With the rapid rise of digital transactions, real-time fraud detection has become a top priority for banks, fintech platforms, and e-commerce companies. Detecting fraud early can prevent millions in losses and protect user trust.

To support this, data teams need low-latency, scalable pipelines capable of ingesting streaming data, applying detection logic, and updating dashboards in near real-time.

Apache Hudi enables real-time fraud detection by offering:

  • Incremental ingestion
  • Record-level updates (upserts)
  • Time travel queries
  • Streaming support on cloud storage (e.g., S3, HDFS)

In this post, we’ll walk through how to build a fraud detection pipeline using Apache Hudi, Apache Kafka, and Spark Structured Streaming.

Why Apache Hudi for Fraud Detection?

Apache Hudi offers several key advantages for fraud detection pipelines:

  • Upserts & Deletes: Update suspicious transactions as fraud is confirmed
  • Incremental Queries: Process only new records for real-time alerting
  • ACID Transactions: Prevent data duplication or partial writes
  • Time-Travel: Analyze historical data for forensic investigations

Architecture Overview

[Transaction Stream (Kafka)]  
↓  
[Spark Structured Streaming]  
↓  
[Hudi Table on S3 or HDFS]  
↓  
[Analytics + Alerts (Athena, Presto, Dashboards)]

You’ll use:

  • Kafka to stream real-time transactions
  • Spark to apply detection logic and write to Hudi
  • Hudi tables to manage mutable, real-time data
  • Query engines to power dashboards and alerts

Step 1: Ingest Streaming Data

Use Spark Structured Streaming to consume Kafka:

df = spark.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "kafka:9092") \
.option("subscribe", "transactions") \
.load()

json_df = df.selectExpr("CAST(value AS STRING)").select(from_json(col("value"), schema).alias("data")).select("data.*")

Define a schema for transactions (e.g., user_id, amount, device_id, location, timestamp).

Step 2: Apply Fraud Detection Rules

Use Spark logic or ML models:

fraud_df = json_df.withColumn("is_fraud",
when((col("amount") > 10000) & (col("location") != col("home_location")), lit(True)).otherwise(lit(False))
)

You can enhance with:

  • Historical profiling
  • ML-based anomaly detection
  • Rule-based scoring

Step 3: Write to Hudi with Upserts

Configure Hudi to update the same user’s transaction if additional fraud signals are found later:

hudi_options = {
'hoodie.table.name': 'fraud_txn_table',
'hoodie.datasource.write.recordkey.field': 'transaction_id',
'hoodie.datasource.write.partitionpath.field': 'dt',
'hoodie.datasource.write.precombine.field': 'event_time',
'hoodie.datasource.write.operation': 'upsert',
'hoodie.datasource.write.table.type': 'MERGE_ON_READ',
'hoodie.datasource.hive_sync.enable': 'true',
'hoodie.datasource.hive_sync.database': 'default',
'hoodie.datasource.hive_sync.table': 'fraud_txn_table',
'hoodie.datasource.hive_sync.mode': 'glue'
}

fraud_df.writeStream \
.format("hudi") \
.options(**hudi_options) \
.outputMode("append") \
.option("checkpointLocation", "s3://checkpoints/fraud/") \
.start("s3://data-lake/fraud_txn_table/")

Step 4: Query in Real-Time

Use Athena, Presto, or Spark SQL to monitor flagged transactions:

SELECT * FROM fraud_txn_table
WHERE is_fraud = true
AND event_time > current_timestamp - interval '10' minute;

You can connect BI tools or dashboards (e.g., Superset, Grafana) to monitor alerts live.

Step 5: Use Incremental Queries for Downstream Jobs

Power alerting engines or rule-based engines using only changed data:

spark.read.format("hudi") \
.option("hoodie.datasource.query.type", "incremental") \
.option("hoodie.datasource.read.begin.instanttime", "20240401000000") \
.load("s3://data-lake/fraud_txn_table/")

Use this for:

  • Streaming enrichment jobs
  • ML feature engineering pipelines
  • Notifying fraud teams

Best Practices

  • Use Merge-on-Read for fast ingestion and delayed compaction
  • Set proper precombine field (e.g., event_time) for deduplication
  • Enable inline or async compaction to manage read performance
  • Partition by date or region to reduce scan size
  • Use Hive sync for seamless integration with Athena or Presto
  • Secure your table with fine-grained access via AWS Lake Formation or Ranger

Conclusion

Apache Hudi is a powerful platform for building real-time fraud detection systems at scale. With upsert support, incremental queries, and streaming capabilities, Hudi enables fast, accurate, and secure pipelines — making it a perfect fit for modern, ACID-compliant lakehouses.

By combining Hudi with Spark, Kafka, and query tools like Athena or Presto, you can turn raw transactional streams into intelligent, real-time fraud insights.