Project snapshot
For EveryMatrix, a provider of iGaming solutions and our long-term partner, we transformed a legacy operator platform into a high-performance administration portal for processing various types of data and generating validated insights. Taking advantage of advanced technologies such as machine learning, big data, and cloud computing analytics, we turned the outdated system into a scalable, robust, and user-friendly environment for iGaming operators.
The delivered solution can process massive amounts of data in various formats received from multiple sources and translate it into visual reports and dashboards, providing actionable insights for customers. The new platform boasts next-level data processing speed and efficiency, accelerating responses and enabling customers to make decisions faster and with higher precision.
EveryMatrix is a global provider of high-tech B2B SaaS iGaming solutions, providing services to gaming operators in multiple countries. With a suite of online gaming products, including provider and agent platforms, payment processing solutions, content aggregators, and marketing software, EveryMatrix brings cutting-edge technology into the centuries-old universe of gaming and betting.
Business challenge
EveryMatrix needed a redesign of their legacy analytics platform for gaming operators to handle increased loads and support an extended range of services. The company was looking for a way to empower operators with insights based on data received from multiple sources and advanced visualizations enabling data-driven decision-making.
An integral part of their iGaming solutions suite was a business intelligence reporting system built on the .NET and MS SQL technology stack. Their iGaming business intelligence software helped gaming operators monitor player activity, generate invoices, calculate revenue, and adhere to gambling legislation.
However, steady annual growth in the number of players, games, and sports events put a heavy burden on this legacy system. Because of serious scalability bottlenecks, it could no longer smoothly process the increasing amounts of data coming from EveryMatrix solutions, customers’ CMSs, and payment operators. Besides, the system was difficult and costly to maintain. EveryMatrix needed a new architectural and technological approach to efficiently manage their 100 TB data warehouse and ensure sufficient scalability for years to come.
As a trusted provider of advanced technology services, big data solutions, and machine learning solutions, Intellias set out to establish powerful mechanisms to collect, aggregate, and process data from all EveryMatrix products and ensure data-driven insights for all customers and partners.
Solution
As a long-time partner of EveryMatrix, we helped them build a sportsbook platform, set up a payment processing system, achieve PCI DSS compliance, and more. Over six years of collaboration, we have increased the size of our EveryMatrix development team to 110 specialists, with several subteams working on different aspects of our client’s business. One of the projects Intellias has been working on is an advanced data services platform and business intelligence (BI) software offering rich data streaming, processing, and reporting capabilities.
To provide real-time, historical, and predictive insights into business operations, our team built a new reporting solution using the latest business intelligence technologies. This solution incorporates:
- an in-memory message broker for receiving data feeds from various sources
- a dedicated data warehouse for storing pre-aggregated multidimensional data
- a customer-facing reporting interface for building, presenting, and visualizing business data
Data collection and validation
The intake message bus, which runs in the cloud, collects data feeds from different sources: EveryMatrix products, the gaming platform, user data, and payment aggregators. This data is then structured, validated, and made available for further processing. Built on the Apache Kafka distributed streaming platform, the message broker is fast, horizontally scalable, and fault-tolerant thanks to data replication.
Data integration and processing
A set of free open-source solutions handle the integration of source data, merging it into a cluster of PostgreSQL servers. We use Apache Airflow for batch processing, Apache NiFi for streaming processing, and Confluent KSQL for Kafka streams. By choosing these services, we removed cost constraints and made the system more scalable.
Source data from the message broker is extracted, transformed, and loaded into the centralized warehouse and dimension tables.
Data rendering
Our own custom visualization solution compiles and presents data to customers in the form of reports, dashboards, graphics, and widgets. Widgets can be integrated directly into EveryMatrix products — Casino Engine, OddMatrix, MoneyMatrix, and PartnerMatrix — to display context-aware data to players and customers.
To support the constant growth of the EveryMatrix user base, which now numbers as many as 5 million, we have come up with several innovative solutions that help our client efficiently deal with the influx of data from different sources and provide customers with actionable business insights.
Cloud computing farm
BigQuery allows us to tap into Google’s powerful computing capabilities to ensure fast processing and analysis of massive data sets. Large volumes of data are split into small chunks stored in Google Cloud and can be easily processed within seconds by renting Google’s resources. By requesting the thousands of Google nodes necessary to compute small portions of data in a few seconds, we achieve fast query dispatching and data collection from multiple machines, resulting in fast calculations.
Streaming platform
Events and data from each product are brought into a single streaming platform with a data collection speed of 1 million messages per second. The requests are used for fraud detection or verification whether the user meets risk assessment requirements. We use Apache Kafka, Kafka Connectors, and Apache NiFi for the events hub and ingest processing.
The business intelligence platform allows data experts to analyze incoming messages on the fly with a delay of just several milliseconds and respond appropriately. If an event is classified under a rule configured in the system, it’s forwarded to the customer at their request. Data specialists can also act on a new event by sending an email or text message to verify user data.
Load balancer
Our team developed an effective load management approach to increase capacity and handle high loads during traffic spikes. Our horizontally scalable solution allocates users to different servers, which can be added to the ring as needed. We use a consistent hash ring to assign users to nodes and a load balancer to distribute user traffic across available machines. In case of a load surge, we can increase the number of servers to withstand it.
Prediction and recommendation engine
We built a machine learning recommendation system with a business intelligence platform that includes a mechanism for data-based comparison of similar users (based on gender, age, place of birth, behavior, etc.) and their buying preferences for products and games.
An interactive recommendation model suggests new games to users based on statistics. The model ensures maximum precision in predicting the value of recommendations for gamers. Implementing this model resulted in a rise in user buying activity and a tangible increase in sales.
Additionally, we provided a fraud detection system, a model for predicting when users will leave the website, and an A/B testing system for calculating metrics.
Business outcome
Intellias has made a meaningful contribution to the transformation of EveryMatrix, guiding the company in a new data-driven direction through our engineering and consulting services. Our strong focus on data management and analytics has helped our client understand their customers’ needs and provide better services as well as enable customers to make informed decisions and build sophisticated systems based on data.
Following our deep analytics approach, which included thorough analysis of KPIs for each operator based on end user data, EveryMatrix was able to make efficient decisions on further improvements. This has resulted in the implementation of machine learning-based solutions that give EveryMatrix important insights into end users’ behavior. From identifying and recommending the most relevant games to eliminating illegitimate user activity, the systems we’ve delivered cover multiple aspects of the iGaming business, including casino business intelligence.
1million
messages per second
90%
prediction precision
100TB
data processing capacity