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.

Editor’s note: PedidosYa is the market leader for online food ordering in Latin America, serving 15 markets and over 400 cities. It’s also one of the largest brands within the German multinational company Delivery Hero SE. With over 20 million app downloads, PedidosYa provides the best online delivery experience through 71,000+ online partners, including restaurants, shops, drugstores, and specialized markets. 

Having constant access to fresh customer data is a key requirement for PedidosYa to improve and innovate our customer’s experience. Our internal stakeholders also require faster insights to drive agile business decisions. Back in early 2020, PedidosYa’s leadership tasked the data team to make the impossible possible. Our team’s mission was to democratize data by providing universal and secure access while creating a comprehensive information ecosystem across PedidosYa. We also had to achieve this goal while keeping costs under control— even during the migration stage and removing operational bottlenecks. 

Challenges with legacy cloud infrastructure

PedidosYa first built its data platform on top of AWS. Our data warehouse ran on Redshift, and our data lake was in S3. We used Presto and Hue as the user interfaces for our data analysts. However, maintaining this infrastructure was a daunting task. Our legacy platform couldn’t keep up with the increasing analytics demands. For example, the data stored on S3 complemented by Presto/Hue required high operational overhead. This was because Presto and our IAM (identity access management) didn’t integrate well in our legacy ecosystem. Managing individual users and mapping IAM roles with groups and Kerberos was operationally time-consuming and costly. Further, sharding access on the S3 files was far too complicated to enable seamless ACLs (access control lists).  

There were also challenges with workload management. Our data warehouse had batch data loaded overnight. If one analyst scheduled a query to run during the overnight ETL (extract, transform, load) workload, it would disrupt the current ETL task. This could stop the entire data pipeline. We’d have to wait until data engineers intervened with a manual fix.

It was also difficult to understand whether a query error was due to performance issues or platform resource exhaustion. This lack of clarity affected our data analysts’ ability to autonomously improve querying efficiency. Data team members needed to manually inspect personal queries looking for performance issues. Also,  the current architecture was prone to a ‘tragedy of the commons’ situation; it was seen as an unlimited and free resource. As a result, it was impossible to disentangle the infrastructure from different stakeholder teams, as all had very different needs. 

The decision to modernize our data warehouse

Given the growing challenges from our legacy platform, our tech team decided to transform our analytics environment with a modern data warehouse. They required the following key criteria from their next data platform: 

• Scalability – The ability to grow with elastic infrastructure.
• Cost control – Cost management and transparency. These factors promote efficiency and ownership—both key aspects of data democratization.
• Metadata management – Intuitive data platform focusing on users’ previous SQL knowledge. Plus, being able to enrich the informational ecosystem with metadata,  to diminish data gatekeepers.
• Ease of management – The team needed to reduce operational costs with a serverless solution. Data engineers wanted to focus on their key roles rather than acting as database administrators and infrastructure engineers. The team also wanted much higher availability, and to reduce the impact of maintenance windows and vacuum/analysis.
• Data governance and access rights – With a growing employee base with varying data access requirements, the team needed a simple yet comprehensive solution to understand and track user access to data.

Migrating to Google Cloud

After exploring other alternatives, we concluded Google Cloud had an answer to each of our decision drivers. Google Cloud’s serverless, managed, and integrated data platform, coupled with its seamless integration across open-source solutions, was the perfect answer for our organization. In particular, the natural integration with Airflow as a job orchestrator and Kubernetes for flexible on-demand infrastructure was key.  

We  used Dataflow together with Pub/Sub and Cloud Functions for our data ingestion requirements, which has made our deployment process with Terraform seamless. Because we set up everything in our environment programmatically, operation time has diminished. Google Cloud reduced the deployment process from about 16 hours in our legacy platform to 4 hours.  This is partly due to the friendliness of automating the deployment (such as schema check, load test, table creation, build.) process with Terraform, Cloud Functions, Pub/Sub, Dataflow, and BigQuery on GCP. Input messages processed with Dataflow allow us to abstract and plan the schema changes according to the needs of the functional team. For example, schema changes raise an alarm, and then we can modify the raw layer table schema. By doing this, we ensure that backend modifications that we don’t control do not affect upper layers.

A key reason why we picked Google Cloud was because of its advanced cost and workload management coupled with its transparent log analytics. This information gives us a complete view into any query performance issues to make improvements on the fly. Further, we achieved a significant amount of cost savings by consolidating multiple tools to BigQuery.With BigQuery, we’ve been able to reduce our total cost per query by 5x.

This was due to a number of reasons:

• Automating pipeline deployment made it much simpler to maintain the data processing processes. 
• Analysts are conscious about what queries they’re running, resulting in running better, more optimized queries. 
• Analysts use a Data Studio dashboard to see their queries and all the associated costs. As a result, there’s a lot more transparency for each persona.

 With these changes, we can easily manage and assign costs associated with each workload with their own cost centers using specific Google Cloud projects.

Change management is always challenging. However, BigQuery is intuitive and doesn’t have a steep learning curve from Hue/Hive on SQL basics. BigQuery also allowed the team to expand its capabilities and enabled them to properly work with nested structures, avoiding unnecessary joins and improving query efficiency. Additionally, we now use Data Catalog as our unique point of truth for metadata management. This allows our team to break the data access barriers and enable federation of data across the organization. By using Airflow to orchestrate everything, we keep track of every data stream. With this information, each end user can see their regularly used data entities’ status via the dashboard. This also adds transparency to our everyday data processes.

Finally, with Google Cloud’s IAM rules applied across the different products, data sharing and access is close to a noOps experience. We have programmatically implemented access according to roles and level access within the company. This allows certain pre-validated roles to view more sensitive information. These solutions help drive a more automated data governance experience. 

Up next: Google Cloud AI/ML

The new stack based on BigQuery has created significant productivity gains. Freed from the burden of operational management, PedidosYa’s data team can now focus on adding value through data tools and products.  

• Our data engineers are better equipped to integrate constantly changing transactional and operational data.
• The dataOps team can automate the infrastructure and provide autonomy to the end user.
• Our data quality team can focus on bringing added value to data stakeholders. 
• Data scientists and data analytics can spend more time analyzing data and less time asking data gatekeepers for data access.

PedidosYa can now democratize data access with a well-governed architecture. We are still at the beginning of our journey, but we are closer to achieving our vision of building a data-driven organization. Up next: expanding our artificial intelligence and machine learning capabilities.

Tune in to Google Cloud’s Applied ML Summit on June 10th, 2021, or listen on-demand later, to learn how to apply groundbreaking machine learning technology in your projects.

Google Cloud Results

• Improves application performance by 8x to 12x; customers can create increasingly complex optimized driving routes in single-digit seconds
• Improves customer satisfaction via increased reliability and greater application performance
• Focuses on adding value to customers by improving software and algorithms, not infrastructure management
• Saves 5x in infrastructure costs

In 2009, Dan Khasis needed to rent an apartment. His search had him driving around the greater New York City area in unfamiliar areas, scattershot-style, often ending up where he started. The frustrating experience led the serial entrepreneur to launch Route4Me, a smartphone navigation app to help consumers create driving routes optimized for multiple stops.

Soon, business users recognized Route4Me’s value and requested enhancements specifically for them. While route optimization apps for big businesses already existed, they were almost exclusively offline desktop programs that were expensive to purchase, deploy, and get trained on. Recognizing the opportunity, Route4Me developed an affordable route optimization solution across various devices, such as smartphones, smartwatches, and telematics devices. The software was tailored to logistics-intensive businesses such as last-mile delivery services and business units conducting field sales, field service, and field marketing functions.

As Route4Me grew its user base, it became clear that its infrastructure of rented, dedicated servers from various providers wasn’t sustainable. “The hardware costs seemed low, but there were many risks and hidden costs,” says Dan Khasis, Co-founder and CEO at Route4Me. For example, “Multi-zone disaster recovery, high availability, automated failover, and on-demand surging of many nodes was simply impossible,“ he adds.

Because under the hood Route4Me’s routing optimization platform requires complex computations, the company needed a globally scalable infrastructure capable of delivering low latency and high throughput. Route4Me also needed to stay competitive by developing and delivering new services as quickly and efficiently as possible.

For these and other reasons, Route4Me moved 100% into the cloud. “Like many entrepreneurial software companies, we test all the latest technologies we can find before upgrading. Typically we go with the fastest technology, with a strong bias towards open source and open standards,” Khasis says. Based on extensive testing, Route4Me selected Google Cloud Platform (GCP). Along with the scalability, flexibility, reliability, and low-cost structure of GCP, Route4Me had already migrated its entire platform to containerized microservices, which Khasis says “are extremely stable and reliable” on Google Kubernetes Engine. While Route4Me has proprietary routing and route optimization engines, it uses Google Maps for high-precision geocoding and as the frontend.

With GCP, Route4Me has reduced its IT infrastructure costs while delivering faster route optimizations and more reliable service to customers. Because of GCP, the company is also planning to add services that will deliver the fastest possible routing simulations and calculations to customers at a price that Khasis says is “impossible without a mature cloud-based platform like GCP.”

Unexpected savings, pleasant surprises

The migration to GCP and Kubernetes Engine required Route4Me to revamp its Service-Oriented Architecture (SOA) and convert millions of lines of code into containerized microservices running on Kubernetes Engine. With more than 150 microservices and thousands of add-on modules and features offered on the Route4Me platform, the migration took several months. But the transition, which began in May 2017 and concluded toward year’s end, went smoothly. “Thanks to the reliability and open source portability of Google Kubernetes Engine, Route4Me experienced one-tenth of the problems that we’ve had when onboarding to other cloud providers,” says Khasis.

Halfway into the migration, Route4Me engineers discovered an unexpected cost savings. The ability to run preemptible virtual machine (VM) instances with Kubernetes Engine resulted in a 90% savings in infrastructure costs, according to Khasis.

The engineering team was also pleasantly surprised by the improved intra-system latency and performance between the Google network and those of third-party systems and other data centers that Route4Me connects to. Overall latency dropped from 8x to 12x. “Where it used to take 8 to 14 seconds to plan a complicated route, now it takes as little as 2 seconds,” Khasis says. Route4Me is also running most of its transactional and operational data through Google BigQuery for a variety of business use cases, including complex machine learning tasks such as geospatial analytics, geospatial pattern detection, and synthetic density.

Scaling while delivering great performance

Route4Me algorithms take into account such data as driving distance, driving time, who’s driving, the day of the week, the vehicle being used, weather conditions, and dozens of other attributes. “All those scenarios and data have to be run in near real time,” Khasis explains. The Route4Me system must access multiple internal and external databases, aggregate all the information in parallel, and deliver it using a high-speed infrastructure platform.

“Our core services and algorithms work much faster on a Google architecture, bringing the total time to solve a complex route problem down to single-digit seconds.” “Many of those steps are resource-intensive,” Khasis adds. “With Kubernetes Engine clusters, we can do much more, scaling up and down as needed, and still deliver great performance to customers around the world.”

Because of its scale, Route4Me built its own automation system for marketing, support, and communications with its customers. “Since we moved our proprietary marketing automation system to GCP, we began delivering our omni-channel marketing communications more reliably, and the correct message reached customers faster and at just the right moment,” says Khasis. “That’s translated to happier customers and increased revenue.”

Customer satisfaction has increased, too, because Route4Me’s users experience far fewer slowdowns than before due to the reliability of GCP. The reliability also means the company spends less time worrying about certain clusters or servers going down for extended periods of time. “We have zero sysadmins, which was the Achilles heel of some of my previous startups,” says Khasis. “So we can focus on software development rather than infrastructure management.”

In order to scale as needed and develop new features, Khasis had expected the company would need to hire more SysAdmin, DevOps, and SecOps staff. “But once we migrated to the modern GCP environment, we didn’t have to make those hires. We saved a lot of money by not having to hire, train, and manage more people,” explains Khasis.

Flexible GCP pricing, in which customers only pay for what they use, has saved Route4Me money on its IT infrastructure. “Preemptible server pricing on GCP is so aggressive,” Khasis says. “If servers are automatically shut off for a certain time period, we don’t pay for them for that period. And if servers are on for a certain amount of time, we get an automatic 30% discount. We’re saving money on the platform with fixed and dynamic workloads.”

Per-second billing with GCP also helps Route4Me cut costs. “If it only takes 25 seconds to do something, we only pay for those 25 seconds,” Khasis says. For the same 25 seconds, other cloud providers might charge for 10 minutes usage or even an hour.”

Road map for the future

In the coming year, Route4Me plans to offer additional add-ons as part of its self-service marketplace, providing customers with transparent pricing on highly complex route optimizations. The service will be extremely valuable to heavy users. For instance, if an organization has to visit 50,000 locations by a certain time, it might wonder if it needs to add 20 people to make that happen and how much it’s going to cost. “Because we’re on GCP, our customer can run a variety of complicated routing scenarios to see which one is the most efficient in seconds instead of minutes,” says Khasis. “As far as I know, none of our competitors can offer that kind of service, giving us an edge as well as a new revenue stream.”

Going forward, Route4Me will begin migrating a huge portion of its core routing optimization platform to Google Google Cloud Spanner. “We want to take further advantage of Cloud Spanner, which comes closest to the CAP theorem and permits us to operate an infinitely scalable and nearly indestructible platform,” Khasis says.

As one example, Route4Me receives telematics data, such as GPS coordinates, from Internet of Things (IoT) devices in smartphones and vehicles, and performs complex algorithmic analysis running on Cloud Spanner. This provides real-time return on investment (ROI) information, so customers can see how much money they’re saving by using Route4Me routing optimization services.

“In order to help as many logistics-intensive businesses as possible, we intend to migrate our proprietary mapping, routing, and route optimization services to Cloud Spanner to take advantage of its extreme reliability and redundancy, and the multi-availability zones of Google Cloud Platform,” says Khasis.

Route4Me also plans to leverage Google machine learning technology, in part to make its routing solution available for use in autonomous and drone vehicles, as well as decentralized edge computing deployments. In addition, Google security and encryption technology will help the company expand its offerings to the heavily regulated medical industry.

Over 60 Route4Me team members use G Suite for almost everything. ”We’re interested in using everything possible with G Suite. We get inspiration from G Suite, too. A lot of thinking and effort went into improving G Suite, and we use that as inspiration to improve own products.”

There are multiple reasons a growing number of database administrators, enterprise architects, application developers and other technology practitioners are moving to Google Cloud’s various database services.

Some are being driven by missing features in offering from other providers such as AWS. In Gartner’s Magic Quadrant for Operational Database Management Systems, the research and advisory firm points out that, “AWS’s surveyed reference customers scored its overall product capabilities one standard deviation (STD) below the mean. Their responses identified missing features such as multiregion writes and autosharding.”

Others are moving to database services on Google Cloud Platform driven by a few benefits. According to Gartner, “Reference customers repeatedly commented on Google’s ease of use and implementation, reliability and integration (with other services and other systems). Reference customers scored Google a full STD above the mean for satisfaction with GCP’s pricing; it received the second-highest satisfaction score of any vendor in this Magic Quadrant.

If you are looking to leverage the power of Google Cloud database offerings—but were unsure of which database services comes closest to the service you are currently using, here’s a handy map to find your way.