Editor’s note: We’re hearing here how martech provider Wunderkind easily met the scaling demands of their growing customer base on multiple use cases with Cloud Bigtable and other Google Cloud data solutions.

Wunderkind is a performance marketing channel and we mostly have two kinds of customers: online retailers, and publishers like Gizmodo Media Group, Reader’s Digest, The New York Post and more. We help  retailers boost their e-commerce revenue through real-time messaging solutions designed for email, SMS, onsite, and advertising. Brands want to provide a one-to-one experience to more of their customers, and we use our extensive history with best practices in email marketing and technology to help brands reach more customers through targeted messaging and personalized shopping experiences. With  publishers, it’s a different value proposition, we use the same platform to provide a non disruptive and personalized ad experience for their website. For example, if you are on their site and then you left, we might show an ad tailored to you when you come back later – depending on the campaign. 

After running into limitations with our legacy database system, we turned to Cloud Bigtable and Google Cloud, which helped us be more flexible and easily scale for high traffic demand – which can be a stable 40,000 requests per second, and meet the needs of our growing number of data use cases. 

Three different databases power our core product

In our core offering, companies send us user events from their websites. We store these events and later decide (using our secret sauce) if and how to reach out to those users on behalf of our customers. Because many of our customers are retailers, Black Friday and Cyber Monday are big traffic days for us as. On such days, we can get 31 billion events, sometimes as many as  200K events per second. We show 1.6 billion impressions that have seen close to 1 billion pageviews. And at the end of all this, we securely send about 100 million emails. We noticed the same thing for election time; traffic reached the same high volume. We need scalable solutions to support this level of traffic as well as the elasticity to let us pay only for what we use, and that’s where Google Cloud comes in.

So how does this work? Our externally facing APIs, which are running on Google Kubernetes Engine, receive those user events—up to hundreds of thousand per second. All the components in our architecture need to be able to handle this demand. So from our APIs, those events go to Pub/Sub, Dataflow and from there they are written to Bigtable and BigQuery, Google Cloud’s serverless, and highly scalable data warehouse. This business user activity data underpins almost all our products. Events can be things like product views or additions to shopping carts. When we store this data in Bigtable, we use a combination of email address and the customer ID as the Bigtable key and we record the event details in that record. 

What do we do with this information next? It’s important to mention that we also mark the last time we received an event about a user in Memorystore for Redis, Google Cloud’s fully managed Redis service. This is important because we have another service that is periodically checking Memorystore for users that have not been active for a campaign-specific period of time (it can be 30 minutes, for example), then deciding whether to reach out to them.

How we decide when we reach out is an intelligent part of our product offering, based on the channel, message, product, etc. When we do reach out, we use Memorystore for Redis as a rate limiter or token bucket. In order not to overwhelm the email or texting providers we send API requests to, we throttle those requests using Memorystore. (We prefer to preemptively throttle the outgoing API requests as opposed to handling errors later.)

When we do reach out, often we will need details for a specific product—let’s say if the website belongs to a retailer. We usually get that information from the retailer through various channels and we store product information in Cloud SQL for MySQL. We pull that information when we need to send an email with product information, and we use Memorystore for Redis to cache that information, since many of the products are repeatedly called. Our Cloud SQL instance has 16 vCPUs, 60GBs of memory and 0.5TB of disk space and when we perform those product information updates, we have about a thousand write transactions per second. We are also in the process of migrating some tables from a self managed MySQL instance, and we keep those tables synchronized with Cloud SQL using Datastream. 

Our user history database was originally stored in AWS DynamoDB, but we were running into problems with how they structured the data, and we’d often get hot shards but with no way to determine how or why. That led to our decision to migrate to Bigtable. We set up the migration first by writing the data to two locations from Pub/Sub, performed some backfill of data until that was up and running, and then started working on the reading. We performed this over a few short months, then switched everything to Bigtable. 

So, as mentioned, we are using Bigtable for multiple databases. The instance that stores our user events has about 30 TB with about 50 nodes.

Profile management

A second use case for Bigtable is for user profile management, where we track, for example, user attributes based on subscription activity, whether they’ve opted in or out of various lists, and where we apply list-specific rules that determine which targeted emails we send out to users. 

Our very own URL shortener

Our third use case for Bigtable is our URL shortener. When our customers build out campaigns and choose a URL, we append tracking information to the query string of the URLs and they become long. Many times, we are sending them via SMS texts, so the URLs need to be short. We originally used an external solution, but made the determination that they couldn’t support our future demands. Our calls tend to be very bursty in nature, and we needed to plan for a future state of supporting higher throughput. We use a separate table in Bigtable for this shortened URL. We generate the short slug that is 62 bit-encoded and use it as the rowkey. We use the long slug as a Protobuf-encoded data structure in one of the row cells and we also have a cell for counting how many times it was used. We use Bigtable’s atomic increment to increase that counter to track how many times the short slug was used. 

When the user receives a text message on their phone, they click the short URL, which goes through to us, and we expand it to the long slug (from Bigtable) and redirect them to the appropriate site location. Obviously, for the URL shortener use case, we need to make the conversion very quickly. Bigtable’s low latency helps us meet that demand and we can scale it up to meet higher throughput demands.

Meeting the future with Google Cloud

Our business has grown considerably, and as we keep signing up new clients, we need to scale up accordingly, and Bigtable has met our scaling demands easily. With Bigtable and other Google Cloud products powering our data architecture, we’ve met the demand of incredibly high traffic days in the last year, including Black Friday and Cyber Monday. Traffic for these events went much higher than expected, and Bigtable was there, helping us easily scale on demand. 

We are working on leveraging a more cloud native approach and using Google Cloud managed services like GKE, Dataflow, pub/sub, Cloud SQL , Memorystore, BigQuery and more. Google has those 1st party products and we don’t see the value in rolling out or self managing such solutions ourselves..

Thanks to Google Cloud, we now have reliable and flexible data solutions that will help us meet the needs of our growing customer base, and delight their users with fast, responsive, personalized shopping messaging and experiences. 

Learn more about Wunderkind and Cloud Bigtable. Or check out our recent blog exploring the differences between Bigtable and BigQuery.

Cloud Spanner 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 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.

Find out How Streak built a graph database on Cloud Spanner to wrangle billions of emails

What Are Cloud Spanner’s Strengths?

What is Cloud Spanner Useful For?

What Does it Cost?

Pricing for Cloud Spanner is simple and predictable. You are only charged for the number of nodes in your instance, the amount of storage that your tables and secondary indexes use (not pre-provisioned), and the amount of network bandwidth used. Note that there is no additional charge for replication. For more detailed pricing information, please view the pricing guide.

Editor’s note: To launch a new mobile payment platform, Mercari needed a database solution strong on scalability, availability, and performance. Here’s how Cloud Spanner delivered those results. 

E-commerce companies need to connect customers to their services securely, reliably, with zero downtime. When Mercari, Inc. launched a new mobile payment platform, we chose Cloud Spanner as part of our data portfolio, which provided us with easy scalability to handle millions of new users, a fully managed service that minimized overhead costs, and deep integration with other Google Cloud services. 

Mercari, Inc., Japan’s largest C2C marketplace, launched its app in 2013, which allows 18.2million monthly users to easily and securely buy new and used items. Mercari expanded into the United States and in 2014 and launched Merpay, a mobile payment service that can be used through Mercari in Japan in 2017. With more than 85 million users, Merpay is now accepted at 1.8 million merchants and e-commerce sites in Japan, supporting payments via the DOCOMO ID contactless system and QR code.

Prioritizing availability and scalability

When we started building Merpay, we were looking for a new database. In the past, Mercari had used MySQL with bare metal hardware. Because of the amount of data, we required additional expertise to manage and maintain the hardware, software and MySQL implementation. Having built much of the microservices architecture for our Mercari app using Google Kubernetes Engine (GKE), it was natural to look at Google Cloud’s managed services when deciding upon our new database infrastructure for Merpay. 

For the database, we were focusing especially on requirements around availability, scalability, and performance. To do a single payment transaction, there were multiple steps each with writes, requiring high-write-throughput and low-latency from the database. Needing a solution that would support reliable payment processing 24/7/365, we chose Spanner, which offers up to 99.999% availability with zero downtime for planned maintenance and schema changes. 

We worked closely with Google Cloud’s Premium Support, Technical Account Management (TAM), and Strategic Cloud Engineer and Cloud Consultant teams to implement Spanner, including separating the payment processing— originally one of the functions in Mercari—as a microservice.

A nod to easier nodes and better scale

After launch, the number of Merpay users reached 2 million in only a few months. We had 45 Spanner nodes at the time of launch of Merpay and about 50 nodes four months later. Because we’d optimized the application side during those four months, we didn’t have to add many nodes to keep up with the growing traffic. 

Whenever we needed to scale up the serving and storage resources in our instances, Spanner made it easy to increase nodes as needed in the Cloud Console. To optimize costs, it’s easy to add nodes during a marketing campaign and remove them afterward. Even if the traffic count is different from expected, we can change the number of nodes immediately. That’s one incredible convenience of Cloud Spanner.

Building powerful pipelines

Our data pipelines are used for KPI analytics, fraud detection, credit scoring, and customer support use cases. Because Cloud Spanner integrates so easily with other Google Cloud data services, the data in Spanner is easily accessible for analytics. From the Spanner database for each microservice, we create both batch and streaming data pipelines using Pub/Sub and Dataflow, as well as Apache Flink, and load the data into BigQuery and Google Cloud Storage. We’re able to quickly aggregate the data required for data platforms such as BigQuery and Cloud Storage. The original data is stored in Spanner and managed by microservices, but analysis through BigQuery is centrally managed by the Data Platform team. This team determines the confidentiality level of data and centrally manages BigQuery permissions using Terraform. By centrally managing data access permissions on the data platform rather than on individual microservices, we’re able to set appropriate security for individual users.

Our full data portfolio also includes Looker, which we use for product analysis, accounting analysis, test performance visualization, operation monitoring, development efficiency analysis, and HR analysis. We also use Dataproc, Cloud Composer, Data Catalog, and Data Studio. The Dataflow template created for the pipeline is also published as OSS. 

With Spanner and other Google Cloud services, our Merpay platform is flexible, secure, scalable and highly available. As Spanner eliminates overhead, we can devote engineering resources to developing new tools and solutions for our customers. We’re looking ahead at providing cryptocurrency service, for example, and are now working on a project to migrate Mercari’s monolithic system that is still on premises entirely over to Google Cloud.

Read more about Mercari and Merpay. Or check out our recent blog: three reasons to consider Cloud Spanner for your next project.

Global life science supply chains are lengthy and complex with many moving parts. One small disruption can create serious delays and affect your ability to deliver therapeutics for patients.

Supply chain disruptors

Over the last few years, healthcare organizations have encountered a range of obstacles, from both internal and external factors, that have resulted in supply networks failing to get drugs and medical devices to where they need to be on time. These obstacles include:

• Labor and supply shortages
• Rising material costs
• Raw material constraints
• Geo-political events
• Unpredictable weather

How do you overcome supply chain disruptors that are out of your control?

The intelligent healthcare supply chain

While many organizations have already implemented data-driven supply chains, organizations are still faced with the challenges of static, siloed, and different functional supply chain applications; limited data exchange with key trading partners across upstream and downstream operations; and the inability to effectively leverage relevant external data.

At Google Cloud, we believe the key to meaningful and effective change is a data-driven supply chain that allows you to achieve visibility, flexibility, and innovation.

Our solutions help you prepare for the unpredictable and enhance the value of your data. By unlocking AI-driven insights, you can strengthen distribution networks and optimize your workflows and supply chains to become more reliable, intelligent, and sustainable. Some of the business challenges we address include:

• Predicting demand with Vertex AI Forecast
• Visual inspection for quality and predictive maintenance with pre-built ML models
• Automating and optimizing pickup and delivery operations with Cloud Fleet Routing API
• Real time and holistic inventory visibility with Supply Chain Twin

Make sure you’re prepared for the unpredictable with real-time visibility over your distribution networks. Learn how you can harness the power of AI and analytics and gain actionable insights that enhance your supply chain.

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:

1. A single pane of glass to query your multicloud data lakes (across Google Cloud Platform, Amazon Web Services, and Microsoft Azure)
2. Cross-cloud analytics by combining data across different platforms with little to no egress costs
3. 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.