Entity Resolution at Scale: Merge Duplicates as Data Moves
Enterprise data rarely arrives clean. The same customer, supplier or product can exist across multiple systems under different names, IDs or formats. For reporting, this creates inconsistency. For AI and automation, it creates unreliable context.
Entity resolution has traditionally been handled through batch processing. But when data is moving continuously through CDC and event streams, resolving duplicates overnight is no longer enough.
What Is Entity Resolution?
Entity Resolution identifies records that refer to the same real-world entity and links or merges them into a trusted representation.
A CRM may contain “John Smith”, an ERP may store “J Smith”, and a support platform may record “John A. Smith”. At small scale, matching these records is simple. Across millions of continuously changing records, it becomes a significant engineering challenge.
Modern entity resolution needs to combine deterministic rules, fuzzy matching and more advanced logic while maintaining low latency and high throughput.
Why Real-Time Entity Resolution Is Difficult
The challenge is not simply finding a duplicate. Streaming records need to be matched against large volumes of historical data without creating processing bottlenecks.
The system also needs to maintain state. A new event may need to be compared with information that arrived months or years earlier, while customer details, supplier records and product hierarchies continue to change.
Many organisations solve this by combining separate CDC tools, streaming engines, matching services, lookup databases, batch jobs and data-quality platforms. The result is often an architecture that works, but becomes increasingly complex and expensive to maintain.
Resolve Entities Inside the Data Flow
IOblend takes a different approach.
Instead of treating entity resolution as a separate downstream process, IOblend allows matching, deduplication, enrichment and validation to happen directly within the data pipeline.
As data moves between operational systems, Microsoft Fabric, Databricks, Snowflake, applications or AI platforms, IOblend can determine whether a record represents a new entity, an update, a duplicate or an exception requiring further validation.
This turns entity resolution from an occasional clean-up exercise into a continuous data operation.
How IOblend Helps
Powered by Apache Spark, IOblend can process historical data, CDC events and streaming workloads through the same execution layer. Data teams can apply matching and transformation logic using familiar SQL or Python without building a separate architecture for every processing pattern.
Deterministic rules such as customer IDs, email addresses, telephone numbers, product codes and composite business keys can be applied directly in the pipeline. More complex records can also be enriched, normalised or validated using AI-assisted processing where ambiguity exists.
Duplicate or invalid records can be merged, corrected or quarantined before they reach reporting, applications or AI systems.
IOblend can also support Slowly Changing Dimension patterns, including SCD Type I and Type II, helping organisations maintain trusted current records while preserving historical changes where required.
Keep Your Existing Data Platform
Entity resolution should not require another major platform migration.
IOblend works across existing environments including Microsoft Fabric, Databricks, Snowflake, databases, ERP platforms, CRM systems, APIs and event sources.
The objective is not to create another storage layer. It is to improve the quality of data while it is already moving between systems.
Entity Resolution Is Becoming an AI Requirement
AI systems are only as reliable as the entities behind their data.
An AI assistant cannot build an accurate customer view if one person exists as multiple disconnected records. An AI agent should not make supplier decisions from duplicate vendor profiles. Analytics cannot calculate customer value correctly if activity is divided across several identities.
Entity resolution is therefore becoming an important part of AI readiness.
The goal is not simply fewer duplicates. It is a continuously trusted view of the business.
One customer. One supplier. One product. One entity downstream systems can rely on.
With IOblend, entity resolution becomes part of the data flow itself, combining streaming integration, CDC, data quality, governance and AI-assisted processing within one execution layer.
Trusted data while it moves. Ready for analytics, automation and AI.
Better airport operations with real-time analytics
Good and bad Welcome to the next issue of our real-time analytics blog. Now that the summer holiday season is upon us, many of us will be using air travel to get to their destinations of choice. This means, we will be going through the airports. As passengers, we have love-hate relationships with airports. Some
The making of a commercial flight
What makes a flight Welcome to the next leg of our airline data blog journey. In this article, we will be looking at what happens behind the scenes to make a single commercial flight, well, take flight. We will again consider how processes and data come together in (somewhat of a) harmony to bring your
Enhance your airline’s analytics with a data mesh
Building a flying program In the last blog, I have covered how airlines plan their route networks using various strategies, data sources and analytical tools. Today, we will be covering how the network plan comes to life. Once the plans are developed, they are handed over to “production”. Putting a network plan into production is
Planning an airline’s route network with deep data insights
What makes an airline Commercial airlines are complex beasts. They comprise of multiple intertwined (and siloed!) functions that make the business work. As passengers, we see a “tip of the iceberg” when we fly. A lot of work goes into making that flight happen, which starts well in advance. Let’s distil the complexity into something
Flying smarter with real-time analytics
Dynamic decisioning We continue exploring the topics of operational analytics (OA) in the aviation industry. Data plays a crucial role in flight performance analytics, operational decisioning and risk management. Real-time data enhances them. The aviation industry uses real-time data for a multitude of operational analytics cases: monitor operational systems, measure wear and tear of equipment,
How Operational Analytics power Ground Handling
The Ground Handling journey – today and tomorrow In today’s blog we are discussing how Operational Analytics (OA) enables the aviation Ground Handling industry to deliver their services to airlines. Aviation is one of the most complex industries out there, so it offers a wealth of examples (plus it’s also close to our hearts). OA
