Mobile gaming is no longer just about fun—it’s about speed, fairness, personalization, and trust. As competition in the digital gaming and fantasy sports space heats up, the ability to respond to user behavior in real time has become a competitive advantage. In this post, I explore how Mobile Premier League (MPL)—one of the world’s largest mobile gaming and eSports platforms—transformed its architecture from slow batch processing to a real-time, AI-powered engine using Data Streaming with Apache Kafka, Apache Flink, and Confluent Cloud. The result? Faster decisions, personalized gameplay, smarter fraud detection, and a better experience for over 90 million users.

Let’s dive into what MPL is, how it works, and what other gaming companies can learn from its real-time transformation.

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What is Mobile Premier League (MPL)?

Mobile Premier League (MPL) is the world’s leading mobile eSports and digital gaming platform. It offers a wide range of skill-based games, including chess, carrom, fantasy sports, puzzles, and competitive card games. The platform hosts millions of monthly competitions and has over 90 million registered users.

Unlike traditional video game platforms or gambling providers, MPL’s focus is not on high-end PC or console gaming. It’s a mobile-first, skill-driven experience designed for real users playing real games with real rewards.

MPL’s Business Model: Skill-Based Gaming, Not Betting

MPL is not a betting vendor. It operates in the skill-based gaming space. Users pay small entry fees to join competitions. They earn points or win cash prizes based on their performance, not chance or odds.

This makes MPL fundamentally different from betting platforms. There are no odds, no sportsbooks, and no random outcomes. Winning depends on a user’s skill level and how well they perform in the game. Whether it’s a five-minute chess match or a fantasy cricket contest, success comes from practice and decision-making, not luck.

Rewards include cash prizes, but also missions, challenges, and loyalty programs. Engagement and fun go beyond monetary gain. MPL is building a long-term gaming ecosystem where users return not just to win, but to play, improve, and connect.

Data Streaming in Mobile Gaming and Fantasy Sports

Modern mobile gaming platforms must handle massive data volumes. They need to deliver smooth gameplay, detect fraud, personalize experiences, and update leaderboards in real time. Apache Kafka and Apache Flink provide the infrastructure to meet these needs.

Streaming data in real time enables:

• Fast reactions to in-game activity
• Real-time updates for user scores and missions
• Personalized rewards based on behavior
• Secure gameplay by detecting fraud as it happens

This is especially important in fantasy sports and competitive tournaments, where timing and fairness are everything.

MPL’s Real-Time Architecture with Confluent and Kafka

MPL was initially built on batch pipelines. Data was collected, processed in large jobs, and acted upon later. This caused delays in decision-making, personalization, and fraud detection. To solve this, MPL partnered with Confluent to build a real-time data streaming platform powered by Kafka and Flink.

This new architecture delivers constant data flows from:

• Player actions
• Game outcomes
• App activity
• Security and fraud detection systems

The event-driven architecture powered by data streaming enables instant decisions and supports real-time machine learning.

“At MPL we are committed to offering the best-in-class security and gameplay experience for our users… Confluent has helped us access real-time data to make informed decisions and stamp out issues before they become problems.”

Jaydeep Punjani, Principal Engineer, MPL

Use Cases: AI and Machine Learning in Real Time

MPL built a powerful Machine Learning platform with Kafka and Flink (presented by Mahesh Jadhav and Lakhan Marda at Current India 2025 in Bangalore) to take advantage of the real-time data. It supports multiple use cases, including:

1. Real-Time Personalization: The platform adjusts game lobbies, missions, and rewards to fit the player’s habits. These changes are made in seconds. This keeps players engaged and reduces churn.
2. Feature Store: The feature store, powered by BigTable and Redis, processes over 150 million features per day. It supports low-latency data updates and serves real-time features to ML models.
3. Real-Time Orchestration: ML models perform over 300 million inferences per day, with p99 turnaround times under 10 seconds. These models guide decisions like matchmaking, mission assignment, and fraud alerts.

Business Impact: More Engagement, Less Fraud, Higher Trust

MPL’s new platform enables:

• Better engagement: Personalized onboarding, targeted missions, and customized offers.
• Higher retention: Players stay longer because the experience adapts to them.
• Increased trust: Fraud is detected and prevented in real time.
• Cost savings: $35K saved monthly by using Confluent’s managed service.

Batch vs. Streaming: The Architectural Shift

MPL relied on batch processing pipelines. A common pattern involved capturing clickstream data, storing it in a data lake, and analyzing it later with tools like Spark.

However, this approach comes with major limitations:

• Latency of several hours or even days
• No ability to react to live user behavior
• Missed chances for real-time personalization or fraud detection

MPL modernized its architecture, powered by Kafka and Flink, to provide a compelling alternative with real-time capabilities:

• Instant processing of in-game or user events
• Real-time machine learning inference for dynamic personalization
• Live orchestration of user journeys, features, and offers

By moving from batch to streaming, MPL gained the ability to make faster, smarter decisions—ultimately delivering better experiences and outcomes.

More Examples for Data Streaming in the Gaming Industry

MPL is not alone. Data streaming is transforming the entire gaming and eSports space.

• Dream11 uses Apache Kafka to manage peak fantasy sports workloads during live cricket events.
• Unity powers one of the largest monetization networks in mobile gaming, using Kafka to process half a million events per second.
• William Hill, a leading betting provider, rebuilt their trading platform on Kafka for low-latency, secure transactions.
• Big Fish Games uses Kafka to deliver in-game purchases and recommendations in real time, driving revenue while users play.

These use cases show the breadth of data streaming with Apache Kafka in mobile gaming, fantasy sports, and digital competitions—not just classic video games. Check out my dedicated blog post to learn more about the state of data streaming with Kafka and Flink in the gaming industry.

How MPL Built a Safer, Smarter, and More Engaging Gaming Platform with Data Streaming

MPL’s journey shows how real-time data streaming creates real results. Moving from batch to real-time unlocked better engagement, stronger personalization, and faster response to user behavior. At the same time, it helped the team reduce fraud and improve platform trust—without adding operational burden.

By using Apache Kafka and Apache Flink (powered by Confluent’s data streaming platform), MPL delivers smarter matchmaking, safer gameplay, and a more dynamic user journey. This isn’t just about technology. It’s about creating a gaming experience that adapts to every player in the moment, not after the fact.

For developers, data engineers, and architects building gaming platforms, MPL offers a clear blueprint. Real-time infrastructure gives you the speed, reliability, and intelligence needed to build products players love—and come back to.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases, including several success stories around gaming, loyalty platforms, and personalized advertising.