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Behind the Scenes: How eBay Provides its Customers New Shopping Experiences
“If it exists in the world, you are likely to find it on eBay.” So they say. With 180 million buyers and a global presence in over 190 markets, it’s probably true. A company that emerged out of the ashes of the dot-com bubble, eBay today is one of the most renowned organizations in the world.
eBay catalog has over 1 billion listings available worldwide at any given point in time and over 15 million listings are added on a daily basis. That essentially means many datasets from different sources. And that was a challenge.
The company needed a scalable and flexible platform to efficiently enable new shopping experiences globally, showcasing unique eBay inventory. It needed to iterate quickly and integrate data-heavy AI technologies.
eBay turned to Cloud Bigtable from Google. Today, Cloud Bigtable handles eBay’s global catalog with billions of listings, scaling to hundreds of terabytes with many millions of reads and writes, and gigabytes of data transferred in and out every second.
This helped eBay synchronize its entire catalog over GCP in near real-time with low latency and high consistency. It also enabled the company to serve catalog data to other services on GCP with high read volume and low latency and keep and serve all listing images in GCP along with AI vision signatures.
Find out how.
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How to Move From Redshift to BigQuery Easily
Enterprise data warehouses are getting more expensive to maintain. Traditional data warehouses are hard to scale and often involve lots of data silos. Business teams need data insights quickly, but technology teams have to grapple with managing and providing that data using old tools that aren’t keeping up with demand. Increasingly, enterprises are migrating their data warehouses to the cloud to take advantage of the speed, scalability, and access to advanced analytics it offers.
With this in mind, we introduced the BigQuery Data Transfer Service to automate data movement to BigQuery, so you can lay the foundation for a cloud data warehouse without writing a single line of code. Earlier this year, we added the capability to move data and schema from Teradata and S3 to BigQuery via the BigQuery Data Transfer Service. To help you take advantage of the scalability of BigQuery, we’ve now added a service to transfer data from Amazon Redshift, in beta, to that list.
Data and schema migration from Redshift to BigQuery is provided by a combination of the BigQuery Data Transfer Service and a special migration agent running on Google Kubernetes Engine (GKE), and can be performed via UI, CLI or API. In the UI, Redshift to BigQuery migration can be initiated from BigQuery Data Transfer Service by choosing Redshift as a source.
The migration process has three steps:
- UNLOAD from Redshift to S3—The GKE agent initiates an UNLOAD operation from Redshift to S3. The agent extracts Redshift data as a compressed file, which helps customers minimize the egress costs.
- Transfer from S3 to Cloud Storage—The agent then moves data from Amazon S3 to a Cloud Storage bucket using Cloud Storage Transfer Service.
- Load from Cloud Storage to BigQuery—Cloud Storage data is loaded into BigQuery (up to 10 million files).
Watch the video to learn more!
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Replacing Oracle with Cloud Spanner: What Optiva Learnt
Optiva is a Canada-based provider of business support system (BSS) and Operations Support Systems (OSS) software and services to the telecommunications vertical.
Optiva sought to improve its product offering. In the way were very tough challenges including a need for accuracy, the need to deal with very high volumes, the need for low latency.
So they looked at the market. And just by chance, they saw a Cloud Spanner press release.” And so we were looking. We were like: what can we do? How can we make this 10 times faster. And we read the Spanner press release, and we’re like: are you kidding me? This is our dream come true. We have a perfect database that can handle the transactional volume. It’s distributed, it’s consistent, multiple writings, like synchronized writing across 1,000 servers. It’s exactly what we needed to replace big bad Oracle,” says Danielle Roystone, CEO of Optiva.
To hear the entire story, watch the video!
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Quick Tips to Get the Most Out of Cloud Spanner
Today, IT Admins and DBAs are inundated with thankless tasks. But they no longer have to deal with that. With Cloud Spanner, they can focus on value-add and innovation instead of maintenance. Creating or scaling a globally replicated database for mission-critical apps now takes a handful of clicks.
Industry-leading high-availability and Google-grade security as defaults, not expensive add-ons, help ensure your apps stay online and more secure.
Cloud Spanner is a powerful product, but many users do not maximize its benefits.
It is the first scalable, enterprise-grade, globally-distributed, and strongly consistent database service built for the cloud specifically to combine the benefits of relational database structure with a non-relational horizontal scale.
This combination delivers high-performance transactions and strong consistency across rows, regions, and continents with an industry-leading 99.999% availability SLA, no planned downtime, and enterprise-grade security. Cloud Spanner revolutionizes database administration and management and makes application development more efficient.
This video highlights best practices, strategies for optimizing applications and workloads, and ways to improve performance and scalability. This live demos shows real-time speed-ups of transactions, queries, and overall performance.
Additionally, this talk explores techniques for monitoring Cloud Spanner to identify performance bottlenecks. Watch now to learn more.
BigQuery Omni: A Game-Changer for Cross-Cloud Data Analysis
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Research shows that over 90% of large organizations already deploy multicloud architectures, and their data is distributed across several public cloud providers. Additionally, data is also increasingly split across various storage systems such as warehouses, operational and relational databases, object stores, etc. With the proliferation of new applications, data is serving many more use cases such as data sciences, business intelligence, analytics, streaming and the list goes on. With these data trends, customers are increasingly gravitating towards an open multicloud data lake. However, multicloud data lakes present several challenges such as data silos, data duplication, fragmented governance, complexity of tools, and increased costs.
With Google’s data cloud technologies, customers can leverage the unique combination of distributed cloud services. They can create an agile cross-cloud semantic business layer with Looker and manage data lakes and data warehouses across cloud environments at scale with BigQuery and capabilities like BigLake and BigQuery Omni.
BigLake is a storage engine that unifies data warehouses and lake houses by standardizing across different storage formats including BigQuery managed table and open file formats such as Parquet and Apache Iceberg on object storage. BigQuery Omni provides the compute engine that runs locally to the storage on AWS or Azure, which customers can use to query data in AWS or Azure seamlessly. This provides several key benefits such as:
- A single pane of glass to query your multicloud data lakes (across Google Cloud Platform, Amazon Web Services, and Microsoft Azure)
- Cross-cloud analytics by combining data across different platforms with little to no egress costs
- Unified governance and secure management of your data wherever it resides
In this blog, we will share cross-cloud analytics use cases customers are solving with Google’s Data Cloud and the benefits they are realizing.
Unified marketing analytics for 360-degree insights
Organizations want to perform marketing analytics – ads optimization, inventory management, churn prediction, buyer propensity trends and many more such analytics. To do this before BigQuery Omni, customers had to use data from several different sources such as Google Analytics, public datasets and other proprietary information stored across cloud environments. This requires moving large amounts of data, managing duplicate copies and incremental costs to perform any cross-cloud analytics and derive actionable insights. With BigQuery Omni, organizations are able to greatly simplify this workflow. Using the familiar BigQuery interface, users can access data residing in AWS or Azure, discover and select just the relevant data that needs to be combined for further analysis. This subset of data can be moved to Google Cloud using Omni’s new Cross-Cloud Transfer capabilities. Customers can combine this data with other Google Cloud datasets and these consolidated tables can be made available to key business stakeholders through advanced analytics tools such as Looker and Looker Studio. Customers are also able to tie in this data now with world class AI models via Vertex AI.
As an illustrative example, consider a retailer who has sales & inventory, user and search data spread across multiple data silos. Using BigQuery Omni they can seamlessly bring these datasets together and power several marketing analytics scenarios like customer segmentation, campaign management and demand forecasting etc.
“Interested in performing cross-cloud analytics, we tested BigQuery Omni and really liked the SQL support to easily get data from AWS S3. We have seen great potential and value in BigQuery Omni for adopting a multi-cloud data strategy.” — Florian Valeye, Staff Data Engineer, Back Market, a leading online marketplace for renewed technology based out of France
Data platform with consistent and unified cross-cloud governance
Another pattern is customers looking to analyze operational, transactional and business data across data silos in different clouds through a unified data platform. These data silos are a result of various factors such as merger and acquisitions, standardization of analytical tools, leveraging best of breed solutions in different clouds and diversification of data footprint across clouds. In addition to a single pane of glass for data access across silos, customers deeply desire consistent and uniform governance of their data across clouds.
“Achieve is looking to deliver a consistent analytics experience to all our customers and stakeholders. With our financial and credit report data distributed across clouds, accessing and getting insights holistically is difficult. Through our exploration with Omni, we are able to access datasets in different clouds using a single familiar BigQuery interface; we see its promise as one of the primary tools in our multi-cloud platform.” — James Simonson, Senior Data Engineer, Achieve
With BigLake and BigQuery Omni abstracting the storage and compute layers respectively, organizations can access and query their data in Google Cloud irrespective of where it resides. They can also set fine-grained row level and column access policies in BigQuery and consistently govern it across clouds. These building blocks enable data engineering teams to build a unified and governed data platform for their data users without having to deal with the complexity of building and managing complex data pipelines. Furthermore, with BigQuery Omni’s integration with Dataplex and Data Catalog, you can discover, search your data across clouds and enrich your data by adding relevant business context with business glossary and rich text.
“Several SADA customers use GCP to build and manage their data analytics platform. During many explorations and proofs of concepts, our customers have seen the great potential and value in BigQuery Omni. Enabling seamless cross-cloud data analytics has allowed them to realize the value of their data quicker while lowering the barrier to entry for BigQuery adoption in a low-risk fashion.” — Brian Suk, Associate Chief Technology Officer, SADA, one of the strategic partners of Google Cloud.
Simplified data sharing between data providers and their customers
A third emerging pattern in cross cloud analytics is data sharing. Several services have the business need to share information such as inventory data, subscriber data to their customers or users who in turn analyze or aggregate the data with their proprietary data and oftentimes share the results back with the service provider. In several cases, the two parties are on different cloud environments, requiring them to move data back and forth.
Consider a company operating in the customer data platform (CDP) space. CDPs were designed to help activate customer data, and a critical first step of that was unifying and managing that customer data. To enable this, many CDP vendors built their solution choosing one of the available cloud infrastructure technologies and copied data from the client’s systems.“Copying data from client applications and infrastructure has always been a requirement to deploy a CDP, but it doesn’t have to be anymore” — Justin DeBrabant, Senior Vice President of Product, ActionIQ.
While a small percentage of customers are fine with moving data across cloud environments, the majority are hesitant to onboard new services and would rather prefer providing governed access to their data sets.
“A new architectural pattern is emerging, allowing organizations to keep their data at one location and make it accessible, with the proper guardrails, to applications used by the rest of the organization’s stack” adds Justin at ActionIQ.
With BigQuery Omni, services in Google Cloud Platform can more easily access and share data with their customers and users in other cloud environments with limited data movement. One of UK’s largest statistics providers has explored Omni for their data sharing needs.
“We tested BigQuery Omni and really like the ability to get data from AWS directly into BQ. We’re excited about managing data sharing with different organizations without onboarding new clouds” – Simon Sandford-Taylor, Chief Information and Digital Officer, UK’s Office for National Statistics
With BigQuery Omni, customers are able to:
- Access and query data across clouds through a single user interface
- Reduce the need for data engineering before analyzing data
- Lower operational overhead and risks by deploying an application that runs across multiple clouds which leverages the same, consistent security controls
- Accelerate access to insights by significantly reducing the time for data processing and analysis
- Create consistent and predictable budgeting across multiple cloud footprints
- Enable long term agility and maximize the benefits every cloud investment
Over the last year, we’ve seen great momentum in customer adoption and added significant innovations to BigQuery Omni including improved performance and scalability for querying your data in AWS S3 or Azure Blob Storage, Iceberg support for Omni, Larger query result set size up to 20GB and Cross-cloud transfer that helps customers easily, securely, and cost effectively move just enough data across cloud environments for advanced analytics.
BigQuery Omni has launched several features to support unified governance of your data across multiple clouds – you can get fine-grained access to your multi-cloud data with row level and column level security. Building on this, we are excited to announce that BigQuery Omni now supports data masking. We’ve also made it easy for customers to try and see the benefits of BigQuery Omni through the limited time free trial available until March 30, 2023.
BigQuery Omni running on other public clouds outside of Google Cloud is available in AWS US East1 (N.Virginia) and Azure US East2 (US East) regions. We are also excited to share that we will be bringing BigQuery Omni to more regions in the future, starting with Asia Pacific (AWS Korea) coming soon.
Getting Started
Get started with a free trial to learn about Omni. Check out the documentation to learn more about BigQuery Omni. You can also leverage the self paced labs to learn how to set up BigQuery Omni easily.
Making Mothers’ Day Special: How Google Cloud Migration for 1-800-FLOWERS.COM, Inc Impacts CX
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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.
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