platform data is natively accessible for analysis within BigQuery. 

But data possession only addresses half of the opportunity: we needed a powerful and modern business intelligence platform to help make sense of the vast amounts of data flowing into the system. Core to the search was to find a partner that approached BI in a way that fit with our future-looking strategy.

Our DTC team relies on the accurate measurement of variable metrics like Customer Acquisition Cost (CAC), Customer Lifetime Value (CLV), Churn, and Net Promoter Score (NPS) as a bellwether of the health of the business and monitoring these figures on a daily basis is paramount to success. To enable us to keep an accurate pulse on strategic KPIs, we considered several incumbent BI platforms. Ultimately we selected Google Cloud’s Looker for a range of benefits that separated it from the rest of the pack.

From a vision perspective, in this particular case we felt Looker was most aligned with our belief that better decisions are made when everyone has access to accurate, up-to-date information. Looker allows us to realize that vision by surfacing data in a simple web-based interface that empowers everyone to take action with real-time data on critical commercial activities. Furthermore, Looker’s ability to automate and distribute formatted modules to a myriad of stakeholders on a regular cadence increases data literacy and business performance transparency.

From a product perspective, we chose Looker for it’s cloud offering, web-based interface, and  centralized, agile modeling layer that creates a trusted environment for all users to confidently interact with data — without any actual data extraction. While other BI tools have centralized semantic layers that require skilled IT resources, we’ve experienced that those can lead to bottlenecks and limited agility. With Looker’s semantic layer, LookML, our BI Team, led by Peter Donald, can easily build upon their SQL knowledge to add both a high degree of control as well as flexibility to our data model.  The fully browser-based development environment allows the data team to rapidly develop, test, and deploy code and is backed by robust and seamless Git source code management. 

In parallel, LookML empowers  business users to collaborate without the need for advanced SQL knowledge. Our data team curates interactive data experiences with Looker to help scale access and adoption. Business users can explore ad hoc analysis, create dashboards, and develop custom data experiences in the web-based environment to get the answers they need without relying on IT resources each time they have a new question, while also maintaining the confidence that the underlying data will always be accurate. This helps us meet our primary goal of providing all businesses users with the data access they need to monitor the pulse of key metrics in near real-time.

Impact and future of DTC BI at Constellation

In short order, taking a modern and integrated approach to the DTC technology stack has delivered economic impact across the portfolio, helping our team understand and combat customer churn, increase conversion rates, and optimize the customer acquisition cost (CAC) and customer lifetime value (CLV) ratios. Perhaps most important is the benefit it can provide to the customer base. Mining customer data and consumer behavior generates data into what our customers are seeking, giving us insights to supply more, or less of it. For example, observing sales velocity and conversion rates by SKU or by region can help us better understand changes in customer taste profiles and fluctuations in demand, providing the foundation for a more powerful innovation pipeline and more effective sales and distribution tactics in wholesale. Our team has also been an early pilot tester for Looker’s new integration with Customer Match, which contributes to the virtuous cycle between data insight and data activation. In the future, our plan is to leverage this cycle to amplify the impact of Google Ads across Search, Shopping, and YouTube placements for the wine and spirits portfolio. 

The operational impact of Looker is also substantial: our team estimates that the number of hours needed to reach critical business decisions has been reduced by nearly 60%, boosting productivity and accelerating the daily operating rhythm. A thoughtfully curated technology stack together with a modern BI solution allows us to stay at the vanguard of the industry. While the DTC sales channel is not designed to surpass the core business of wholesale for Constellation in terms of size, the approach enables unparalleled insights and measurement abilities that will pay dividends for the entire business for years to come.