Home Depot Leverages Google Cloud’s BigQuery and DataFlow to Break Data Silos and Craft Personalized CX
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The Home Depot, Inc., is the world’s largest home improvement retailer with annual revenue of over $151B. Delighting our customers—whether do-it-yourselfers or professionals—by providing the home improvement products, services, and equipment rentals they need, when they need them, is key to our success.
We operate more than 2,300 stores throughout the United States, Canada, and Mexico. We also have a substantial online presence through HomeDepot.com, which is one of the largest e-commerce platforms in the world in terms of revenue. The site has experienced significant growth both in traffic and revenue since the onset of Covid-19.
Because many of our customers shop at both our brick-and-mortar stores and online, we’ve embarked on a multi-year strategy to offer a shopping experience that seamlessly bridges the physical and digital worlds. To maximize value for the increasing number of online shoppers, we’ve shifted our focus from event marketing to personalized marketing, as we found it to be far more effective in improving the customer experience throughout the sales journey. This led to changing our approach to marketing content, email communications, product recommendations, and the overall website experience.
Challenge: launching a modern marketing strategy using legacy IT
For personalized marketing to be successful, we had to improve our ability to recognize a customer at the point of transaction so we could—among other things—suspend irrelevant and unnecessary advertising. Most of us have experienced the annoyance of receiving ads for something we’ve already purchased, which can degrade our perception of the brand itself. While many online retailers can identify 100% of their customer transactions due to the rich information captured during checkout, most of our transactions flow through physical stores, making this a more difficult problem to solve.
Our old legacy IT system, which ran in an on-premises data center and leveraged Hadoop, also challenged us since maintaining both the hardware and software stack required significant resources. When that system was built, personalized marketing was not a priority, so it took several days to process customer transaction data and several weeks to roll out any system changes. Further, managing and maintaining the large Hadoop cluster base presented its own set of issues in terms of quality control and reliability, as did keeping up with open-source community updates for each data processing layer.
Adopting a hybrid approach
As we worked through the challenges of our legacy system, we started thinking about what we wanted our future system to look like. Like many companies, we began with a “build vs. buy” analysis. We looked at several products on the market and determined that while each of them had their strengths, none was able to offer the complete set of features we needed.
Our project team didn’t think it made sense to build a solution from scratch, nor did we have access to the third-party data we needed. After much consideration, we decided to adopt a solution that combined a complete rewrite of the legacy system with the support of a partner to help with the customer transaction matching process.
Building the foundation on Google Cloud
We chose Google Cloud’s data platform, specifically BigQuery, Dataflow, DataProc, Cloud Storage, and Cloud Composer. Google Cloud platform empowered us to break down data silos and unify each stage of the data lifecycle from ingestion, storage, and processing to analysis and insights. Google Cloud offered best-in-class integration with open-source standards and provided the portability and extensibility we needed to make our hybrid solution work well. The open standards of BigQuery’s BQ Storage API allowed us to leverage fast BQ storage layers to be utilized with other compute platforms, e.g., DataProc.
We used BigQuery combined with Dataflow to integrate our first- and third-party data into an enterprise data and analytics data lake architecture. The system then combined previously siloed data and used BigQuery ML to create complete customer profiles spanning the entire shopping experience, both in-store and online.
Understanding the customer journey with the help of Dataflow and BigQuery
The process of developing customer profiles involves aggregating a number of first- and third-party data sources to create a 360-degree view of the customer based on both their history and intent. It starts with creating a single historical customer profile through data aggregation, deduplication, and enrichment. We used several vendors to help with customer resolution and NCOA (Change of Address) updates, which allows the profile to be house-holded and transactions to be properly reconciled to both the individual and the household. This output is then matched to different customer signals to help create an understanding of where the customer is in their journey—and how we can help.
The initial implementation used Google Dataflow, Google’s streaming analytics solution, to load data from Google Cloud Storage into BigQuery and perform all necessary transformations. The Dataflow process was converted into BQML (BigQuery Machine Learning) since this significantly reduced costs and increased visibility into data jobs. We used Google Cloud Composer, a fully managed workflow orchestration service, to help orchestrate all data operations and DataProc and Google Kubernetes Engine to enable special case data integration so we could quickly pivot and test new campaigns. The architecture diagram below shows the overall structure of our solution.
Taking full advantage of cloud-native technology
In our initial migration to Google Cloud, we moved most of our legacy processes in their original form. However, we quickly learned that this approach didn’t take full advantage of the cloud-native and more improved features Google Cloud offered such as auto scaling of resources, flexibility to decouple storage from the compute layer, and a wide variety of options to choose the best tool for the job. We refactored our Hadoop-based data pipelines written in Java-based Map Reduce and our Pig Latin jobs to Dataflow and BigQuery jobs. This dramatically reduced processing time and made our data pipeline code concise and efficient.
Previously, our legacy system processes ran longer than intended, and data was not used efficiently. Optimizing our code to be cloud-native and leveraging all the capabilities of Google Cloud services resulted in reduced run times. We decreased our data processing window from 3 days to 24 hours, improved resource usage by dramatically reducing the amount of compute we used to possess this data, and built a more streamlined system. This in turn reduced cloud costs and provided better insight. For example, DataFlow offers powerful native features to monitor data pipelines, enabling us to be more agile.
Leveraging the flexibility and speed of the cloud to improve outcomes
Today, using a continuous integration/continuous delivery (CI/CD) approach, we can deploy multiple system changes each week to further improve our ability to recognize in-store transactions. Leveraging the combined capabilities of various Google Cloud systems—BigQuery, DataFlow, Cloud Composer, Dataproc, and Cloud Storage–we drastically increased our ability to recognize transactions and can now connect over 75% of all transactions to an existing household. Further, the flexible Google Cloud environment coupled with our cloud-native application makes our team more nimble and better able to respond to emerging problems or new opportunities.
Increased speed has led to better outcomes in our ability to match transactions across all sales channels to a customer and thereby improve their experience. Before moving to Google Cloud, it took 48 to 72 hours to match customers to their transactions, but now we can do it in less than 24 hours.
Making marketing more personal—and more efficient
The ability to quickly match customers to transactions has huge implications for our downstream marketing efforts in terms of both cost and effectiveness. By knowing what a customer has purchased, we can turn off ads for products they’ve already bought or offer ads for things that support what they’ve bought recently. This helps us use our marketing dollars much more efficiently and offer an improved customer experience.
Additionally, we can now apply the analytical models developed using BQML and Vertex AI to sort customers into audiences. This allows us to more quickly identify a customer’s current project, such as remodeling a kitchen or finishing a basement, and then personalize their journey by offering them information on products and services that matter most at a given point through our various marketing channels. This provides customers with a more relevant and customized shopping journey that mirrors their individual needs.
Protecting a customer’s privacy
With this ability to better understand our customers, we also have the responsibility to ensure we have good oversight and maintain their data privacy. Google’s cloud solutions provide us the security needed to help protect our customers’ data, while also being flexible enough to allow us to support state and federal regulations, like the California Customer Privacy Act. This way we can provide a customer the personalized experience they desire without having to fear how their data is being used.
With flexible Google Cloud technology in place, The Home Depot is well positioned to compete in an industry where customers have many choices. By putting our customers’ needs first, we can stay top of mind whenever the next project comes up.
Architecting Data Pipelines Directly Improves Customer Experience at Universe.com
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At Universe, we serve customers day and night and are always working to make sure they have a great experience, whether online or at one of our live events.
Our technology has to make that possible, and our legacy systems weren’t cutting it anymore. What we needed was a consistent, reliable infrastructure that would help our internal teams provide a fast and innovative ticket-buying experience to customers.
With our data well-managed, we could free up time for our developers to bring new web features to customers, like tailored add-ons at checkout.
Our team of about 20 software engineers needed more flexibility and agility in our infrastructure; we were using various data processing tools, and it wasn’t easy to share data across teams so that everyone saw the same information.
We also needed to incorporate streaming data into the data warehouse to ensure the consistency and integrity of data that’s read in a particular window of time from multiple sources. Our developer teams needed to be able to ship new features faster, and the data back ends were getting in the way.
In addition, when GDPR regulations went into effect, we needed to make sure all our data was anonymized, and we couldn’t do that with our legacy tools.
Finding the right data tools for the job
To make sure our customers were getting a top-notch online experience, we had to make the right technology choices.
Our first step was to centralize multiple data sources and create a single data warehouse that could serve as the foundation for all of our reporting requirements, both internal and external.
The new technology infrastructure we built had to let us move and analyze data easily, so our teams could focus on using that data and insights to better serve our customers.
Previously, we had lots of siloed systems and applications running in AWS. We did a trial using Redshift, but we needed more flexibility than it offered in how we loaded historical data into our cloud data warehouse. Though we were using MongoDB Atlas for our transactional database, it was important to continue using SQL for querying data.
The trial task that really sold us on BigQuery was when we wanted to alter a small table that had about 20 million rows, used for internal reporting.
We needed to add a value, but our PostgreSQL system wouldn’t allow it. Using Apache Beam, we set up a simple pipeline that moved data from the original source into BigQuery to see if we could add the column there.
BigQuery ingested the data and let us add the new value in seconds. That was a significant moment that led us to start looking at how we could build end-to-end solutions on Google Cloud. BigQuery gave us multiple options to load our historical data in batches and build powerful pipelines.
We also explored Google Cloud’s migration tools and data pipeline options. Once we saw how Cloud Dataflow worked, with its Apache Beam back-end, we never looked back. Google Cloud provided us with the data tools we needed to build our data infrastructure.
Cloud for data, and for users
Introducing new technologies isn’t always simple—companies sometimes avoid it altogether because it’s so hard. But our Google Cloud onboarding process has been easy.
It took us less than two months to fully deploy our BigQuery data warehouse using the Cloud Dataflow-Apache Beam combination.
Moving to Google Cloud brought us a lot of technology advantages, but it’s also been hugely helpful for our internal users and customers. The data analytics capabilities that we’re now able to offer users has really impressed our internal teams, including developers and DevOps, even those who haven’t used this type of technology before.
Some internal clients are already entirely self-service. We’ve hosted frequent demos, and also hosted some “hack days,” where we share knowledge with our internal teams to show them what’s possible.
We quickly found that BigQuery helped us solve scale and speed problems.
One of our main pain points had been adding upsell opportunities for customers during the checkout process. The legacy technology hadn’t allowed us to quickly reflect those changes in the data warehouse. With BigQuery, we’re able to do that, and devote fewer resources to making it happen. We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.
We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.
Two features in particular that we implemented using BigQuery have helped us improve the performance of our core transactional database. First, using Cloud Dataflow to convert raw MongoDB logs to structured rows under a BigQuery table, which we can then query using SQL to identify slow or underperforming queries. Second, we can now query multiple logging tables using wildcards, since we load Fastly logs to BigQuery.
Along with MongoDB Atlas as our main transactional database, much of our infrastructure now runs as Google Cloud microservices using Google Kubernetes Engine (GKE), including the home page and our payment system. Kubernetes cron jobs power background scheduled jobs, and we also use Cloud Pub/Sub. Cloud Storage handles any data storage if any space constraints emerge.
Our overall performance has increased by about 10x with BigQuery. Both our customers and internal clients, like our sales and finance teams, benefit from the new low-latency reporting. Reports that used to be weekly or monthly are now available in near-real time. It’s not only faster to read records, but faster to move the data, too.
We have Cloud Dataflow pipelines that write to multiple places, and the speed of moving and processing data is incredibly helpful. We stream in financial data using Cloud Dataflow in streaming mode, and plan to have different streaming pipelines as we grow. We have several batch pipelines that run every day. We can move terabytes of data without performance issues, and process more than 100,000 rows of data per second from the underlying database. It used to take us a month to move that volume of data into our data warehouse. With BigQuery, it takes two days.
We’re also enjoying how easy and productive these tools are. They make our life as software engineers easier, so we can focus on the problem at hand, not fighting with our tools.
What’s next for Universe
Our team will continue to push even more into Google Cloud’s data platform. We have plans to explore Cloud Datastore next. We’re also moving our databases to PostgreSQL on GCP, using Cloud Dataflow and Beam. BigQuery’s machine learning tools may also come into play as Universe’s cloud journey evolves, so we can start doing predictive analytics based on our data. We’re looking forward to gaining even more speed and agility to meet our business goals and customer needs.
HarbourBridge Schema Assistant Allows Quick, Bulk Migration to Cloud Spanner
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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
- 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.
- Local type mapping. Users can override the custom type mapping for a given table/column.
- Session management. A session keeps track of all the changes made to the schema mapping.
- Customization of secondary indexes. Users can add, edit and delete secondary indexes to optimize their Spanner performance.
- 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.
MerPay Platform Scales its Reach to Millions of Users using Cloud Spanner
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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.
Speed Up Data-driven Innovation in Life Sciences with Google Cloud
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The last few years have underscored the importance of speed in bringing new drugs and medical devices to market, while ensuring safety and efficacy. Over this time, healthcare and life sciences organizations have transformed the way they research, develop, and deliver patient care by embracing agility and innovation.
Now, the industry is set to reap the benefits of cloud technology and overcome the existing barriers to innovation.
What’s holding back innovation?
Costly clinical trials: The process of trialing and developing new drugs and devices is still long and costly, with more than 1 in 5 clinical trials failing due to a lack of funding.1 The high failure rate comes as no surprise when you consider the average clinical trial costs $19 million and takes 10-15 years (through all 3 phases) to be approved.2
Stringent security requirements: Pre-clinical R&D and clinical trials use large volumes of highly sensitive patient data – making the life sciences industry one of the top sectors targeted by hackers.3 On top of this, the FDA and other regulatory bodies have strict requirements for medical device cybersecurity.
Unpredictable supply chains: Global supply chains are becoming increasingly complex and unpredictable. This can be brought on by anything from supply shortages, to geo-political events, and even bad weather. Making things worse is the lack of visibility into medical shipment disruptions – so when disaster strikes you’re often caught off guard.
Google Cloud for life sciences
At Alphabet, we’ve made significant investments in healthcare and life sciences, helping to tackle the world’s biggest healthcare problems, from chronic disease management, to precision medicine, to protein folding.
Together with Google, you can transform your life sciences organization and deliver secure, data-driven innovation across the value chain.
- Accelerate clinical trials to deliver life-saving treatments faster and at less cost. Clinical trials require relevant and equitable patient cohorts that can produce clinically valid data. Solutions like DocAI can enable optimal patient matching for clinical trials, helping organizations optimize clinical trial selection and increase time to value. How that patient data is collected is also important. Collection in a physician’s office captures a snapshot of the participant’s data at one point in time and doesn’t necessarily account for daily lifestyle variables. Fitbit, used in more than 1,500 published studies–more than any other wearable device–can enrich clinical trial endpoints with new insights from longitudinal lifestyle data, which can help improve patient retention and compliance with study protocols. We have introduced Device Connect for Fitbit, which empowers healthcare and life sciences enterprises with accelerated analytics and insights to help people live healthier lives. We are able to empower organizations to improve clinical trials in key ways:
- Enable clinical trial managers to quickly create and launch mobile and web RWE collection mechanism for patient reported outcomes
- Enable privacy controls with Cloud Healthcare Consent API and, as needed, remove PHI using Cloud Healthcare De-identification API
- Ingest RWE and data into BigQuery for analysis
- Leverage Looker to enable quick visualization and powerful analysis of a study’s progress and results
- Ensure security and privacy for a safe, coordinated, and compliant approach to digital transformation. Google Cloud offers customers a comprehensive set of services including pioneering capabilities such as BeyondCorp Enterprise for Zero Trust and VirusTotal for malicious content and software vulnerabilities; Chronicle’s security analytics and automation coupled with services such as Security Command Center to help organizations detect and protect themselves from cyber threats; as well as expertise from Google Cloud’s Cybersecurity Action Team. Google Cloud also recently acquired Mandiant, a leader in dynamic cyber defense, threat intelligence and incident response services.
- Optimize supply chains and enhance your data to prepare for the unpredictable. With a digital supply chain platform, we can empower supply chain professionals to solve problems in real time including visibility and advanced analytics, alert-based event management, collaboration between teams and partners, and AI-driven optimization and simulation.
Ready to learn more? We’ll be taking a deep dive into each of the challenges outlined above in our life sciences video series. Stay tuned.
- National Library of Medicine
- How much does a clinical trial cost?
- Life Sciences Industry Becomes Latest Arena in Hackers’ Digital Warfare
Unification of Dataplex and Data Catalog: A Full Spectrum of Data Governance and Data Management at Scale
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Today, we are excited to announce that Google Cloud Data Catalog will be unified with Dataplex into a single user interface. With this unification, customers have a unified experience to search and discover their data, enrich it with relevant business information, organize it by logical data domains, and centrally govern and monitor their distributed data with built-in data intelligence and automation capabilities. Customers now have access to an integrated metadata platform that connects technical and operational metadata with business metadata, and then uses this augmented and active metadata to drive intelligent Data management and governance got easier the unification of Dataplex and Data Catalog
The enterprise data landscape is becoming increasingly diverse and distributed with data across multiple storage systems, each having its own way of handling metadata, security, and governance. This creates a tremendous amount of operational complexity, and thus, generates strong market demand for a metadata platform that can power consistent operations across distributed data.
Dataple provides a data fabric to automate data management, governance, discovery, and exploration across distributed data at scale. With Dataplex, enterprises can easily arrange their data into data domains, delegate ownership, usage, and sharing of data to data owners who have the right business context, while still maintaining a single pane of glass to consistently monitor and govern data across various data domains in their organization.
Prior to this unification, data owners, stewards and governors had to use two different interfaces – Dataplex to organize, manage, and govern their data, and Data Catalog to discover, understand, and enrich their data. Now with this unification, we are creating a single coherent user experience where customers can now automatically discover and catalog all the data they own, understand data lineage, check for data quality, augment with business knowledge, organize data into domains, and then use that combined metadata to power data management. Together we provide an integrated experience that serves the full spectrum of data governance needs in an organization, enabling data management at scale.
“With Data Catalog now being part of Dataplex, we get a unified, simplified, and streamlined experience to effectively discover and govern our data, which enables team productivity and analytics agility for our organization. We can now use a single experience to search and discover data with relevant business context, organize and govern this data based on business domains, and enable access to trusted data for analytics and data science – all within the same platform.” said Elton Martins, Senior Director of Data Engineering at Loblaw Companies Limited.
Getting started
Existing Data Catalog and Dataplex customers and new customers can now start using Dataplex for metadata discovery, management and governance. Please note that while the user experience interface is unified via this release, all existing APIs and feature functionalities of both products will continue to work as before. To learn more, please refer to technical documentations or contact the Google Cloud sales team.
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