Mexican SaaS ISV and Wi-Fi innovator boosts query performance by 97%, cuts costs by 50%, and offers insightful predictive analytics with MySQL HeatWave and HeatWave AutoML.
Introduction & Background
Aiwifi is a young Mexican company that has developed Wi-Fi solutions that connect shoppers to shopping websites through customized captive portals, allowing businesses to offer a seamless and personalized experience to their customers.
Its value proposition to businesses in industry sectors such as retail, food & beverage, or sports is the ability to gather valuable customer data by tracking user-profiles and activity. They can then leverage this information to create targeted marketing campaigns and improve the customer experience through constant polls that measure in detail the perception and experience of each customer.
Business Challenges & Goals
Upon starting up in 2019, Aiwifi chose Amazon Web Services (AWS) as its platform and Amazon Relational Database Service (RDS/MySQL) as the backend database. However, as the business rapidly grew and generated heavy data loads, the lack of performance became a bottleneck for sustained growth, and database costs became a heavy challenge for the start-up company.
Aiwifi began searching for a managed database system that could address performance issues and provide immediate results for powerful analysis and predictions.
With the introduction of MySQL HeatWave for AWS, Aiwifi found a solution to the growth limitations caused by poor performance and expensive infrastructure. In 2023, it migrated from Amazon RDS to MySQL HeatWave running natively inside AWS.
Business Results & Metrics
By migrating from Amazon RDS to MySQL HeatWave, the Wi-Fi innovator gained an immediate performance boost with queries running 13X faster. After database migration, loading time on captive portals dropped by 50%. Increased response time coupled with high availability and scalability have allowed Aiwifi to quickly onboard new customers without constantly adding expensive resources.
At the same time, costs were reduced by 50%. With MySQL HeatWave doing the heavy lifting on most queries, Aiwifi was able to use a smaller MySQL instance. Migration also put an end to the high data egress fees charged by AWS RDS.
The company experienced a significant improvement in dashboard availability, providing customers with insights into content consumption, campaign impact, user preferences, and personalized content for end users.
MySQL HeatWave, a database system for high-performance, secure transaction processing, real-time analytics, and machine learning, eliminated the constant need for query optimization, allowing Aiwifi’s developers to focus on building machine learning models.
Today, with hundreds of thousands of users connecting through captive portals integrated with Aiwifi solutions, MySQL HeatWave efficiently handles complex queries on over 40 million records in real time. The system processes vast amounts of data, which is then displayed on dashboards showcasing user profiles, browsing activity, and preferences for food, clothing, and other items. Aiwifi’s customers utilize this data to create campaigns, surveys, and promotions, as well as offer discount coupons and foster customer loyalty.
The new platform has helped transform commercial locations—stores, restaurants, malls, sports arenas, and airports—into intelligent spaces through data capture, analytics, customer engagement, and loyalty.
Importantly, the all-in-one nature of MySQL HeatWave brought Aiwifi in-database machine learning through HeatWave AutoML, which automates the machine learning process, with no need for the data or the model to leave the database.
Aiwifi sees its future in offering superlative analytics driven by machine learning and, in cooperation with MySQL Support and ML teams, has built several use cases with HeatWave AutoML.
For instance, using HeatWave AutoML, the captive portal developer can detect among 100,000 Wi-Fi connections those who are shopping customers versus in-store employees, security, or cleaning staff. These are valid metrics but can be filtered out according to preference. Offering its customers such innovative analytics allows them to segment their user base, create more personalized marketing content, and deliver a richer customer experience more accurately. Aiwifi provides its clients with quantified metrics which allow them to calculate the return on investment (ROI) generated through the platform.
The consumer interest and buying pattern data collected in shops, restaurants, malls, airports, or sports arenas in real time feeds another HeatWave AutoML use case—the modeling of sample groups of people that yield predictions of what could interest similar groups of people tomorrow. With such predictions, Aiwifi intends to offer valuable recommendation engines to its customers.
“HeatWave AutoML is one instance where Aiwifi has eliminated the costs of supporting multiple third-party tools for transaction processing, data warehousing, analytics, machine learning, and other resources that are not integrated into other platforms. Aiwifi estimates that MySQL HeatWave replaced up to 5 external systems,” commented Eric Aguilar.
Why MySQL HeatWave?
Aiwifi, hosting its solutions on AWS from the beginning, decided to stick with that platform but transitioned from AWS RDS/MySQL to MySQL HeatWave.
The close collaboration between Aiwifi and Oracle’s Machine Learning team has enabled the company to deploy ML solutions in record time.
“We know AWS and it meets our needs, but moving to MySQL HeatWave has given us an astonishing boost in performance and therefore a pathway to accelerate our expansion. Making HeatWave available on multiple cloud platforms is a very smart move by Oracle,” Eric Aguilar said.
Implementation
The migration was smooth, requiring only five days. There was no need to change the application code. All components of the MySQL HeatWave service on AWS—the service console, the control plane, and the data plane—are built and optimized for AWS.
“The move to MySQL HeatWave on AWS was fully transparent. Now we have the performance boost that we needed going forward, plus the machine learning capabilities,” said Eric Aguilar.