Migrating MySQL to Spanner? Here’s What You Need to Know
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Since its initial release in 1995, MySQL has not only been the de-facto database for many data storage needs, but has gathered much attention over the years as one of the most well-recognized databases around when it comes to Relational Database Management Systems and transactional data processing.
Any application in retail, e-commerce, or banking has likely had its fair share of business depending on a relational database for its transactional needs. Many of these applications are built on MySQL due to its flexibility, open source nature, and strong community support.
Whether in the context of a migration from a relational database (like MySQL or PostgreSQL) , migration from a NoSQL database (like Cassandra), or a green grass workload, many companies turn to Cloud Spanner seeking a high availability SLA (99.99% for regional instances and 99.999% for multi region instances), unlimited scale, and low operational overhead – no patching required, no maintenance or other planned downtimes, just to name a few benefits.
The tooling and open source ecosystem around Spanner has evolved and grown ever since the service was introduced. HarbourBridge is a part of this ecosystem, and it’s meant to help customers port their existing MySQL or PostgreSQL schema to a Cloud Spanner schema.
Application Migration Tips
Despite helpful tools like HarbourBridge, database migrations are never trivial. Here are a few things to pay attention to when migrating from MySQL to Spanner, and how to update your application logic to address them.
Note – The following snippets use the PHP client for Cloud Spanner. A couple of the snippets reference a partial Magento port that our friends over at Searce have been working on. Once you understand the operations, you should be able to implement the same in any of the other languages that Cloud Spanner supports.
Cloud spanner enforces strict data types
In a MySQL query, the value of an attribute can be referenced as either a string or an integer. Example:
- select * from catalog_eav_attribute where attribute_id = 46;
- select * from catalog_eav_attribute where attribute_id = “46”;
Both are valid and equivalent.
In Cloud Spanner, the query will return an error if you try to reference an integer type by using a string representation.
Here is an example of a working query from Cloud Spanner:
- select * from catalog_eav_attribute where attribute_id = 46; — this will work
- Select * from catalog_eav_attribute where attribute_id = “46” — will fail, since we are supplying a string with “46”
You may use a function like the following to assist you with such transformations.
/*** Formats the SQL for Cloud Spanner* Example* Input SQL : <select statement> WHERE* (`product_id` = '340') ORDER BY position ASC* Output SQL : <select statement> WHERE* (`product_id` = 340) ORDER BY position ASC* In the above example integer*`340` is sanitized by removing single quotes.* Sanitization is required since Cloud Spanner* has strict typing* @param string $sql* @return string $sql*/public function sanitizeSql(string $sql){if (preg_match_all("/('[^']*')/", $sql, $m)) {$matches = array_shift($m);for($i = 0; $i < count($matches); $i++) {$curr = $matches[$i];$curr = filter_var($curr,FILTER_SANITIZE_NUMBER_INT);if (is_numeric($curr)) {$sql = str_replace($matches[$i],$curr, $sql);}}}return $sql;}
Using sequences in primary keys is not a Spanner best practice
Cloud Spanner does not implement a sequence generator, and it is not a Spanner best practice to use sequential IDs because doing so can cause hotspotting.
An alternative mechanism of generating a unique primary key is to use a UUID or any other similar mechanisms that result in non-sequential values. For more information, please refer to this article.
In order to convert all existing primary keys to a UUID pattern, you can change the schema using the snippet here
You may use this code snippet to modify the application code to generate the UUID for the auto increment. There are other ways to do this as well, such as using PHP built-in uniqid function.
/*** Generate UUID.* @return string*/public function getAutoIncrement(){if (function_exists('com_create_guid') === true) {return trim(com_create_guid(), '{}');}return sprintf('%04X%04X-%04X-%04X-%04X-%04X%04X%04X',mt_rand(0, 65535), mt_rand(0, 65535),mt_rand(0, 65535), mt_rand(16384, 20479),mt_rand(32768, 49151), mt_rand(0, 65535),mt_rand(0, 65535), mt_rand(0, 65535));}
Implicit casting and the need for explicit casting of field data types
Both MySQL and Cloud Spanner follow the SQL standard, hence much of the query syntax is the same. One notable difference is that the Cloud Spanner field types are implicitly cast to an appropriate data type out of the ones mentioned here. When implicit casting of the field type fails, Spanner returns a read error, so it would be safer to perform the casting to the appropriate type when issuing the SELECT statement.
Modify application code to cast to respective data type before execution of query.
$con = $this->getSpannerConnection();/*** Cloud Spanner follows strict type so cast the columns appropriately*/$select = $con->addCast($select, "`t_d`.`value`", 'string');$select = $con->addCast($select, "`t_s`.`value`", 'string');$select = $con->addCast($select, "IF(t_s.value_id IS NULL,t_d.value, t_s.value)", 'string');$values = $con->fetchAll($select);
Modify the application code to cast the column to its respective type
/*** Cast the column with type* @param string $sql* @param string $col* @param string $type* @return string*/public function addCast(string $sql,string $col, string $type){$cast = "cast(".$col." as ".$type.")";return str_replace($col, $cast, $sql);}
Using interleaved tables to improve read performance
Cloud Spanner’s table interleaving is a great choice for many parent-child relationships where the child table’s primary key includes the parent table’s primary key columns. Interleaving ensures that child rows are collocated with their parent rows, which can significantly improve query performance.
Refer to the statements here for a few samples of creating interleaved tables. To learn more about table interleaving, visit the documentation.
Conclusion
Database migrations are complicated. Hopefully, using HarbourBridge and the list of tips in this article can make that task easier. For additional tips, please take a look at this migration guide and read more about HarbourBridge.
Transforming Software Development Education with CourseMatix
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As the world increasingly relies on software, businesses have struggled to find enough developer talent to build and maintain their applications. IDC estimates the global shortage of software developers reached 1.4 million in 2021 and expects that number to balloon to 4 million by 2025.
Higher education is a great starting point to solve the software development skills shortage. One institution — Spokane Community College — has made strides in enhancing student learning and serving more students with the right mix of software development and IT education while saving educators time.
Building a modern IT education platform
As a first step to improve computer science education and scale the number of students it could teach, Spokane Community College needed a platform that could help instructors more efficiently manage the teaching process. That’s where CourseMatix came in.
CourseMatix is an education platform that delivers the required tools, platforms, and frameworks such as automated grading, assignment tracking, and testing to save teachers time and enhance students’ learning.
CourseMatix is entirely made up of microservices, with significant compute demands because it creates and runs a unique dedicated environment for each student. The platform is complex by nature as it supports a range of technologies that computer science professors need to teach varying coursework. This led to issues with scalability, reliability, and uptime when running on legacy infrastructure.
“We really wanted to no longer manage our infrastructure, leaving us to focus entirely on our platform,” says Aleks Korn, chief executive officer of CourseMatix. “We engaged Google Cloud partner CDW to see how we could simplify and improve our IT environment. The decision was quickly made to migrate to Google Kubernetes Engine (GKE).”
GKE provides CourseMatix with automatic horizontal and vertical scaling to minimize costs and downtime, while removing management and maintenance requirements. This enables CourseMatix to expand the reach of its platform at the lowest possible price and maintain a lean team. CDW helped CourseMatix make this decision to migrate to GKE, managed the migration, and provided guidance on a storage solution.
Because CourseMatix has high data storage volume requirements (given the size of software development assignments) and needs to be fully reliable to ensure work is not lost, it also chose to work with NetApp. NetApp Cloud Volumes Service for Google Cloud perfectly complements GKE in its highly expandable, reliable, and user-friendly capabilities. The platform was now ready for its U.S. launch at Spokane Community College.
Transforming software development education
Spokane Community College needed a solution to address the challenges facing its professors, particularly with respect to time management.
“Each software development assignment can take an hour to grade,” says Bret Dickey, professor of software development at Spokane Community College. “When you have 100 students, you’re talking weeks of turnaround time to grade those projects. CourseMatix provides immediate feedback, which is a game changer for us as professors and our students.”
Students now complete and submit their assignments with real-world tools, using source control, creating branches to complete their work, and merging those branches to push up to the server where pipelines run the applications.
“It’s making our teaching better overall, including using real-world concepts to enhance teaching and outcomes,” says Dickey.
CourseMatix also provides the tooling to upskill on various programming languages and software packages required for coursework, helping Spokane Community College manage server-side frameworks and client-side languages that each have their own tools.
“Students can access their cloud-based coding environment and all the tools they may need for an assignment are ready to go,” says Karmen Blake, another software development professor at Spokane Community College. “Having a central system for tooling within CourseMatix has been a huge time saver, meaning more time for learning and practice that prepares students for careers as professional software developers.”
Karmen and Dickey have seen marked improvements in their ability to teach and in student learning by using CourseMatix. For example, the platform provides automated hints and varying error messages based on the number of attempts a student has made to solve a problem to help each student along without making the assignment too easy or too difficult.
As a cloud-based platform, CourseMatix also empowers students from a wide range of socioeconomic backgrounds to learn computer science.
“As long as students can access the internet on their laptops, they have everything to complete their assignments,” says Karmen. “The cloud-based CourseMatix offering improves efficiency and equity across all students in our computer science programs.”
With a more fluid experience for students and immense amounts of time saved on grading, Spokane Community College is in a better position to increase enrollment in software development classes.
“Now that we have automated our grading and optimized our assignment processes, I am beginning to take on more students and teach more classes,” says Dickey. “I could not have imagined this outcome only a couple of years ago, and it’s all possible thanks to CourseMatix.”
To learn more about how partners help organizations make the most of Google Cloud, you can visit our partner page.
GCP Launches Datastream, A Serverless Change Data Capture and Replication Service
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Today, we’re announcing Datastream, a serverless change data capture (CDC) and replication service, available now in preview. Datastream allows enterprises to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. You can now easily and seamlessly deliver change streams from Oracle and MySQL databases into Google Cloud services such as BigQuery, Cloud SQL, Google Cloud Storage, and Cloud Spanner, saving time and resources and ensuring your data is accurate and up-to-date.
“Global companies are demanding change data capture to provide replication capabilities across disparate data sources, and provide a real-time source of streaming data for real-time analytics and business operations,” says Stewart Bond, Director, Data Integration and Intelligence Software Research at IDC.
However, companies are finding it difficult to realize these capabilities because commonly used data replication offerings are costly, cumbersome to set up, and require significant management and monitoring overhead to run flexibly or at scale. This leaves customers with a difficult-to-maintain and fragmented architecture.
Datastream’s differentiated approach
Datastream is taking on these challenges with a differentiated approach. Its serverless architecture seamlessly and transparently scales up or down as data volumes shift in real time, freeing teams to focus on delivering up-to-date insights instead of managing infrastructure. It also provides the streamlined customer experience, ease of use, and security that our customers have come to expect from Google Cloud, with private connectivity options built into the guided setup experience.
Datastream integrates with purpose-built and extensible Dataflow templates to pull the change streams written to Cloud Storage, and create up-to-date replicated tables in BigQuery for analytics. It also leverages Dataflow templates to replicate and synchronize databases into Cloud SQL or Cloud Spanner for database migrations and hybrid cloud configurations.
Datastream also powers a Google-native Oracle connector in Cloud Data Fusion’s new replication feature for easy ETL/ELT pipelining. And by delivering change streams directly into Cloud Storage, customers can leverage Datastream to implement modern, event-driven architectures.
Customers tell us about the benefits they’ve found using Datastream. That includes Schnuck Markets, Inc., “Leveraging Datastream, we’ve been able to replicate data from our on-premises databases to BigQuery reliably and with little impact to our production workloads. This new method replaced our batch processing and allowed for insights to be leveraged from BigQuery quicker,” says Caleb Carr, principal technologist from Schnuck Markets. “Furthermore, implementing Datastream removed the need for our analytics group to reference on-premises databases to do their work and support our business users.”
Cogeco Communications, Inc. used Datastream to also realize the value of low-latency data access. “Datastream unlocked new customer interaction opportunities not previously possible by enabling low-latency access in BigQuery to our operational Oracle data.” says Jean-Lou Dupont, Senior Director, Enterprise Architecture, Cogeco Communications, Inc. “This streamlined integration process brings data from hundreds of disparate Oracle tables into a unified data hub. Datastream enabled us to achieve this with 10X time and effort efficiency.”
In addition, Major League Baseball (MLB) used Datastream’s replication capabilities to migrate their data from Oracle to Cloud SQL for PostgreSQL. “As we’re modernizing our applications, replicating the database data reliably out of Oracle and into Cloud SQL for PostgreSQL is a critical component of that process,” says Shawn O’Rourke, manager of technology at MLB. “Using Datastream’s CDC capabilities, we were able to replicate our database securely and with low latency, resulting in minimal downtime to our application. We can now standardize on this process and repeat it for our next databases, regardless of scale.”
Our partner HCL has worked with many organizations looking to get more out of their data and plan for the future. “HCL customers across every industry are looking for ways to extract more value out of their vast amounts of data,” says Siva G. Subramanian, Global Head for Data & Analytics at HCL Google Business Unit. “CDC plays a big part in the solutions we offer to our customers using Google Cloud. Datastream enables us to deliver a secure and reliable solution to our customers that’s easy to set up and maintain. CDC is a key and integrated part of Google Cloud Data Solutions.”
“Google Cloud’s new CDC offering, Datastream, is a differentiator for Google among hyperscale cloud service providers, by supporting replication of data from Oracle and MySQL databases into the Google Cloud environment using a serverless cloud-native architecture, which removes the burden of infrastructure management for organizations, and provides elastic scalability to handle real-time workloads,” says Stewart Bond, Director, Data Integration and Intelligence Software Research at IDC.
Datastream under the hood
Datastream reads CDC events (inserts, updates, and deletes) from source databases, and writes those events with minimal latency to a data destination. It leverages the fact that each database source has its own CDC log—for MySQL it’s the binlog, for Oracle it’s LogMiner—which it uses for its own internal replication and consistency purposes. Using Google-native, agentless, high-scale log reader technology, Datastream can quickly and efficiently generate change streams populated by events based on the database’s CDC log while minimizing performance impact on the source database.
Each generated event includes the entire row of data from the database, with the data type and value of each column. The original source data types, whether it’s, for example, an Oracle NUMBER type or a MySQL NUMERIC type, are normalized into Datastream unified types. The unified types represent a lossless superset of all possible source types, and the normalization means data from different sources can easily be processed and queried downstream in a source-agnostic way. Should a downstream system need to know the original source data type, it can perform a quick API call to Datastream’s Schema Registry, which stores up-to-date, versioned schemas for every data source. This also allows for in-flight downstream schema drift resolution as source database schemas change.
The generated streams of events, referred to as “change streams,” are then written as files, either in JSON or Avro format during preview or in other formats like Parquet in the future, into a Cloud Storage bucket organized by source table and event times. Files are rotated as table schemas change, so events in a single file always have the same schema, as well as on a configurable file size or rotation frequency setting. This way customers can find the best balance between the speed of data availability and the file size that makes the most sense for their business use case.
Through its integration with Dataflow, Datastream powers up-to-date, replicated tables for analytics over BigQuery, and for data replication and synchronization to Cloud SQL and Spanner. Datastream refers to these constantly updated tables as “materialized views.” They are kept up-to-date via Dataflow template-based upserts into Cloud SQL or Spanner, or through consolidations into BigQuery. The consolidations, performed as part of the Dataflow template, take the change streams that are written into a log table in BigQuery, and push those changes into a final table, which mirrors the table from the source.
Datastream offers a variety of secure connectivity methods to sources, so your data is always safe in transit. And with its serverless architecture, Datastream can scale up and down readers and processing power to seamlessly keep up with the speed of data and ensure minimal latency end to end. As data volumes decrease, Datastream automatically scales back down—the result is a “pay for what you use” pricing model, where you never have to pay for idle machines or worry about bottlenecks and delays during data peaks.
Get started with Datastream
Datastream, now available in preview, supports streaming change data from Oracle and MySQL sources, hosted either on-premises or in the cloud, into Cloud Storage. You can start streaming your data today for $2 per GB of data processed by Datastream.
To get started, head over to the Datastream area of your Google Cloud console, under Big Data, and click Create Stream. There you can:
- Initiate stream creation, and see what actions you need to take to set up your source and destination for successful streaming.
- Define your source and destination, whose connectivity information is saved as connection profiles you can re-use for other streams. Sources support multiple connectivity options, with both private and public connectivity options to suit your business needs.
- Select the source data you’d like to stream, and which you’d like to exclude.
- Test your stream to ensure it will be successful when you’re ready to go.
Start your stream and your database’s CDC data will start to flow to your Cloud Storage bucket! From there you can integrate with Dataflow templates to load data into BigQuery, Spanner, or Cloud SQL. Datastream’s preview is supported in us-central1, europe-west1, and asia-east1, with additional regions coming soon.https://www.youtube.com/embed/FZG4w4Vbj38?enablejsapi=1&
Datastream will become generally available later this year, and will soon expand its support to also include PostgreSQL and SQL Server as sources, as well as out-of-the-box integration with BigQuery for easy delivery of up-to-date replicated tables for analytics, and message queues like Pub/Sub for real-time change stream access.
For more resources to help get you started with change streaming, check out the Datastream documentation.
Seven-Eleven Japan Leverages Google Cloud’s Performance and Speed for Real-time Business Insights
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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.
How Constellation Brands’ Direct-to-Customer Tech Delivers Economic Impact across Business Portfolio
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Editor’s note: Today we’re hearing from Ryan Mason, Director, Head of DTC Growth & Strategy, at alcoholic beverage firm, Constellation Brands on the company’s shift to Direct-to-Consumer (DTC) sales and how Google Cloud’s powerful technology stack helped with this transformation.
It’s no secret that consumer businesses have been up-ended in a lasting manner after 18 months of the pandemic. Consumers have been forced to shop differently over the past year – and as a result, they’ve evolved to be more comfortable with online spending and have grown to expect a certain level of convenience. While the e-commerce share of consumer sales has grown steadily over the past decade, the pandemic was the catalyst for the famous “10 years of growth in 3 months” which many argue is here to stay.
Facing this reality head-on, we placed a new emphasis on Direct-to-Consumer (DTC) with our acquisition of Empathy Wines, a DTC-native wine brand that sells directly to consumers via e-commerce. To accelerate our innovation in the DTC space, we added headcount and new functions to the existing Empathy team and empowered the newly-minted DTC group to apply their digital commerce operating model across the rest of the wine and spirits portfolio, which includes Robert Mondavi Winery, Meiomi Wines, The Prisoner Wine Company, High West Whiskey, and more.
One pandemic and one year later, DTC sales have surged in the wine and spirits category with Constellation positioned as a leader armed with a unique and powerful cloud technology stack, best-in-class e-commerce user experiences, modernized fulfillment solutions, and data-driven growth marketing.
Benefits of Going DTC
A report from McKinsey estimates that the strategic business shift to DTC has been accelerated by two years because of the pandemic and argues that consumer brands that want to thrive will need to aim for a 20% DTC business or higher, which is already taking shape in the market: Nike’s direct digital channels are on track to make up 21.5% of the total business by the end of 2021, up from 15.5% in the last fiscal year, and Adidas is aiming for 50% DTC by 2025. But outside of the clear revenue upside, the auxiliary benefits of going DTC are robust.
For Constellation Brands, each of these four pillars ring true, and our shift toward DTC is as much about margin accretion and revenue mix management as it is about consumer insights and data. The added complexities of the alcohol space add wrinkles to our DTC approach and manifest in many areas like consumer shopping preference, shipping and logistics hurdles, and more. In order to win share early and continue to lead the category, we recognized the need to harness the immense amount of first-party data to power impactful and actionable insights.
Our DTC technology architecture has fostered a value chain that is completely digitized: website traffic, marketing expenditures, tasting room transactions, e-commerce transactions, logistics and fulfillment events, cost of goods sold (COGS) and margin profiles, etc. are recorded and stored in a data warehouse in real time. For the first time, at any given moment, we can easily and deterministically answer complex business questions like “what is the age and gender distribution of my customers from Los Angeles who have purchased SKU X from Brand.com Y in the last 6 months? What is the cohort net promoter score? Did that increase after we introduced same-day shipping in this zip code? By how much?”
The ability to answer these questions and understand the root causes allows us to stay nimble with product offerings and iterate marketing strategies at the speed of consumer preference. Further, it enables us to optimize our omnichannel presence in the same manner by leaning on DTC consumer insights to develop valuable strategies with key wholesale distribution partners and 3-Tier eCommerce partners like Drizly and Instacart. At its core, Constellation’s DTC practice is designed to be the consumer-centric “tip-of-the-spear” responsible for generating insights from which all sales channels, including wholesale, can benefit.
Constellation’s DTC technology approach prioritizes consumer-centricity and insights generation
We have taken a modern approach to building a digital commerce technology stack, leveraging a hub-and-spoke model built around Shopify Plus and other key emergent technology providers like email provider Klaviyo, loyalty platform Yotpo, Net Promoter Score measurer Delighted, Customer Service module Gorgias, payments processor Stripe, event reservations platform Tock, and many more. For digital marketing and analytics, we use Google Cloud and Google Marketing Platform, which includes products like Analytics 360, Tag Manager 360, and Search Ads 360.
To help gather, organize, and store all of the inbound data from the ecosystem, we partnered with SoundCommerce, a data processing platform for eCommerce businesses. Together with SoundCommerce, we are able to automate data ingestion from all endpoints into a central data warehouse in Google BigQuery. With BigQuery, our data team is able to break data silos and quickly analyze large volumes of data that help unlock actionable insights about our business. BigQuery itself allows for out-of-the-box predictive analytics using SQL via BigQuery ML, and a key differentiator for us is that all Google Marketing Platform data is natively accessible for analysis within BigQuery.
But data possession only addresses half of the opportunity: we needed a powerful and modern business intelligence platform to help make sense of the vast amounts of data flowing into the system. Core to the search was to find a partner that approached BI in a way that fit with our future-looking strategy.
Our DTC team relies on the accurate measurement of variable metrics like Customer Acquisition Cost (CAC), Customer Lifetime Value (CLV), Churn, and Net Promoter Score (NPS) as a bellwether of the health of the business and monitoring these figures on a daily basis is paramount to success. To enable us to keep an accurate pulse on strategic KPIs, we considered several incumbent BI platforms. Ultimately we selected Google Cloud’s Looker for a range of benefits that separated it from the rest of the pack.
From a vision perspective, in this particular case we felt Looker was most aligned with our belief that better decisions are made when everyone has access to accurate, up-to-date information. Looker allows us to realize that vision by surfacing data in a simple web-based interface that empowers everyone to take action with real-time data on critical commercial activities. Furthermore, Looker’s ability to automate and distribute formatted modules to a myriad of stakeholders on a regular cadence increases data literacy and business performance transparency.
From a product perspective, we chose Looker for it’s cloud offering, web-based interface, and centralized, agile modeling layer that creates a trusted environment for all users to confidently interact with data — without any actual data extraction. While other BI tools have centralized semantic layers that require skilled IT resources, we’ve experienced that those can lead to bottlenecks and limited agility. With Looker’s semantic layer, LookML, our BI Team, led by Peter Donald, can easily build upon their SQL knowledge to add both a high degree of control as well as flexibility to our data model. The fully browser-based development environment allows the data team to rapidly develop, test, and deploy code and is backed by robust and seamless Git source code management.
In parallel, LookML empowers business users to collaborate without the need for advanced SQL knowledge. Our data team curates interactive data experiences with Looker to help scale access and adoption. Business users can explore ad hoc analysis, create dashboards, and develop custom data experiences in the web-based environment to get the answers they need without relying on IT resources each time they have a new question, while also maintaining the confidence that the underlying data will always be accurate. This helps us meet our primary goal of providing all businesses users with the data access they need to monitor the pulse of key metrics in near real-time.
Impact and future of DTC BI at Constellation
In short order, taking a modern and integrated approach to the DTC technology stack has delivered economic impact across the portfolio, helping our team understand and combat customer churn, increase conversion rates, and optimize the customer acquisition cost (CAC) and customer lifetime value (CLV) ratios. Perhaps most important is the benefit it can provide to the customer base. Mining customer data and consumer behavior generates data into what our customers are seeking, giving us insights to supply more, or less of it. For example, observing sales velocity and conversion rates by SKU or by region can help us better understand changes in customer taste profiles and fluctuations in demand, providing the foundation for a more powerful innovation pipeline and more effective sales and distribution tactics in wholesale. Our team has also been an early pilot tester for Looker’s new integration with Customer Match, which contributes to the virtuous cycle between data insight and data activation. In the future, our plan is to leverage this cycle to amplify the impact of Google Ads across Search, Shopping, and YouTube placements for the wine and spirits portfolio.
The operational impact of Looker is also substantial: our team estimates that the number of hours needed to reach critical business decisions has been reduced by nearly 60%, boosting productivity and accelerating the daily operating rhythm. A thoughtfully curated technology stack together with a modern BI solution allows us to stay at the vanguard of the industry. While the DTC sales channel is not designed to surpass the core business of wholesale for Constellation in terms of size, the approach enables unparalleled insights and measurement abilities that will pay dividends for the entire business for years to come.
How Do You Cut Costs and Improve Staff Productivity, and Business Visibility? Ascend Money Has an Answer
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Hundreds of millions of residents of South East Asia have only limited access to banking and finance services. However, help is at hand. One of South East Asia’s largest fintech businesses, Ascend Money is using digital technologies to realize its mission of enabling as many people as possible to access innovative financial services and live better lives.
According to Ascend Money, 60 percent of the region’s 620 million residents do not have access to a full suite of financial services. Even a relatively small proportion of this market represents a big opportunity for the business.
Ascend Money’s offerings include the TrueMoney regional payment platform for underserved and digital consumers. The TrueMoney platform supports more than 40 million consumers across six countries: Cambodia, Indonesia, Myanmar, the Philippines, Thailand, and Vietnam.
“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business.”
– Abraham Jarrett, Head of Engineering, Ascend Money
TrueMoney also provides an e-wallet app that provides easy ways to top up mobile phones, undertake online shopping, and pay for products and services; a network of 65,000 branded shops; and international remittances, initially between Thailand and Myanmar. In addition, Ascend Money offers financial products to small to medium businesses.
Ascend Money is part of the South East Asian online business Ascend Group, which also operates e-commerce, e-procurement, data centers, cloud services, fulfilment, and digital marketing services.
Cost is a key criteria
Ascend Money initially started operations using physical infrastructure and on-premises workforce productivity applications. However, as the business grew its customer base and expanded into new markets, its technology leaders began to explore options to improve value for money; reduce the maintenance load on in-house team members; improve its data management and analysis; and collaborate more effectively.
“Operating our own on-premises infrastructure entails a higher cost of ownership and maintenance, as well as requiring us to scale up our hardware as needed,” says Abraham Jarrett, Head of Engineering, Ascend Money. “We conducted an evaluation and found that moving to the cloud would deliver a range of benefits.”
G Suite and Google Cloud Platform the best fit
The business evaluated solutions available in the market and opted to move to G Suite and Google Cloud Platform.
“Moving to Google Cloud Platform was an optimization exercise, with lowering our costs our primary goal, and achieving better performance an added benefit,” says Jarrett. Google Cloud Platform monitoring, diagnostic, and analytics tools have enabled Ascend Money to reduce its infrastructure spending, becoming more efficient and cost-effective in the process.
“Managing software is a big cost for us. Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.”
– Abraham Jarrett, Head of Engineering, Ascend Money
The Ascend Money team saw Google Kubernetes Engine as enabling a seamless way of transitioning from an on-premises data center to a cloud service. “We considered industry trends such as what people were adopting and who we could hire when making our decision,” says Jarrett. “Engineering teams are focusing on using Kubernetes open source container management at scale and as a way of doing business, which was attractive to us.
“Google Kubernetes Engine presented the easiest way to manage and orchestrate our containers, and drive us away from our existing infrastructure.”
BigQuery best for cost and ease of use
Ascend Money also reviewed data infrastructure solutions, with its business intelligence and data platform teams considering a range of options. The teams quickly ruled out an on-premises solution due to the capital investment required and opted for a cloud service. Following a rigorous evaluation of Google Cloud Platform against the services offered by another cloud provider, Ascend Money opted to run on Google Cloud.
“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business,” says Jarrett. The business is now running an architecture comprising a BigQuery analytics data warehouse; Cloud Storage to store raw and archive data; Cloud Dataflow to process stream and batch data, and Cloud Pub/Sub for event ingestion and delivery.
“We’re expanding our utilization of BigQuery and other services on a daily basis,” says Jarrett.
Ascend Money is also using a range of Google Cloud Platform services for the infrastructure outside its data platform, including Stackdriver logging and monitoring; Cloud KMS to manage cryptographic keys for its cloud services, Cloud Build to undertake continuous integration, delivery, and deployment; Cloud DNS to provide domain name system services; and Cloud Functions to enable its developers to run and scale code in the cloud.
With Google Cloud Platform, Ascend Money is now processing about 12GB of batch data per day and streaming data from about 200 data marts per day in Thailand alone.
Cost effective scalability and faster to market
Google Kubernetes Engine has enabled the business to scale and deliver to market faster. “We can build on the platform, take the application and container and deploy them to scale out,” says Jarrett. “The Google Kubernetes Engine orchestration mechanism that provides containerized, elastic scalability is extremely important in allowing us to manage costs and expend our effort efficiently. Through Kubernetes, we can write once and deploy everywhere.”
With Google Cloud Platform, the business has saved 3,000,000 THB (about US$90,000) in licensing costs and, through automation tools, reduced the time to complete infrastructure activities by 50 percent.
“We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion.”
– Abraham Jarrett, Head of Engineering, Ascend Money
Ascend Money’s entire workforce is now using G Suite to collaborate and operate productively. The business is achieving a range of benefits including being able to get new team members up and running more quickly and reduced cost. “Managing software is a big cost for us,” says Jarrett. “Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.” Meanwhile, the shorter time to onboard team members to G Suite is paying off with improved productivity and an accelerated ability to collaborate.
Ascend Money team members primarily use Sheets, Slides, and Docs to create internal materials, including product requirement documents in Docs and workforce and project planning spreadsheets in Sheets.
The organization also uses Hangouts Meet to collaborate when working from different locations. “We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion,” says Jarrett. “People can work from home and we have five regions plus Thailand where they can collaborate live on a document, such as a slide deck for board meetings or meetings with our chief executive officer, head of engineering, or other senior managers.”
20 hours per week saved
Jarrett describes the time savings of using web browser-based productivity software and G Suite as saving them an average of 20 hours per week. This has improved productivity and the quality of life for Ascend Money’s hard-working team, increasing overall staff satisfaction.
“The ability to work remotely is a big win for us,” Jarrett says. “Our team members can keep one computer at work and one at home and access the same information, they can work in a plane while it sits on the tarmac, or update a Google Doc or a Google Sheet in real time on their phone. They can work anytime, from any location, as long as they have an internet connection. They have less dead time, meaning more uptime.”
With Google Cloud Platform and G Suite, Ascend Money is ideally positioned to support growing demand for its fintech services in South East Asia, particularly among people with limited access to banking. “We have the agility and dynamism to support rising demand and look forward to continuing to work with Google to realize our business ambitions,” says Jarrett.
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