Square, SumUp, Shopify: Real-Time Point-of-Sale (POS) in the Age of Data Streaming

Point-of-Sale (POS) systems are no longer just cash registers. They are becoming real-time, connected platforms that handle payments, manage inventory, personalize customer experiences, and feed business intelligence. Small and medium-sized merchants can now access capabilities that were once reserved for enterprise retailers. Mobile payment platforms like Square, SumUp, and Shopify make it easy to sell anywhere and integrate sales channels seamlessly.

At the same time, data streaming technologies such as Apache Kafka and Apache Flink are transforming retail operations. They enable instant insights and automated actions across every store, website, and supply chain partner.

This post explores the current state of mobile payment solutions, the role of data streaming in retail, how Kafka and Flink power POS systems, the SumUp success story, and the future impact of Agentic AI on the checkout experience.

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 download my free book about data streaming use cases, including various use cases from the retail industry.

Mobile Payment and Business Solutions for Small and Medium-Sized Merchant

The payment landscape for small and medium-sized merchants has undergone a rapid transformation. For years, accepting card payments meant expensive contracts, bulky hardware, and complex integration. Today, companies like Square, SumUp, and Shopify have made the mobile payment process simple, mobile, and affordable.

Block (Square) offers a unified platform that combines payment processing, Point-of-Sale (POS) systems, inventory management, staff scheduling, and analytics. It is especially popular with small retailers and service providers who value flexibility and ease of use.

SumUp started with mobile card readers but has expanded into full POS systems, online stores, invoicing tools, and business accounts. Their solutions target micro-merchants and small businesses, enabling them to operate in markets that previously lacked access to digital payment tools.

Shopify integrates its POS offering directly into its e-commerce platform. This allows merchants to sell in physical stores and online with a single inventory system, unified analytics, and centralized customer data.

These companies have blurred the lines between payment providers, commerce platforms, and business management systems. The result is a market where even the smallest shop can deliver a payment experience once reserved for large retailers.

Data Streaming in the Retail Industry

Retail generates more event data every year. Every scan at a POS, every online click, every shipment update, and every loyalty point redemption is a data event. In traditional systems, these events are collected in batches and processed overnight or weekly. The problem is clear: by the time insights are available, the opportunity to act has often passed.

Data streaming solves this by making all events available in real time. Retailers can instantly detect low stock in a store, trigger replenishment, or offer dynamic discounts based on current shopping patterns. Fraud detection systems can block suspicious transactions before they complete. Customer service teams can see the latest order updates without asking the warehouse.

In previous retail industry examples, data streaming has powered:

• Omnichannel inventory visibility for accurate stock counts across stores and online channels.
• Dynamic pricing engines that adjust prices based on demand and competitor activity.
• Personalized promotions triggered by live purchase behavior.
• Real-time supply chain monitoring to handle disruptions immediately.

Emerging Trend: Unified Commerce

The next stage beyond omnichannel is Unified Commerce. Here, all sales channels – physical stores, online shops, mobile apps, marketplaces, and social commerce – operate on a single, real-time data foundation. Instead of integrating separate systems after the fact, every transaction, inventory update, and customer interaction flows through one unified platform.

Data streaming technologies like Apache Kafka make Unified Commerce possible by ensuring all touchpoints share the same up-to-date information instantly. This enables consistent pricing, seamless cross-channel returns, accurate loyalty balances, and personalized experiences no matter where the customer shops. Unified Commerce turns fragmented retail technology into a single, connected nervous system.

Data Streaming with Apache Kafka and Flink for POS in Retail

In an event-driven retail architecture, Apache Kafka acts as the backbone. It ingests payment transactions, inventory updates, and customer interactions from multiple channels. Kafka ensures these events are stored durably, replayable for compliance, and available to any downstream system in milliseconds.

Apache Flink adds continuous stream processing capabilities. For POS use cases, this means:

• Running fraud detection models in real time, with alerts sent instantly to the cashier or payment gateway.
• Aggregating sales data on the fly to power live dashboards for store managers.
• Updating loyalty points immediately after a purchase to improve customer satisfaction.
• Ensuring that both physical stores and e-commerce channels reflect the same stock levels at all times.

Together, Kafka and Flink create a foundation for operational excellence. They enable a shift from manual, reactive processes to automated, proactive actions.

Using data streaming at the edge for POS systems enables ultra-low latency processing and local resilience, but it can be harder to scale and manage across many locations. Running data streaming in the cloud offers central scalability and simplified governance, though it depends on reliable connectivity and may introduce higher latency.

SumUp: Real-Time POS at Global Scale with Data Streaming in the Cloud

SumUp processes millions of transactions per day across over 30 countries. To handle this scale and maintain high availability, they adopted an event-driven architecture powered by Apache Kafka leveraging fully managed Confluent Cloud.

In the Confluent customer story, SumUp explains how Kafka has allowed them to:

• Process every payment event in real time.
• Maintain a unified data platform across regions, ensuring compliance with local payment regulations.
• Scale easily to handle seasonal transaction spikes without service interruptions.
• Speed up developer delivery cycles by providing event data as a service across teams.

Implementing Critical Use Cases Across the Business

More than 20 teams at SumUp now rely on Confluent Cloud to deliver mission-critical capabilities.

• Global Bank Tribe: Operates SumUp’s banking and merchant payment services. Real-time data streaming keeps transaction records updated instantly in merchant accounts. Reusable data products improve resilience for high-volume processes such as 24/7 monitoring, fraud detection, and personalized recommendations.
• CRM Team: Delivers customer and product information to operational teams in real time. Moving away from batch processing creates a smoother customer experience and enables data sharing across the organization.
• Risk Data and Machine Learning Platform: Feeds standardized, near-real-time data into machine learning models. These models make decisions on the freshest data available, improving outcomes for both teams and merchants.

By embedding Confluent Cloud across multiple domains, SumUp has turned event data into a shared asset that drives operational efficiency, customer satisfaction, and innovation at scale. For merchants, this means faster transaction confirmations, improved reliability, and new digital services without downtime.

The Future of POS and Impact of Agentic AI

The POS of tomorrow will be more than a payment device. It will be a connected intelligence hub.

Agentic AI with autonomous AI systems capable of proactive decision-making will play a central role. Future capabilities could include:

• AI-driven recommendations for upsells, customized to each shopper’s behavior and context.
• Predictive inventory replenishment that automatically places supplier orders when stock is low.
• Automated fraud prevention that adapts in real time to emerging threats.
• Dynamic loyalty program offers tailored at the exact moment of purchase.

When Agentic AI is powered by real-time event data from Kafka and Flink, decisions will be both faster and more accurate. This will shift POS systems from being passive endpoints to active participants in business growth.

For small and medium-sized merchants, this evolution will unlock capabilities previously available only to enterprise retailers. The result will be a competitive, data-driven retail landscape where agility and intelligence are built into every transaction.

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 download my free book about data streaming use cases, including various use cases from the retail industry.