Editor’s note: In honor of Mother’s Day, we look at how 1-800-FLOWERS.COM, Inc. migrated to Google Cloud as part of its digital transformation to quickly deploy seamless and convenient customer experiences across multiple brands on Mother’s Day and every day. 

As a leading provider of gifts designed to help customers express, connect, and celebrate,  1-800-FLOWERS.COM, Inc. has embraced cloud technologies to grow and transform its business through constant innovation. As part of our digital transformation, we recently completed the migration of our ecommerce platform and other services to Google Cloud. We’ve transitioned from a monolithic to a microservices platform, moved many workloads from our on-premises data centers to our Google Cloud environment, and scaled both horizontally and vertically. 

Since our migration, we’ve developed efficient processes to launch new brands, improved the customer experience across all brands, and seen significantly increased site traffic. 

Nurturing a more delightful customer journey

Customer delight is at the core of everything we do. Whether it be with a flower bouquet, a sweet treat, or a personalized keepsake, our mission is to deliver smiles. With the rise of the COVID-19 pandemic, we’ve all been challenged to find unique and safe ways to continue honoring the special connections in our lives and celebrating occasions with loved ones. Our customers have adapted by doing things such as sending gifts to isolated loved ones, sharing the same meal together virtually, or using video to engage with others through group activities like flower arranging and building charcuterie boards. 

The customer experience is a top priority for us, and we constantly look for innovative ways to enhance the customer journey across our ecommerce platform of more than a dozen brands. As a result, we’ve continued to see a rise in demand as customers enjoy the ease and convenience of our site and discover our full family of brands. 

Migrating to a cloud-first mindset

As we’ve continued to innovate and iterate on the customer experience, we knew we wanted to evolve our platform. We wanted to shift to a microservices platform, which would allow our team the opportunity to release updates to our site more often and set up the right continuous integration/continuous deployment (CI/CD) practices. 

Working with Google Cloud, we were able to move our ecommerce platform to the cloud and standardize our site and brand deployment by building one release that could then be repeated across all of our brands. We built everything in a modular fashion, including microservices and      code libraries, so that sites could be easily constructed and replicated for each brand. And because of this, we were able to launch Shari’s Berries extremely quickly after we acquired the brand in 2019.   

Moving our platform to a completely homegrown solution of microservices was a daunting task. But our team handled it beautifully through load-testing, stress-testing, and building new monitoring tools. And with Google Cloud supporting us all along the way, managing the migration process was simple and easy from start to finish. 

Arranging a better bouquet of services

Currently, we’ve migrated every customer-facing touchpoint for all of our brands to Google Cloud—whether it’s on the web or mobile, our AI bots, or our chat interfaces. 

• We run on Google Kubernetes Engine and Istio.
• We have nearly 200 microservices built to help power our entire ecommerce stack across several cloud services running on Google Cloud. 
• We’re utilizing BigQuery for our offline intelligence.

Results are coming up roses

Our new stack on Google Cloud has benefits for both us and our customers. We moved from a session-based to a token-based system, which provides enhanced security as well as a consistent, convenient experience across all our brands. Using service workers and a single-page app, we are able to download all the relevant site content to the browser in under two seconds to create an instant-click experience for each and every customer. We also use Google Analytics to measure our user interactions and provide personalized results to each customer. Our hope is that with this new system, we can learn from customer behavior to offer gift givers a more personalized shopping experience during each visit. 

The benefits of our new tech stack have not only helped us enhance the solutions we offer to customers today, they’ve also enabled us to offer new ones at lightning speed. With our legacy system, we used to release new code once a week or once a month. Now, even during our peak periods, we’re able to release 10 to 15 times a day and can deploy and pivot quickly to create new microservices and microsites on the fly—often without having to touch any code. 

Efficiencies abound     

The benefits of moving our platform to Google Cloud have extended to our internal teams as well. Before the migration, we had only two environments for developing and testing, which made it time-consuming to test updates before they went into production. Now with Google Cloud, we have several different journey teams—which are made up of developers, product owners, and technical owners—all working in several different environments, solving problems, and creating new solutions together. 

Everyone is now empowered to be self-sufficient, developing and releasing microservices on their own when they’re ready. This has given our developers more time to take part in continued development and learning opportunities. For example, we offer lunch-and-learn sessions as well as other resources for everyone to take advantage of so they can continue to learn and refine their skills.

Planting the seeds for future growth

As we look to the future and think about how we help our customers express, connect, and celebrate, we’ll continue to collaborate across teams to deliver solutions that spread smiles. Specifically, we’re exploring additional use of AI to help us better serve our customers across all our brands. 

We’ve enjoyed the ongoing support we’ve received from the Google Cloud team as they help us build new solutions and design a road map for the future. Their support has helped the 1-800-FLOWERS.COM, Inc. team to realize the power of the cloud and bring the very best experience to our customers.

Learn more about 1-800-FLOWERS.COM, Inc., or check out our recent blog about cloud migration for the real world.

About Grofers

Grofers is an India-based local mobile e-commerce platform that delivers groceries, fruits and vegetables, cosmetics and electronics. Customers use Grofers’ mobile app to shop for products from local stores in 17 cities in India.

Industries: Services

Location: India

India-based Grofers is a hyperlocal mobile platform that delivers groceries, fruits and vegetables, cosmetics and electronics. Customers use Grofers’ mobile app to shop for products from local stores in 17 cities in India.

In 2015, the number of deliveries Grofers fulfilled was growing at the rate of 10 times a month, and its delivery system struggled under the stress. When traffic spiked on weekends, its database frequently crashed. Fulfilling and delivering orders depended on manual processes such as telephone calls. Grofers needed a solution that would allow it to quickly scale and improve its delivery process.

Building an entire backend in two weeks

When Grofers launched in 2013, the logistics system was entirely manual. Staff at the headquarters called delivery drivers with order details and delivery addresses. Drivers then decided what order to pick up goods from local merchants. With a small number of customers and orders, that wasn’t a problem. But the manual system couldn’t handle growth. Grofers recognized it had to automate the system.

To do that, it turned to Google Cloud Platform. Grofers built a new backend for its delivery system using Google App Engine. When an order arrives, the new system looks at the status of all drivers — including their current location, the order he’s completing, and the pickup and delivery locations on the route. Based on that information, the system calculates the optimal driver for each order and sends the driver the order via a mobile app.

The system also takes advantage of Google Cloud Endpoints, which generates libraries for Javascript, iOS and Android. Grofers used Google Cloud Endpoints to create mobile apps to communicate with its delivery staff.

Scaling fast, dramatically improving delivery metrics

With the new backend and mobile apps, Grofers can scale quickly to keep up with its rapid growth. Grofers now delivers 70,000 orders per day, up from 10,000 before the GCP deployment, and has had 100 percent uptime. Each driver handles an average of 15 orders per day, compared to only five previously, letting Grofers handle more work with less staff. All this has been accomplished at very low cost: Prakash says that GCP costs account for only two percent of the company’s infrastructure spending. Without GCP, Prakash says the backend would be far more expensive and account for up to 50 percent of the company’s infrastructure expenses.

Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing. And, they need to plan for the future – how will more data be handled and how new downstream teams will be supported?

Checkout how Independence Health Group is addressing their enterprise data warehouse (EDW) migration in the video above.

Why BigQuery?

On-premises data warehouses become difficult to scale so most companies’ biggest goal is to create a forward looking system to store data that is secure, scalable and cost effective. GCP’s BigQuery is serverless, highly scalable, and cost-effective and is a great technical fit for the EDW use-case. It’s a multicloud data warehouse designed for business agility. But, migrating a large, highly-integrated data warehouse from on-premise to BigQuery is not a flip-a-switch kinda migration. You need to make sure your downstream systems dont break due to inconsistent results in migrating datasets, both during and after the migration. So..you have to plan your migration. 

Data warehouse migration strategy

 The following steps are typical for a successful migration: 

• Assessment and planning: Find the scope in advance to plan the migration of the legacy data warehouse 
  • Identify data groupings, application access patterns and capacities
  • Use tools and utilities to identify unknown complexities and dependencies 
  • Identify required application conversions and testing
  • Determine initial processing and storage capacity for budget forecasting and capacity planning 
  • Consider growth and changes anticipated during the migration period 
  • Develop a future state strategy and vision to guide design
• Migration: Establish GCP foundation and begin migration
  • As the cloud foundation is being set up, consider running focused POCs to validate data migration processes and timelines
  • Look for automated utilities to help with any required code migration
  • Plan to maintain data synchronization between legacy and target EDW during the duration of the migration. This becomes a critical business process to keep the project on schedule.
  • Plan to integrate some enterprise tooling to help existing teams span both environments
  • Consider current data access patterns among EDW user communities and how they will map to similar controls available in Big Query. 
  • Key scope includes code integration and data model conversions
  • Expect to refine capacity forecasts and refine allocation design. In Big Query there are many options to balance cost and performance to maximize business value. For example, you can use either on-demand or flat-rate slot pricing or a combination of both. 
• Validation and testing
  •  Look for tools to allow automated, intelligent data validation 
  • Scope must include both schema and data validation
  • Ideally solutions will allow continuous validation from source to target system during migration
  • Testing complexity and duration will be driven by number and complexity of applications consuming data from the EDW and rate of change of those applications 

A key to successful migration is finding Google Cloud partners with experience migrating EDW workloads. For example, our Google Cloud partner Datametica offers services and specialized Migration Accelerators for each of these migration stages to make it more efficient to plan and execute migrations.

Data warehouse migration: Things to consider

• Financial benefits of open source: Target moving to ‘Open Source’ where none of the services have license fees. For example BigQuery uses Standard SQL; Cloud Composer is managed Apache Airflow, Dataflow is based on Apache Beam. Taking these as managed services provides the financial benefits of open source, but avoids the burden of maintaining open source platforms internally. 
• Serverless: Move to “serverless” big data services. The majority of the services used in a recommended GCP data architecture scale on demand allowing more cost effective alignment with needs. Using fully managed services lets you focus engineering time on business roadmap priorities, not building and maintaining infrastructure. 
• Efficiencies of a Unified platform: Any data warehouse migration involves integration with services that surround the EDW for data ingest and pre-processing and advanced analytics on the data stored in the EDW to maximize business value. A cloud provider like GCP offers a full breadth of integrated and managed ‘big data’ services with built-in machine learning. This can yield significantly reduced long-term TCO by increasing both operational and cost efficiency when compared to EDW-specific point solutions. 
• Establishing a solid cloud foundation: From the beginning, take the time to design a secure foundation that will serve the business and technical needs for workloads to follow. Key features include: Scalable Resource Hierarchy, Multi-layer security, multi-tiered network and data center strategy and automation using Infrastructure-as-Code. Also allow time to integrate cloud-based services into existing enterprise systems such as CI/CD pipelines, monitoring, alerting, logging, process scheduling, and service request management. 
• Unlimited expansion capacity: Moving to cloud sounds like a major step, but really look at this as adding more data centers accessible to your teams. Of course, these data centers offer many new services that are very difficult to develop in-house and provide nearly unlimited expansion capacity with minimal up-front financial commitment. . 
• Patience and interim platforms: Migrating an EDW is typically a long running project. Be ready to design and operate interim platforms for data synchronization, validation and application testing. Consider the impact on up-stream and down-stream systems. It might make sense to migrate and modernize these systems concurrent with the EDW migration since they are probably data sources and sinks and may be facing similar growth challenges. Also be ready to accommodate new business requirements that develop during the migration. Take advantage of the long duration to have existing your operational teams learn new services from the partner leading the deployment so your teams are ready to take over post-migration. 
• Experienced partner: An EDW migration can be a major undertaking with challenges and risks during migration, but offers tremendous opportunities to reduce costs, simplify operations and offer dramatically improved capacities to internal and external EDW users. Selecting the right partner reduces the technical and financial risks, and allows you to plan for and possibly start leveraging these long-term benefits early in the migration process.

Example Data Warehouse Migration Architecture

• Setup foundational elements. In GCP these include, IAM for authorization and access, cloud resource hierarchy, billing, networking, code pipelines, Infrastructure as Code using Cloud Build with Terraform ( GCP Foundation Toolkit), Cloud DNS and a dedicated/partner Interconnect to connect to the current data centers.
• Activate monitoring and security scanning services before real user data is loaded using Cloud Operations for monitoring and logging and Security Command Center for security monitoring. 
• Extract files from on-premise legacy EDW and move to Cloud Storage and establish on-going synchronization using Big Query Transfer services. 
• From Cloud Storage, process the data in Dataflow and Load/Export data to BigQuery. 
• Validate the export using Datametica’s validation utilities running in a GKE cluster and Cloud SQL for auditing and historical data synchronization as needed. Application teams test against the validated data sets throughout the migration process. 
• Orchestrate the entire pipeline using Cloud Composer, integrated with on-prem scheduling services as needed to leverage established processes and keep legacy and new systems in sync. 
• Maintain close coordination with teams/services ingesting new data into the EDW and down-streams analytics teams relying on the EDW data for on-going advanced analytics. 
• Establish fine-grained access controls to data sets and start making the data in Big Query available to existing reporting, visualization and application consumption tools using BigQuery data connectors for ‘down-stream’ user access and testing. 
• Incrementally increase Big Query flat-rate processing capacity to provide the most cost-effective utilization of resources during migration. 

To learn more about migrating from on-premises Enterprise Data Warehouses (EDW) to Bigquery and GCP here.