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Editor’s note: Manhattan Associates provides transformative, modern supply chain and omnichannel commerce solutions. It enhanced the scalability, availability, and reliability of its software-as-a-service through a seamless migration to Google Cloud SQL for MySQL.

Geopolitical shifts and global pandemics have made the global supply chain increasingly unpredictable and complex.

At Manhattan Associates, we help many of the world’s leading organizations navigate that complexity through industry-leading supply chain commerce solutions like warehouse management, transportation management, order management, point of sale and much more, to continuously exceed increasing expectations.

The foundation for those solutions is Manhattan Active® Platform, a cloud-native, API-first microservices technology platform that’s been engineered to handle the most complex supply chain networks in the world and designed to never feel like it.

Manhattan Active solutions enable our clients to deliver exceptional shopping experiences in the store, online, and everywhere in between. They unify warehouse, automation, labor and transportation activities, bolster resilience, and seamlessly support growing sustainability requirements.

More Resiliency and Less Downtime

Manhattan Active solutions run 24×7 and need a database solution that can support this. Cloud SQL for MySQL helps us meet our availability goals with automatic failovers, automatic backups, point-in-time recovery, binary log management, and more. Cloud SQL also allows us to create in-region and cross-region replicas efficiently with near zero replication lags. We can create a new replica for a TB size DB in under 30 minutes, a process which used to take several days.

We provide a 99.9% overall up-time service level agreement (SLA) for Manhattan Active Platform, and Cloud SQL helps us keep that promise. Unplanned downtime is 83% less than it would have been with our previous database solutions.

Flexibility and Total Cost of Ownership

One of the fundamental requirements in a cloud-native platform like Manhattan Active is a robust, efficient, and cost-effective database. Our original database solutions struggled across different cloud platforms and created challenges in total cost of ownership and licensing.

We needed a more cost-efficient approach to managing a highly reliable and available database engine that could operate as a managed service, and Cloud SQL delivered.

We were able to move every Manhattan Active solution from our previous cloud vendor to Google Cloud, including the shift to Cloud SQL, with less than four hours of downtime.

Today, we run hundreds of Cloud SQL instances and operate most of them with just a few database administrators (DBA). By offloading the majority of our database management tasks to Cloud SQL, we significantly reduced the cost to maintain Manhattan Active Platform databases.

We also need a solution where we resize our database within minutes. This requirement is needed to manage database performance and infrastructure costs. The ease of resizing our database within minutes allows us to keep the optimal performance levels and saves significantly on overall infrastructure costs.

A Winning Innovation Combination

Cloud SQL provides highly scalable, available, and reliable database capabilities within Manhattan Active Platform, which helps us provide significantly better outcomes for our clients and better experiences for their customers.

Learn more about how you can use Cloud SQL at your organization. Get started today.

At HSBC, we serve 39 million customers, in-person and online, from consumers to businesses, in 66 countries. We maintain data centers in 21 countries, with more than 94,000 servers. With an on-premises infrastructure supporting our business, we kept running into capacity challenges, which really became an innovation blocker and ultimately a business constraint.

Our teams wanted to do more with data to create better products and services, but the technology tools we had weren’t letting us grow and explore. And that data was growing continually. Just one of our data warehouses had grown 300% from 2014 to 2018.

We had a huge amount of data, but what’s the point of having all that data if we couldn’t get insights and business value from it? We wanted to serve our customers flexibly, in the ways that work best for them. 

We knew moving to cloud would let us store and process more data, but as a global bank, we were moving complex systems that needed to also be secure.

It was a team effort to create the project scope and strategy up front, and it paid off in the end. Our cloud migration now enables us to use an agile, DevOps mindset, so we can fail fast and deliver smaller workloads, with automation built in along the way.

This migration also helped us eliminate technical debt and build a data platform that lets us focus on innovation, not managing infrastructure. Along the way, we invented new technology and built processes that we can use as we continue migrating.

Planning for a cloud move
We chose cloud migration because we knew we needed cloud capabilities for our business to really reach its digital potential. We picked Google Cloud, specifically BigQuery, because it’s super fast over small and large datasets, and because we could use both a SQL interface and Connected Sheets to interact with it.

We had to move our data and its schema into the cloud—without having to manually manage every detail and miss the timelines we had set. Our data warehouse is huge, complex, and mission-critical, and didn’t easily lend itself to fit into existing reference architectures. We needed to plan ahead and automate to make sure the migration was efficient, and to ensure we could simplify data and processes along the way.

The first legacy data warehouse we migrated had been built over a period of 15 years, with 30 years worth of data comprising millions of transactions and 180 TB of data.

It ran 6,500 extract, transform, load (ETL) jobs and more than 2,500 reports, getting data from about 100 sources. Cloud migration choices usually involves either re-engineering or lift-and-shift, but we decided on a different strategy for ours: move and improve.

This allowed us to take full advantage of BigQuery’s capabilities, including its capacity and elasticity, to help solve our essential problem of capacity constraints. 

Taking the first steps to cloud
We started creating our cloud strategy through a mapping exercise, which also helped start the change management process among internal teams. We chose architecture decision records as our migration approach, basing those on technical user journeys, which we mapped out using an agile board. User journeys included things like “change data capture,” “product event handling,” or “slowly changing dimensions.” 

These are typical data warehouse topics that have to be addressed when going through a migration, and we had others more specific to the financial services industry, too.

For example, we needed to make sure the data warehouse would have a consistent, golden source of data at a specific point in time. We considered business impacts as well, so we prioritized initially moving archival and historical data to immediately take load off of the old system.

We also worked to establish metrics early on and introduce new concepts, like managing queries and quotas rather than managing hardware, so that data warehouse users would be prepared for the shift to cloud.

To simplify as we went, we examined what we currently had stored in our data warehouse to see what was used or unused. We worked with stakeholders to assess reports, and identified about 600-plus reports that weren’t being used that we could deprecate. We also examined how we could simplify our ETL jobs to remove the technical debt added by previous migrations, giving our production support teams a bit more sleep at night. 

We used a three-step migration strategy for our data: first, migrating schema to BigQuery; second, migrating the reporting load to BigQuery, adding metadata tagging and performing the reconciliation process; and third, moving historical data by converting all the SQL script into data into BigQuery-compliant scripts. 

Creating new tools for migration automation
In keeping with our automation mantra, we invented multiple accelerators to speed up migration. We developed these to meet the timelines we’d set, and to eliminate human error. 

The schema parser and data reconciliation tool helped us migrate our data layer onto BigQuery. SQL parser helped migrate the data access layer onto Google Cloud Platform (GCP) without having to individually migrate 3,500 SQL instances that don’t have data lineage or documentation. This helped us to prioritize workloads.

And the data lineage tool identified components across layers to find dependencies. This was essential for finding and eliminating integration issues during the planning stage, and for identifying application owners during the migration. Finally, the data reconciliation tool reconciles any discrepancies between the data source and the cloud data target. 

Building a cloud future
We used this first migration in our UK data center as a template, so we now have a tailored process and custom tools that we’re confident using going forward.

Our careful approach has paid off for our teams and our customers. We’re enjoying better development and testing procedures. We’ve created an onboarding path for applications, we have a single source of truth in our data warehouse, and we use authorized views for secure data access. The flexibility and scalable capacity of BigQuery means that users can explore data without constraints and our customers get the information they need, faster. 

Cloud Bigtable is a popular and widely used key-value database available on Google Cloud. The service provides scale elasticity, cost efficiency, excellent performance characteristics, and 99.999% availability SLA. This has led to massive adoption with thousands of customers trusting Bigtable to run a variety of their mission-critical workloads.

Bigtable has been in continuous production usage at Google for more than 15 years now. It processes more than 5 billion requests per second at peak and has more than 10 exabytes of data under management. It’s one of the largest semi-structured data storage services at Google.

One of the key use cases for Bigtable at Google is ad personalization. This post describes the central role that Bigtable plays within ad personalization.

Ad personalization

Ad personalization aims to improve user experience by presenting topical and relevant ad content. For example, I often watch bread-making videos on YouTube. If ads personalization is enabled in my ad settings, my viewing history could indicate to YouTube that I’m interested in baking as a topic and would potentially be interested in ad content related to baking products.

Ad personalization requires large-scale data processing in near real-time for timely personalization with strict controls for user data handling and retention. System availability needs to be high, and serving latencies need to be low due to the narrow window within which decisions need to be made on what ad content to retrieve and serve. Sub-optimal serving decisions (e.g. falling back to generic ad content) could potentially impact user experience. Ad economics requires infrastructure costs to be kept as low as possible.

Google’s ad personalization platform provides frameworks to develop and deploy machine learning models for relevance and ranking of ad content. The platform supports both real-time and batch personalization. The platform is built using Bigtable, allowing Google products to access data sources for ads personalization in a secure manner that is both privacy and policy compliant, all while honoring users’ decisions about what data they want to provide to Google

The output from personalization pipelines, such as advertising profiles are stored back in Bigtable for further consumption. The ad serving stack retrieves these advertising profiles to drive the next set of ad serving decisions.

Some of the storage requirements of the personalization platform include:

• Very high throughput access for batch and near real-time personalization
• Low latency (<20 ms at p99) lookup for reads on the critical path for ad serving
• Fast (i.e. in the order of seconds) incremental update of advertising models in order to reduce personalization delay

Bigtable

Bigtable’s versatility in supporting both low-cost, high-throughput access to data for offline personalization as well as consistent low-latency access for online data serving makes it an excellent fit for the ads workloads.

Personalization at Google-scale requires a very large storage footprint. Bigtable’s scalability, performance consistency and low cost required to meet a given performance curve are key differentiators for these workloads.

Data model

The personalization platform stores objects in Bigtable as serialized protobufs keyed by Object ids. Typical data sizes are less than 1 MB and serving latency is less than 20 ms at p99.

Data is organized as corpora, which correspond to distinct categories of data. A corpus maps to a replicated Bigtable.

Within a corpus, data is organized as DataTypes, logical groupings of data. Features, embeddings, and different flavors of advertising profiles are stored as DataTypes, which map to Bigtable column families. DataTypes are defined in schemas which describe the proto structure of the data and additional metadata indicating ownership and provenance. SubTypes map to Bigtable columns and are free-form.

Each row of data is uniquely identified by a RowID, which is based on the Object ID. The personalization API identifies individual values by RowID (row key), DataType (column family), SubType (column part), and Timestamp.

Consistency

The default consistency mode for operations is eventual. In this mode, data from the Bigtable replica nearest to the user is retrieved, providing the lowest median and tail latency.

Reads and writes to a single Bigtable replica are consistent. If there are multiple replicas of Bigtable in a region, traffic spillover across regions is more likely. To improve the likelihood of read-after-write consistency, the personalization platform uses a notion of row affinity. If there are multiple replicas in a region, one replica is preferentially selected for any given row, based on a hash of the Row ID.

For lookups with stricter consistency requirements, the platform first attempts to read from the nearest replica and requests that Bigtable return the current low watermark (LWM) for each replica. If the nearest replica happens to be the replica where the writes originated, or if the LWMs indicate that replication has caught up to the necessary timestamp, then the service returns a consistent response. If replication has not caught up, then the service issues a second lookup—this one targeted at the Bigtable replica where writes originated. That replica could be distant and the request could be slow. While waiting for a response, the platform may issue failover lookups to other replicas in case replication has caught up at those replicas.

Bigtable replication

The Ads personalization workloads use a Bigtable replication topology with more than 20 replicas, spread across four continents.

Replication helps address the high availability needs for ad serving. Bigtable’s zonal monthly uptime percentage is in excess of 99.9%, and replication coupled with a multi-cluster routing policy allows for availability in excess of 99.999%.

A globe-spanning topology allows for data placement that is close to users, minimizing serving latencies. However, it also comes with challenges such as variability in network link costs and throughputs. Bigtable uses Minimum Spanning Tree-based routing algorithms and bandwidth-conserving proxy replicas to help reduce network costs.

For ads personalization, reducing Bigtable replication delay is key to lowering the personalization delay (the time between a user’ action and when that action has been incorporated into advertising models to show more relevant ads to the user). Faster replication is preferred but we also need to balance serving traffic against replication traffic and make sure low-latency user-data serving is not disrupted due to incoming or outgoing replication traffic flows. Under the hood, Bigtable implements complex flow control and priority boost mechanisms to manage global traffic flows and to balance serving and replication traffic priorities.

Workload Isolation

Ad personalization batch workloads are isolated from serving workloads by pinning a given set of workloads onto certain replicas; some Bigtable replicas exclusively drive personalization pipelines while others drive user-data serving. This model allows for a continuous and near real-time feedback loop between serving systems and offline personalization pipelines, while protecting the two workloads from contending with each other.

For Cloud Bigtable users, AppProfiles and cluster-routing policies provide a way to confine and pin workloads to specific replicas to achieve coarse-grained isolation.

Data residency

By default, data is replicated to every replica—often spread out globally—which is wasteful for data that is only accessed regionally. Regionalization saves on storage and replication costs by confining data to the region where it is most likely to be accessed. Compliance with regulations mandating that data pertaining to certain subjects are physically stored within a given geographical area is also vital.

The location of data can be either implicitly determined by the access location of requests or through location metadata and other product signals. Once the location for a user is determined, it is stored in a location metadata table which points to the Bigtable replicas that read requests should be routed to. Migration of data based on row-placement policies happens in the background, without downtime or serving performance regressions.

Conclusion

In this blog post, we looked at how Bigtable is used within Google to support an important use case—modeling user intent for ad personalization.

Over the past decade, Bigtable has scaled as Google’s personalization needs have scaled by orders of magnitude. For large-scale personalization workloads, Bigtable offers low cost storage with excellent performance characteristics. It seamlessly handles global traffic flows with simple user configurations. Its ease at handling both low-latency serving and high-throughput batch computations make it an excellent option for lambda-style data processing pipelines.

We continue to drive high levels of investment to further lower costs, improve performance, and bring new features to make Bigtable an even better choice for personalization workloads.

Learn more

To get started with Bigtable, try it out with a Qwiklab and learn more about the product here.

Acknowledgements
We’d like to thank Ashish Awasthi, Ashish Chopra, Jay Wylie, Phaneendhar Vemuru, Bora Beran, Elijah Lawal, Sean Rhee and other Googlers for their valuable feedback and suggestions.

With the rise of technologies like smartphones, retailers have felt the pressure to meet evolving consumer needs and expectations. Seven-Eleven Japan(“SEJ”) has long been on the forefront of this thanks to the way they develop and invest in IT. However, in recent years, Japan’s leading convenience store chain has struggled to maintain its complex legacy systems at the rate needed to keep up with today’s rapid digitization, spurred on by the increasing proliferation of smartphones and an IT vendor-dependent structure.  

Legacy systems limiting real-time responsiveness and innovation 

Since its early days, SEJ has been proactive in adopting information technology, mainly relying on technology solutions from Japan’s leading vendors. But as the systems have grown, key business issues have been resolved using a vendor-dependent structure rather than being driven by SEJ’s own needs.  

Datasets and business logic were combined and built into legacy environments, gradually leading to data silos. As a result, data was distributed across multiple systems, causing a variety of problems, including the inability to efficiently retrieve data when needed, delays in accessing data collected in individual stores, and difficulties taking measurements at the right time in business operations that require real-time responsiveness.

Connecting different systems also takes time and money, and the lead time for introducing new services—from planning to development and launch—has been longer than expected. 

To solve these problems, SEJ’s IT department built “Seven Central”—a new platform for practical data use launched in 2020 to support the company’s future IT strategies and digital transformation initiatives.

At its core, Seven Central’s ultimate purpose is to allow real-time data views. Versatile, real-time datasets—such as point-of-sale (POS) data from 7-Eleven stores—are consolidated into a centralized location in the cloud. They created a simple data mart that provides data via an API to enable them to respond more quickly to requests from individual departments. 

“In such uncertain times, it’s vital to use data to make quick decisions,” says Izuru Nishimura, Executive Officer and Head of ICT Department. “Each department across the entire company will be able to gain an immediate understanding of the situation based on the most up-to-date data and respond accordingly. This is why we built Seven Central.”

Google Cloud selected to help SEJ build and grow their data cloud

Today’s rapidly changing business environment has also highlighted the risk of IT support becoming a bottleneck. The long-term strategy is to gradually expand the datasets managed and collected in Seven Central according to business needs. 

In the first phase, SEJ collected POS data from all 21,000+ stores to enable real-time analysis. Moving forward, they would like to collect other relevant data—for example, unstructured data, such as images and videos, or master datasets that are currently stored externally. 

Google Cloud was already a top contender when SEJ started developing Seven Central in 2019. They compared various public cloud services besides Google Cloud, focusing on three main capabilities.

“We placed particular emphasis on service scalability to drive future digital transformation; security when handling data, which is the lifeline of our company; and finally, openness,” says Nishimura. He emphasizes that openness was perhaps the most important factor for choosing Google Cloud. Breaking away from the negative aspects of an entirely vendor-dependent system enabled them to build an agile development system with multiple vendors. 

Google Cloud technologies including BigQuery and API management platform, Apigee, play a vital role in Seven Central. BigQuery’s high-speed processing at petabyte scale and fully managed infrastructure helped keep costs low during development and verification.   

“Data is stored in a way that allows you to share it easily across organizations, which helps solve the issue of data silos from the perspective of scalability. I also like the fact there are some interesting features that could be used in the future—like BigQuery ML, which enables machine learning on BigQuery,” says Nishimura. 

Apigee allows SEJ to separate datasets and business logic, which is one of the key points of Seven Central. While the trend these days is to standardize interfaces using an API, the reality tends to involve many different APIs rather than the introduction of one unified API. With Apigee, SEJ provides a single unified API for all of its data cloud, and they can now understand what data is used thanks to Apigee’s API usage visualizations.  

“Right now, we collect data from all 21,000+ stores,” says Nishimura. “But in anticipation of a future expansion in business operations, we have designed a system that can scale up and run without issue, even if we were to have 30,000 stores, with 1,000 customers per store per day, purchasing five items per person.”

Real-time insights with BigQuery and Cloud Spanner

Google Cloud partner Cloud Ace came on board early in the planning phases. Based on their recommendations, SEJ decided to continue making full use of BigQuery to analyze data collected from all 21,000+ stores throughout Japan, while also using Cloud Spanner’s availability, near-unlimited scalability and transactional consistency to help achieve the real-time results needed for the project.

“Given that both the data and the regularity with which it is accessed are expected to steadily increase in the future, we chose Cloud Spanner as backend storage for data delivery via API. We consider it a good choice,” says Shota Kikuchi, General Manager, Consulting Department, Technology Division, Cloud Ace Co., Ltd.

Finally, they chose to use Google Cloud’s Stream Analytics Solutions messaging service for collecting POS data in real time, which can then be put to immediate use with Cloud Spanner and BigQuery. 

High-speed responses exceed targets and create new value 

Seven Central went live in September 2020 with surprising results. 

They initially set a target time of one hour from when a customer makes a purchase to the point when Seven Central can use that data. But when the final system was first tried—it took barely a minute. Moving forward they estimate that the latest inventory data from the service side will become available within a few minutes of being added to the system.  

“This is real innovation, and I must admit that I am quite surprised. As well as being able to solve existing issues, we also hope it will lead to new improvements and services that have been unimaginable up until now,” says Nishimura.

The team hopes to roll out the Seven Central platform in all companies affiliated with Seven & i Holdings—not just SEJ. They also plan to explore Google Cloud AI and machine learning technologies to take on challenges in new areas. For example, they are investigating the idea of clustering individual stores using BigQuery ML.

Seven Central has already attracted attention from many departments and received a lot of requests. Nishimura and his team say they hope to continue to grow Seven Central while still observing their fundamental principles—not including business logic, maintaining real-time results, and staying true to the uniqueness of SEJ.

Learn more about Google Cloud smart analytics solutions.