Migrating MySQL to Spanner? Here’s What You Need to Know
5610
Of your peers have already read this article.
2:00 Minutes
The most insightful time you'll spend today!
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.
Google Data Cloud: The Catalyst for Modern App Development and Innovation
1376
Of your peers have already read this article.
3:30 Minutes
The most insightful time you'll spend today!
97 zettabytes was the estimated volume of data generated worldwide in 20221. This sort of explosion in data volume is happening in every enterprise. Now imagine being able to access all this data you own from anywhere, at any time, analyze it, and leverage its insights to innovate your products and services? One of the biggest barriers to fulfilling this vision is the complexity inherent in dealing with data trapped across silos in an enterprise. Google Data Cloud offers a unified, open, and intelligent platform for innovation, allowing you to integrate your data on a common architectural platform. Across industries, organizations are able to reimagine their data possibilities in entirely new ways and quickly build applications that delight their customers.
Siloed data: The barrier to speed and innovation
Digital technologies ranging from transaction processing to analytics and AI/ML use data to help enterprises understand their customers better. And the pace of innovation has naturally accelerated as organizations learn, adapt, and race to build the next generation of applications and services to compete for customers and meet their needs. At Next 2022, we made a prediction that the barriers between transactional and analytical workloads will mostly disappear.
Traditionally, data architectures have separated transactional and analytical systems—and that’s for good reason. Transactional databases are optimized for fast reads and writes, and analytical databases are optimized for analyzing and aggregating large data sets. This has siloed enterprise data systems, leaving many IT teams struggling to piece together solutions. The result has been time consuming, expensive, and complicated fixes to support intelligent, data-driven applications.
But, with the introduction of new technologies, a more time-efficient and cost-effective approach is possible. Customers now expect to see personalized recommendations and tailored experiences from their applications. With hybrid systems that support both transactional and analytical processing on the same data, without impacting performance, these systems now work together to generate timely, actionable insights that can be used to create better experiences and accelerate business outcomes.
According to a 2022 research paper from IDC, unifying data across silos via a data cloud is the foundational capability enterprises need to gain new insights on rapidly changing conditions and to enable operational intelligence. The modern data cloud provides unified, connected, scalable, secure, extensible, and open data, analytics, and AI/ML services. In this platform, everything is connected to everything else.
Why reducing data barriers delivers more value
The primary benefit of a unified data cloud is that it provides an intuitive and timely way to represent data and allows easy access to related data points. By unifying their data, enterprises are able to:
- Ingest data faster – for operational intelligence
- Unify data across silos – for new insights
- Share data with partners – for collaborative problem solving
- Change forecasting models – to understand and prepare for shifting markets
- Iterate with decision-making scenarios – to ensure agile responses
- Train models on historical data – to build smarter applications
As generative AI applications akin to Bard, an early experiment by Google, become available in the workplace, it will be more important than ever for organizations to have a unified data landscape to holistically train and validate their proprietary large language models.
With these benefits enterprises can accelerate their digital transformation in order to thrive in our increasingly complex digital environment. A survey of more than 800 IT leaders indicated that using a data cloud enabled them to significantly improve employee productivity, operational efficiency, innovation, and customer experience, among others.
Build modern apps with a unified and integrated data cloud
Google Cloud technologies and capabilities reduce the friction between transactional and analytical workloads and make it easier for developers to build applications, and to glean real-time insights. Here are a few examples.
- AlloyDB for PostgreSQL, a fully managed PostgreSQL-compatible database, and AlloyDB Omni, the recently launched downloadable edition of AlloyDB, can analyze transactional data in real time. AlloyDB is more than four times faster for transactional workloads and up to a 100 times faster for analytical queries compared to standard PostgreSQL, according to our performance tests. This kind of performance makes AlloyDB the ideal database for hybrid transactional and analytical processing (HTAP) workloads.
- Datastream for BigQuery, a serverless change data capture and replication service, provides simple and easy real time data replication from transactional databases like AlloyDB, PostgreSQL, MySQL and Oracle directly into BigQuery, Google Cloud’s enterprise data warehouse.
- And, query federation with Cloud Spanner, Cloud SQL, and Cloud Bigtable, make data available right from the BigQuery console allowing customers to analyze data in real-time in transactional databases.
Speed up deployments and lower costs
By reducing data barriers, we’re taking a fundamentally different approach that allows organizations to be more innovative, efficient, and customer-focused by providing:
- Built-in industry leading AI and ML that helps organizations not only build improved insights, but also automate core business processes and enable deep ML-driven product innovation.
- Best-in-class flexibility. Integration with open source standards and APIs ensures portability and extensibility to prevent lock-in. Plus, choice of deployment options means easy interoperability with existing solutions and investments.
- The most unified data platform with the ability to manage every stage of the data lifecycle, from running operational databases to managing analytics applications across data warehouses and lakes to rich data-driven experiences.
- Fully managed database services that free up DBA/DevOps time to focus on high-value work that is more profitable to the business. Organizations that switch from self-managed databases eliminate manual work, realize significant cost savings, reduce risk from security breaches and downtime and increase productivity and innovation. In an IDC survey, for example, Cloud SQL customers achieve an average three-year ROI of 246% because of the value and efficiencies this fully managed service delivers.
Google Data Cloud improves the efficiency and productivity of your teams, resulting in increased innovation across your organization. This is the unified, open approach to data-driven transformation bringing unmatched speed, scale, security, and with AI built in.
In case you missed it: Check out our Data Cloud and AI Summit that happened on March 29th, to learn more about the latest innovations across databases, data analytics, BI and AI. In addition, learn more about the value of managed database services like Cloud SQL in this IDC study.
6166
Of your peers have already watched this video.
18:00 Minutes
The most insightful time you'll spend today!
An Overview of Google’s Data Cloud
Data access, management and privacy has been at the center of priorities for enterprises that are aiming to be more agile, reliable and data-driven. Google Cloud’s technology innovations spanning products like BigQuery, Spanner, Looker and VertexAI help organizations navigate the complexities related to siloed data in large volumes sprawled across databases, data lakes, data warehouses, and data marts in multiple clouds and on-premises. Watch the video to learn how companies are building data on Google Cloud for better analysis, security and management to achieve bottomline!
What is Cloud SQL for SQL Server? And What Do IT Departments Say About It?
3793
Of your peers have already read this article.
4:30 Minutes
The most insightful time you'll spend today!
At Google Cloud, we develop our database services to meet the needs of enterprise teams wherever they are in their cloud journey. That is why we announce launched Cloud SQL for SQL Server and make it available to all of our customers.
This addition to our database lineup means you can migrate your enterprise SQL Server workloads to Google Cloud easily, and take advantage of fully managed services.
Cloud SQL for SQL Server is a key component when onboarding your existing applications and infrastructure to get the benefits of a fully managed and compatible database that will reduce your operational costs and overhead.
Highlights of Cloud SQL for SQL Server
Cloud SQL for SQL Server brings some key benefits that can help you run workloads easily.
- Compatibility: Cloud SQL for SQL Server offers multiple editions of the current version of SQL Server and works with popular clients such as SQL Server Management Studio.
- Flexible backups: Schedule automatic daily backups or run them on-demand.
- Scalability: Enable the automatic storage increase configuration and Cloud SQL will add storage capacity whenever you approach your limit. Easily scale up your customized machines’ memory and processor cores as necessary.
- Built-in high availability: Cloud SQL for SQL Server has built-in high availability enabled for all editions that synchronously replicates data to each zone’s regional persistent disk.
Cloud SQL for SQL Server is currently available regions and will integrate with our existing Cloud SQL functionality, such as connectivity via the Cloud SQL Proxy. Here’s a look at creating a new instance.
Early adopters of Cloud SQL for SQL Server have been using the service talk about the value for onboarding more workloads.
“Cloud SQL for SQL Server was very easy and fast to get up and going.” says Andrew P. Toi, database engineer lead at advertising management software company WideOrbit Inc. “With built-in high availability that can be used across editions, I can reduce the cost and overhead of managing databases dramatically, especially for smaller workloads”
And desktop virtualization infrastructure company Itopia had this to say. “Cloud SQL Server dramatically reduces the complexity and cost involved in deploying cloud desktop infrastructure for our customers,” says Ubaldo Don, CTO of Itopia. “It turns a bulky pillar of infrastructure into a one-click service, reducing IT service management overhead.”
Google Cloud Helped Digitec Galaxus Personalize Over 2 Million Newsletters in a Week
8285
Of your peers have already read this article.
3:00 Minutes
The most insightful time you'll spend today!
Digitec Galaxus AG is the biggest online retailer in Switzerland, operating two online stores: Digitec, Switzerland’s online market leader for consumer electronics and media products, and Galaxus, the largest Swiss online shop with a steadily growing range of consistently low-priced products for almost all daily needs.
Known for its efficient, personalized shopping experiences, it’s clear that Digitec Galaxus understands what it takes to deliver a platform that is interesting and relevant to customers every time they shop.
The problem: Personalizing decisions for every situation
Digitec Galaxus already had established an engine to help them personalize experiences for shoppers when they reached out to Google Cloud. They had multiple recommendation systems in place and were also extensive early adopters of Recommendations AI, which already enabled them to offer personalized content in places like their homepages, product detail pages, and their newsletter.
But those same systems sometimes made it difficult to understand how best to combine and optimize to create the most personalized experiences for their shoppers. Their requirements were threefold:
- Personalization: They have over 12 recommenders they can display on the app, however they would like to contextualize this and choose different recommenders (which in turn select the items) for different users. Furthermore they would like to exploit existing trends as well as experiment with new ones.
- Latency: They would like to ensure that the solution is architected so that the ranked list of recommenders can be retrieved with sub 50 ms latency.
- End-to-end easy to maintain & generalizable/modular architecture: Digitec wanted the solution to be architected using an easy to maintain, open source stack, complete with all MLops capabilities required to train and use contextual bandits models. It was also important to them that it is built in a modular fashion such that it can be adapted easily to other use cases which have in mind such as recommendations on the homepage, Smartags and more .
To improve, they asked us to help them implement a machine learning (ML) contextual bandit based recommender system on Google Cloud taking all the above factors into consideration to take their personalization to the next level.
Contextual bandits algorithms are a simplified form of reinforcement learning and help aid real-world decision making by factoring in additional information about the visitor (context) to help learn what is most engaging for each individual. They also excel at exploiting trends which work well, as well as exploring new untested trends which can yield potentially even better results. For instance, imagine that you are personalizing a homepage image where you could show a comfy living room couch or pet supplies.
Without a contextual bandit algorithm, one of these images would be shown to someone at random without considering information you may have observed about them during previous visits. Contextual bandits enable businesses to consider outside context, such as previously visited pages or other purchases, and then observe the final outcome (a click on the image) to help determine what works best.
Creating a personalization system with contextual bandits
While Digitec Galaxus heavily personalizes their website homepages, they are very very sensitive and also require more cross-team collaboration to update and make changes.
Together with the Digitec Galaxus team, we decided to narrow the scope and focus on building a contextual bandit personalization system for the newsletter first. The digitec Galaxus team has complete control over newsletter decisions and testing various ML experiments on a newsletter would have less chance of adverse revenue impact than a website homepage.
The main goal was to architect a system that could be easily ported over to the homepage and other services offered by Digitec with minimal adaptations. It would also need to satisfy the functional and non-functional requirements of the homepage as well as other internal use cases.
Below is a diagram of how the newsletter’s personalization recommendation system works:
- The system is given some context features about the newsletter subscriber such as their purchase history and demographics. Features are sometimes referred to as variables or attributes, and can vary widely depending on what data is being analyzed.
- The contextual bandit model trains recommendations using those context features and 12 available recommenders (potential actions).
- The model then calculates which action is most likely to enhance the chance of reward (a user clicking in the newsletter) and also minimize the problem (an unsubscribe).
Calculating whether a click was a newsletter or an unsubscribe enabled the system to optimize for increasing clicks and avoid showing non-relevant content to the user (click-bait). This enabled Digitec Galaxus to exploit popular trends while also exploring potentially better-performing trends.
How Google Cloud helps
The newsletter context-driven personalization system was built on Google Cloud architecture using the ML recommendation training and prediction solutions available within our ecosystem.
Below is a diagram of the high-level architecture used:
The architecture covers three phases of generating context-driven ML predictions, including:
ML Development: Designing and building the ML models and pipeline
Vertex Notebooks are used as data science environments for experimentation and prototyping. Notebooks are also used to implement model training, scoring components, and pipelines. The source code is version controlled in Github. A continuous integration (CI) pipeline is set up to automatically run unit tests, build pipeline components, and store the container images to Cloud Container Registry.
ML Training: Large-scale training and storing of ML models
The training pipeline is executed on Vertex Pipelines. In essence, the pipeline trains the model using new training data extracted from BigQuery and produces a trained, validated contextual bandit model stored in the model registry. In our system, the model registry is a curated Cloud Storage.
The training pipeline uses Dataflow for large scale data extraction, validation, processing, and model evaluation, and Vertex Training for large-scale distributed training of the model. AI Platform Pipelines also stores artifacts, the output of training models, produced by the various pipeline steps to Cloud Storage. Information about these artifacts are then stored in an ML metadata database in Cloud SQL. To learn more about how to build a Continuous Training Pipeline, read the documentation guide.
ML Serving: Deploying new algorithms and experiments in production
The training pipeline uses batch prediction to generate many predictions at once using AI Platform Pipelines, allowing Digitec Galaxus to score large data sets. Once the predictions are produced, they are stored in Cloud Datastore for consumption. The pipeline uses the most recent contextual bandit model in the model registry to evaluate the inference dataset in BigQuery and give a ranked list of the best newsletters for each user, and persist it in Datastore. A Cloud Function is provided as a REST/HTTP endpoint to retrieve the precomputed predictions from Datastore.
All components of the code and architecture are modular and easy to use, which means they can be adapted and tweaked to several other use cases within the company as well.
Better newsletter predictions for millions
The newsletter prediction system was first deployed in production in February, and Digitec Galaxus has been using it to personalize over 2 million newsletters a week for subscribers. The results have been impressive, 50% higher than our baseline. However, the collaboration is still ongoing to improve the results even more.
“Working at this level in direct exchange with Google’s machine learning experts is a unique opportunity for us. The use of contextual bandits in the targeting of our recommendations enables us to pursue completely new approaches in personalization by also personalizing the delivery of the respective recommender to the user. We have already achieved good results in our newsletter in initial experiments and are now working on extending the approach to the entire newsletter by including more contextual data about the bandits arms. Furthermore, as a next step, we intend to apply the system to our online store as well, in order to provide our users with an even more personalized experience. To build this scalable solution, we are using Google’s open source tools such as TFX and TF Agents, as well as Google Cloud Services such as Compute Engine, Cloud Machine Learning Engine, Kubernetes Engine and Cloud Dataflow.”—Christian Sager, Product Owner, Personalization ( Digitec Galaxus)
Since the existing architecture and system is also dynamic, it will automatically adapt to new behaviours, trends, and users. As a result, Digitec Galaxus plans to re-use the same components and extend the existing system to help them improve the personalization of their homepage and other current use cases they have within the company. Beyond clicks and user engagement, the system’s flexibility also allows for future optimization of other criteria. It’s a very exciting time and we can’t wait to see what they build next!
BURGER KING Germany: Serving Up Marketing Insights and Supply Chain Visibility Easily
13932
Of your peers have already read this article.
8:30 Minutes
The most insightful time you'll spend today!
Do hamburgers really come from Hamburg? This may still be a matter of debate, but the popularity of American-style burger joints not just in Hamburg but all over Germany, is clear. Germany’s top two fast food companies are both burger chains. One of them is BURGER KING®, a global brand that welcomes more than 11 million customers worldwide every day. The company arrived in Germany in 1976, when its first restaurant opened in Berlin. It now operates more than 100 restaurants across Germany, with franchisees operating more than 600 restaurants of their own.
“In the fast food industry, being able to move quickly is very important. That means delivering the right promotion to our app or launching a viral campaign within days. To do that across more than 700 restaurants, we need the support of the right technology.”
—Oliver Mielentz, IT Manager, BURGER KING® Deutschland GmbH
Previously a subsidiary of the U.S. business, BURGER KING® Germany became an independent company in 2015. As a result, it needed to develop its own IT infrastructure, and the changeover needed to happen fast. “We had to put in place systems that would work for the entire network of franchisees and enable us to easily roll out campaigns,” explains Oliver Mielentz, IT Manager at BURGER KING® Deutschland GmbH.
With the help of Google Cloud Premier partner Cloudwürdig, BURGER KING® Germany chose Google Cloud and G Suite as the right combination to suit its needs.
“In the fast food industry, being able to move quickly is very important,” says Oliver. “That means delivering the right promotion to our app or launching a viral campaign within days. To do that across more than 700 restaurants, we need the support of the right technology.”
Building a franchisee platform in just three months
When a business has multiple franchisees, it’s important to make sure everyone is on the same page, especially in the fast-paced fast food environment. “We have to collate data from all our franchisees and produce reports quickly in order to react to changes in customer behavior,” explains Oliver. “That means processing every transaction that takes place in our restaurants.” Following the restructure, BURGER KING® Germany also needed to build a secure invoicing system with data storage and optimize its communication channels.
“Using Tableau with BigQuery, we’re able to produce reports very quickly. Previously, it took much longer, as data had to be fetched manually. Our reaction time is now almost a business day faster.”
—Oliver Mielentz, IT Manager, BURGER KING® Deutschland GmbH
With support from Witter-IT, BURGER KING® Germany chose Cloudwürdig to build its BKD Connect internal platform on Google Cloud. Thanks to the ready-to-go tools on Google Cloud, it was able to put its invoicing system and data warehouse in place in just three months.
For the BURGER KING® Germany data warehouse, Cloudwürdig built an ETL pipeline that channels ticket data for every sale into BigQuery. “Data is gathered from the restaurants,” says Oliver, “and using Tableau with BigQuery, we’re able to produce reports very quickly. Previously, it took much longer, as data had to be fetched manually. Our reaction time is now almost a business day faster.”
As the ticket data for every transaction is stored in BigQuery, the marketing team can easily see exactly which products are selling well. That’s crucial for tweaking promotions as well as monitoring the supply chain to make sure enough produce is delivered to restaurants in response to changes in demand.
“Thanks to BigQuery, we have a speedy data pipeline that enables us to react on the same day to changes in the market and eliminate bottlenecks in production,” says Oliver.
Switching to G Suite to improve communication
To enable franchisees to sign in to its BKD Connect Platform, BURGER KING® Germany needed a secure authentication system. To solve that problem, it chose to provide franchisees with G Suite accounts. “It’s really easy to set up a new franchisee on the platform. I just create a new G Suite account and Drive folder for it, and it’s ready to go,” says Oliver. G Suite also helps the franchise network to run efficiently, as daily reports are automatically saved to Drive and shared to the appropriate regional network. “Thanks to that system, it’s much easier for any team at headquarters to access the information it needs,” Oliver explains.
BURGER KING® Germany also recently extended its use of G Suite across the whole company. “Following an evaluation of our previous email and productivity software, I made the decision to switch solely to G Suite,” says Oliver. BURGER KING® Germany employees now use Gmail, Calendar, and Drive for their day-to-day productivity needs. “We only just completed the migration, but already, everyone’s happy,” says Oliver. “It’s so easy to share a file using Drive or set up a meeting on Calendar.”
“We’re big fans of Hangouts Meet, and we have two rooms here at our Hanover headquarters equipped with Hangouts Meet hardware,” Oliver adds. “The speech quality is good, and it’s helpful to be able to see every participant, especially when you’re running a meeting with multiple franchisees.”
Optimizing infrastructure to power innovative campaigns
The BURGER KING® app, available for iOS and Android, helps the company to deliver a great customer experience. Through their MyBK accounts, guests can access coupons and special promotions. “We had a really interesting campaign for Easter: guests used the app to hunt for virtual Easter eggs,” explains Oliver. “We knew it was going to be big, and our previous back end wouldn’t have been able to handle the traffic.”
To enable the marketing campaign to go ahead, BURGER KING® Germany moved the back end of the app, along with its website, to Google Cloud. For developing and running its web and app back ends, it now uses App Engine and virtual machines on Compute Engine, as well as Memorystore and Cloud Functions. For monitoring and logging, it uses Stackdriver, and Cloud CDN and Cloud DNS to easily handle its traffic.
“We ran the campaign without any performance issues, even though we were receiving several million hits a day,” says Oliver. Since migrating the back end to Google Cloud, the marketing team also launched the popular “Escape the Clown” campaign. “That campaign blew our minds!” says Oliver. “It wouldn’t have been possible without Google Cloud, because it required a lot of back end capacity.”
To develop the app infrastructure it needs, BURGER KING® Germany relies on Cloudwürdig. “Working with Cloudwürdig is great because the team has the same agile mindset as us,” says Oliver. “When we have a new idea, we just set up a meeting, and in a couple of days the new infrastructure is in place. For Escape the Clown, it only took a few weeks to get everything ready to launch.”
Leveraging integrated tools to grow the business
Using Google Cloud together with G Suite enables BURGER KING® Germany to run its franchise network efficiently, while keeping its IT team lean. “Google Cloud and G Suite are the perfect fit for the way of working at BURGER KING® Germany,” says Oliver. “Many of the company’s operatives are often on the road, visiting restaurants and franchisees. With these tools, they can work flexibly and react quickly to the situation on the ground.”
“In order to grow the business, we need to use the data we receive every day to understand exactly what is happening in our restaurants. With the tools provided by Google Cloud, we can get more guests through the door and offer them a better experience.”
—Oliver Mielentz, IT Manager, BURGER KING® Deutschland GmbH
It also helps to keep infrastructure costs under control. “With Google Cloud, we only pay for what we use, which is really important for us,” Oliver explains. “It means we can scale up quickly if we see an opportunity to react to a trend in customer behavior and launch a new marketing campaign that resonates with the moment. When it’s finished, we can then scale down again, and that definitely saves us money.”
BURGER KING® is now working with Cloudwürdig to add more functionality to the BURGER KING® app using Google Kubernetes Engine. “We like to work with customers long-term to support their digital transformation. BURGER KING® Germany is a great example of how one project can develop into a great collaboration,” says Benny Woletz, Managing Director of Cloudwürdig.
BURGER KING® also plans to expand its presence in Germany and gain a greater market share by further tailoring both its marketing and the way it runs its restaurants to answer its guests’ needs. “In order to grow the business, we need to use the data we receive every day to understand exactly what is happening in our restaurants,” says Oliver. “With the tools provided by Google Cloud, we can get more guests through the door and offer them a better experience.”
More Relevant Stories for Your Company
NCR’s Emerald Leverages Google Cloud to Help Grocers Boost Operational Agility
In recent years, the grocery industry has had to shift to facilitate a wider variety of checkout journeys for customers. This has meant ensuring a richer transaction mix, including mobile shopping, online shopping, in-store checkout, cashierless checkout or any combination thereof like buy online, pickup in store (BOPIS). What’s more,
Revolutionizing Healthcare Operations with Data Engine Accelerators
Healthcare leaders are increasingly challenged to drive operational improvements throughout their facilities, and inefficiencies can cost organizations both time and money and impact patient outcomes. Additionally, staffing shortages and employee burnout remain a major concern in healthcare. Addressing these challenges can help healthcare providers improve organizational operations, patient experiences and
Skincare Firm Scales 4X in Minutes with SAP on Google Cloud
As the number one skincare brand in the United States, Rodan + Fields must support its team of over 300,000 of independent contractors as well as work to ensure a really personalized experience for customers. To keep pace with the company’s growth, Rodan + Fields realized it needed a more
No More ‘Tab Game’ with Easy Tutorials on Google Cloud Console
When it comes to learning how to implement some technology, we all have our own version of what I call the "tab game"—that is, your setup for all the tabs and windows you need open at once. You may have several monitors so you can see documentation, your IDE, and
