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.

Today we’re announcing the HarbourBridge Schema Assistant, which provides a guided schema-design workflow for migrating from MySQL or PostgreSQL to Spanner. HarbourBridge imports dump files (from mysqldump or pg_dump) or directly connects to your source database, and converts the source database schema to an equivalent Spanner schema. The new Schema Assistant capability displays the source schema and Spanner schema side-by-side, highlights errors and walks you through a series of steps to validate and optimize your Spanner schema. It also produces a browsable assessment report with an overall migration-fitness score for Spanner, a table-by-table detailed analysis of type mappings and a list of features used in the source database that aren’t supported by Spanner. It supports editing of table and column names, column types, primary keys and constraints, as well as dropping of tables, columns, foreign keys and secondary indexes.

The new Schema Assistant complements HarbourBridge’s existing data and schema migration capabilities and is a critical step towards our goal of building a complete open-source migration toolkit. HarbourBridge continues to support command-line schema and data migration and turn-key Spanner evaluation.

Complementing the bulk data migration capabilities of HarbourBridge, we are also announcing the ability to migrate change events from MySQL to Cloud Spanner.

Supported Features in Schema Assistant

1. Global type mapping. Users can customize the global mapping for how types should be mapped to Spanner consistently across the schema. For example, mapping large integers in source schema to Spanner’s NUMERIC.
2. Local type mapping. Users can override the custom type mapping for a given table/column.
3. Session management. A session keeps track of all the changes made to the schema mapping.
4. Customization of secondary indexes. Users can add, edit and delete secondary indexes to optimize their Spanner performance.
5. Customization of foreign keys and interleaved tables. Table interleaving is an important design consideration when migrating to Cloud Spanner as explained in more detail in this blog post.

Features in the pipeline

We are already working to further expand the supported set of schema editing features and welcome your feedback. We are particularly excited to expand the Schema Assistant’s design recommendations for optimizing Spanner schemas e.g. in-depth recommendations for primary key design.

HarbourBridge is open source and we gladly accept contributions from the wider community.

Enterprises expect extreme reliability of the database infrastructure that’s accessed by their applications. Despite your best intentions and careful engineering, database errors happen, whether that’s machine crashes or network partitioning. Good planning can help you stay ahead of problems and recover more quickly when issues do occur.

This blog shows one approach of deploying a database architecture that implements high availability and disaster recovery for MySQL on Compute Engine, using regional disks as well as load balancers.

Any database architecture must provide approaches to tolerate errors and recover from those errors quickly without losing data. These approaches are expressed in RTO (recovery time objective) and RPO (recovery point objective), which offer ways to set and then measure how long a service can be unavailable, and how far back data should be saved.

After a database error, a database must recover as fast as possible with an RTO as small as possible, ideally in seconds. There must be as little data loss as possible—ideally, none at all. The desired RPO is the last consistent database state.

From a database architecture and deployment viewpoint, this can be accomplished with two distinct concepts: high availability and disaster recovery. Use both at the same time in order to achieve an architecture that’s prepared for the widest range of errors or incidents.

Creating a resilient database architecture

A high-availability database architecture has database instances in two or more zones. If a server on a zone fails, or the zone becomes inaccessible, the instances in other zones are available to continue the processing. The figure below shows two instances, one in zone zn1, and one in zone zn2. The load balancer in front supports directing traffic to a healthy database instance available for read and write queries.

A disaster recovery architecture adds a second high-availability database setup in a second region. If one of the regions becomes inaccessible or fails, the other region takes over. The figure below shows two regions, primary and DR. Data is replicated from the primary to the DR region so that the DR region can take over from the latest consistent database state. The load balancer in front of the regions directs traffic to the region in charge of the read and write traffic. Here’s how this architecture looks:

In addition to the database instance setup, a regional disk is deployed so that data is written simultaneously in two zones, proving fail-safe in the event of zone failure. This is a huge advantage of Google Cloud, allowing you to skip MySQL-level replication within a region. Each write operation to disk is done in two zones synchronously. When the primary instance fails, a standby instance is mounted with regional persistent disk(s), and the database service (MySQL) is then started using the same. This brings the peace of mind of not worrying about replication lag or database state for high availability.

From a disaster recovery process view, the following happens over time during a failure situation:

• Normal steady state database operation
• A failure happens and a region becomes unavailable or the database instance inaccessible
• A decision must be made to fail over or not (in case there is the expectation that the region becomes available soon enough or the instance becomes responsive again)
• DNS is updated manually, therefore it redirects application traffic to a second region
• Fallback to the primary region after it becomes available again is optional, as the second region is a fully built-out deployment

From a high-availability process view, the following happens over time during a failure situation:

• Normal steady state database operation
• Database instance fails or becomes unavailable
• Launch the standby instance
• Mount regional SSD and start database
• Automatic redirection of application traffic to the standby via load balancer
• After the failed or unavailable instance becomes available again, a fallback can take place or not

The database architecture shown demonstrates a highly available architecture supporting disaster recovery. With regional disks and load balancers, it is straightforward to provide a resilient database deployment.

Find out more about load balancers and regional disks. Check out general HA and DR processes and detailed steps in the initial part of the reference guide. Try it out to become familiar with the architecture as well as the two major failover processes.

Education data can inform strategy, enhance both teaching and learning, and help us better understand customer needs. But that data is only as useful as an organization’s ability to access, analyze, and act upon it. Education Technology (EdTech) companies with a strong data warehouse management policy can help to make the most of educational data at all levels.

Creating a data team

When an EdTech doesn’t have a dedicated data team, databases will likely be poorly maintained and data can be hard to find, it can lack critical security, or fail to meet a customer’s specific requirements. A dedicated data team can prevent this and better support an organization’s data needs.  There’s a correlation between the maturity of a data team and the success of an organization’s data utilization. When considering the three primary components of team, outlook, and results, we can better understand an organization’s maturity. By using this matrix from Looker’s Analytical Maturity Playbook, you can assess the maturity of your organization’s data team. Most would agree, the goal is for your operation to move from data chaos to data-driven. But, how can you do that? Think about your data this way: the intelligence you gain from your data is not only an asset…it’s a product. When your data team views your business intelligence as a product—one that’s just as important as the services you offer—they can set the stage for creating a successful data-driven company. Mature data organizations:

• Report functionality regularly
• Constantly assess potential improvements to existing systems
• Manage content rollout to internal employees

Redivis: Making data accessible, connected & secure

Redivis is a platform that connects academic researchers with data and the tools to understand education intelligence. Their customers, who are researchers, expressed how difficult it is to find new datasets for their studies. Once they found a dataset, they often had to access and work with it before they could tell if the information would be useful for their studies. Redivis Data administrators were also concerned about data security. Redivis built their platform on top of Google Cloud’s security infrastructure, making it a highly scalable data processing environment. 

Redivis developed a transparent, tiered access system for datasets that allows researchers to request separate access to a dataset’s documentation to determine whether they can use the dataset. Detailed audit logs (supported by Google Cloud Logging) and robust application-level security controls tighten data access. To offer more insights for their researchers, they securely connected their private datasets with public datasets hosted in BigQuery allowing researchers to run queries on billions of records in seconds, not hours.

With a data team in place, it’s time to visualize where to store and organize your data. Data is an asset. You want to invest in collecting, operationalizing, and storing it properly. “Inadequate data aggregation, harmonization, and processing impedes teams from making the right data-driven decisions and pivoting when needed,” according to Jesus Trujillo Gomez, a strategic business executive at Google Cloud. You can determine how mature your warehouse is by using this matrix to help you identify where your organization falls on the spectrum (least mature on the left, most mature on the right). A company’s data warehouse can be assessed in four areas: Technology, Pipeline, Quality, and Performance. Here are some suggested steps to bring your EdTech company one step closer to a data powerhouse. 

Classcraft: Using a data-driven approach to make learning fun

Launched in 2014, learning company Classcraft is reimagining the classroom, creating better learning environments with gamification. To support their mission, their team has created a complete data platform that uses data analytics to find new ways to engage students in the classroom. Classcraft became a Google for Education Build Partner to keep up with rising demand. They expanded their footprint to reach 6 million students and educators in 165 countries. 

They also migrated to Google Cloud, building on several integrations with Google Workspace for Education, Google Classroom, and Chrome for Education, allowing them to support customers ranging from individual teachers to entire school districts.  Classcraft migrated its database and analytics infrastructure to BigQuery and Cloud Storage. “We capture about 10 million new data points every month related to school culture and student performance,” says Chief Technology Officer and VP of Product Stéphane Guillemette. “With BigQuery and other Google Cloud tools, we can quickly deliver valuable insights to educators.”