Big Data Fundamentals: stream processing example

Real-Time Fraud Detection with Apache Flink and Iceberg: A Production Deep Dive

1. Introduction

The increasing velocity of online transactions necessitates real-time fraud detection. Traditional batch processing, even with daily or hourly updates, is insufficient to prevent significant financial losses. We faced a challenge at scale: detecting fraudulent credit card transactions within seconds of initiation, processing a sustained 100K+ transactions per second, while maintaining a low false positive rate. This required a shift from nightly batch analysis to a continuously updating, low-latency stream processing pipeline. This blog post details our implementation using Apache Flink, Apache Iceberg, and AWS infrastructure, focusing on architectural decisions, performance tuning, and operational considerations. Data volume is approximately 5TB/day, with a schema evolving roughly monthly due to new transaction types and risk indicators. Query latency requirements are under 500ms for real-time dashboards and alerts. Cost-efficiency is paramount, given the continuous nature of the processing.

2. What is Stream Processing in Big Data Systems?

Stream processing, in our context, is the continuous ingestion, transformation, and analysis of data records as they arrive. Unlike batch processing, which operates on finite datasets, stream processing deals with unbounded streams. It’s not merely about speed; it’s about maintaining state and reacting to patterns over time. Our architecture leverages Flink’s stateful stream processing capabilities to track transaction history and identify anomalies.

We utilize Apache Iceberg as our data lake table format. Iceberg provides ACID transactions, schema evolution, and time travel capabilities, crucial for handling evolving fraud patterns and enabling auditability. Data is ingested in Avro format for schema evolution support and efficient serialization. Protocol-level behavior involves Flink’s checkpointing mechanism for fault tolerance, writing state to S3, and Iceberg’s metadata updates to ensure consistency.

3. Real-World Use Cases

Beyond fraud detection, this architecture supports several critical use cases:

• CDC Ingestion: Capturing changes from transactional databases (e.g., PostgreSQL) using Debezium and feeding them into the stream for real-time analytics.
• Streaming ETL: Transforming raw transaction data (e.g., enriching with geolocation data) before storing it in Iceberg.
• Large-Scale Joins: Joining transaction streams with historical customer data (stored in Iceberg) to assess risk profiles.
• ML Feature Pipelines: Generating features from streaming data for real-time fraud scoring models.
• Log Analytics: Analyzing application logs in real-time to detect security threats or performance bottlenecks.

4. System Design & Architecture

graph LR
    A[Debezium (CDC)] --> B(Kafka);
    C[Transaction API] --> B;
    B --> D{Flink Application};
    D --> E[Iceberg Table (S3)];
    E --> F[Presto/Trino];
    F --> G[Real-time Dashboard];
    D --> H[Alerting System];
    subgraph AWS
        B
        E
        F
        H
    end

This diagram illustrates the end-to-end pipeline. Debezium captures database changes and the Transaction API directly publishes transaction events to Kafka. A Flink application consumes these events, performs transformations (feature engineering, risk scoring), and writes the results to an Iceberg table stored in S3. Presto/Trino queries the Iceberg table for real-time dashboards, and an alerting system triggers notifications based on high-risk transactions.

We deploy Flink on AWS EMR, leveraging YARN for resource management. The Iceberg table is partitioned by transaction date and customer ID to optimize query performance. Kafka is configured with multiple partitions to maximize parallelism.

5. Performance Tuning & Resource Management

Performance tuning is critical for maintaining low latency and high throughput. Key strategies include:

• Parallelism: flink.parallelism is set to 200, matching the number of Kafka partitions.
• Memory Management: taskmanager.memory.process.size is set to 16GB, with taskmanager.memory.fraction at 0.7 to allocate sufficient memory for state management.
• I/O Optimization: fs.s3a.connection.maximum is set to 1000 to handle concurrent S3 requests. We also utilize S3 multipart uploads for large files.
• File Size Compaction: Iceberg’s compaction process is scheduled to run hourly, optimizing file sizes for query performance.
• Shuffle Reduction: We utilize Flink’s rebalance and rescale operators strategically to minimize data shuffling. spark.sql.shuffle.partitions (though a Spark setting, the principle applies to Flink’s shuffle behavior) is conceptually relevant – minimizing the number of partitions reduces shuffle overhead.

These configurations result in a sustained throughput of 120K transactions/second with an average latency of 300ms.

6. Failure Modes & Debugging

Common failure modes include:

• Data Skew: Certain customer IDs generate disproportionately more transactions, leading to uneven workload distribution. We mitigate this using Flink’s keyBy operator with a salt strategy.
• Out-of-Memory Errors: Large state sizes can exhaust memory resources. We address this by using Flink’s RocksDB state backend and increasing memory allocation.
• Job Retries: Transient network issues or S3 outages can cause job failures. Flink’s checkpointing mechanism ensures automatic recovery.
• DAG Crashes: Logic errors in the Flink application can lead to DAG crashes.

Debugging involves analyzing Flink’s web UI, examining logs (using CloudWatch Logs), and monitoring metrics (using Datadog). Specifically, we monitor state size, latency percentiles, and the number of failed checkpoints.

7. Data Governance & Schema Management

We utilize the AWS Glue Data Catalog as our metadata store for Iceberg tables. Schema evolution is managed using Iceberg’s schema evolution capabilities. New schema versions are registered in the Glue Data Catalog, and Flink applications are configured to handle schema compatibility. We enforce schema validation using Avro schemas to ensure data quality. Backward compatibility is maintained by adding new fields with default values.

8. Security and Access Control

Data is encrypted at rest in S3 using KMS keys. Access to S3 buckets and the Glue Data Catalog is controlled using IAM policies. We leverage AWS Lake Formation for fine-grained access control to Iceberg tables, including row-level security based on customer ID. Audit logging is enabled for all data access events.

9. Testing & CI/CD Integration

We utilize Great Expectations for data quality testing, validating schema consistency and data completeness. DBT tests are used to validate transformations performed by the Flink application. Our CI/CD pipeline includes unit tests for Flink operators and integration tests that validate the end-to-end pipeline. We deploy Flink applications using a blue/green deployment strategy to minimize downtime.

10. Common Pitfalls & Operational Misconceptions

• Ignoring State Size: Underestimating state size leads to OOM errors. Metric Symptom: Increasing task manager memory usage, frequent garbage collection. Mitigation: Use RocksDB, tune memory settings, optimize stateful operations.
• Insufficient Parallelism: Low parallelism limits throughput. Metric Symptom: Low CPU utilization, high latency. Mitigation: Increase flink.parallelism, add Kafka partitions.
• Incorrect Checkpointing Configuration: Infrequent checkpoints increase recovery time. Metric Symptom: Long recovery times after failures. Mitigation: Reduce checkpoint interval, increase checkpoint timeout.
• Ignoring Data Skew: Uneven workload distribution impacts performance. Metric Symptom: Some tasks are significantly slower than others. Mitigation: Use salting, rebalance operator.
• Lack of Schema Enforcement: Schema drift leads to data quality issues. Metric Symptom: Data validation failures, incorrect query results. Mitigation: Enforce schema validation using Avro schemas, implement schema evolution strategies.

11. Enterprise Patterns & Best Practices

• Data Lakehouse vs. Warehouse: We opted for a data lakehouse architecture (Iceberg on S3) for flexibility and cost-efficiency.
• Batch vs. Micro-batch vs. Streaming: True streaming (Flink) is essential for low-latency fraud detection.
• File Format Decisions: Avro provides schema evolution support, crucial for evolving fraud patterns.
• Storage Tiering: We utilize S3 Intelligent-Tiering to optimize storage costs.
• Workflow Orchestration: Apache Airflow orchestrates the entire pipeline, including data ingestion, Flink application deployment, and Iceberg compaction.

12. Conclusion

Real-time fraud detection requires a robust and scalable stream processing architecture. Our implementation using Apache Flink and Iceberg has proven effective in detecting fraudulent transactions with low latency and high accuracy. Next steps include benchmarking new Flink configurations, introducing schema enforcement at the ingestion layer, and migrating to a more granular partitioning scheme for improved query performance. Continuous monitoring and optimization are crucial for maintaining the reliability and efficiency of this critical system.