In the era of instant insights, real-time analytics has become a necessity for modern data-driven applications. Whether it’s detecting fraud, tracking user behavior, or updating dashboards with the latest metrics, businesses need fast access to fresh data.

Apache Hudi enables real-time, incremental data processing on data lakes like Amazon S3 or HDFS while maintaining ACID guarantees, upsert capabilities, and query efficiency — making it ideal for lakehouse-style architectures.

In this post, we’ll explore advanced real-time analytics use cases powered by Hudi, including streaming ingestion, change data capture (CDC), and time-travel insights.

Why Hudi for Real-Time Analytics?

Apache Hudi provides a unique blend of features that enable low-latency, high-throughput data pipelines:

  • Upserts and deletes on S3/HDFS
  • Streaming ingestion with Spark or Flink
  • Incremental and snapshot queries
  • Time-travel and rollback capabilities
  • Native integration with Hive, Presto, Trino, and Athena

These features let you use a single table for:

  • Near real-time updates
  • Historical trend analysis
  • Machine learning model training

Use Case 1: Streaming User Activity into a Lakehouse

Goal: Capture user clickstream data in near real-time and update metrics dashboards.

Pipeline:

  1. Ingest raw events via Kafka or Kinesis
  2. Stream into Hudi using Spark Structured Streaming or Flink
  3. Partition data by event_date, use user_id as record key
  4. Query with Presto or Trino for real-time aggregation
df.writeStream \
.format("hudi") \
.option("hoodie.table.name", "user_activity") \
.option("hoodie.datasource.write.recordkey.field", "event_id") \
.option("hoodie.datasource.write.partitionpath.field", "event_date") \
.option("checkpointLocation", "/tmp/checkpoints/user_activity") \
.outputMode("append") \
.start("s3://datalake/hudi/user_activity")

Use Case 2: Real-Time Fraud Detection

Goal: Detect suspicious financial activity within seconds of ingestion.

Strategy:

  • Stream banking events into a Hudi table
  • Run lightweight, low-latency queries on Merge-on-Read (MOR) format
  • Enable fast updates using upsert operations
  • Power dashboards via Athena/Trino on latest data

Hudi’s support for asynchronous compaction and column pruning keeps reads fast while ingesting thousands of events per second.

Use Case 3: Change Data Capture (CDC) for BI Dashboards

Goal: Reflect latest changes in source databases (MySQL/Postgres) in dashboards.

Pipeline:

  • Use Debezium or AWS DMS to capture DB changes to Kafka
  • Ingest Kafka streams into Hudi with structured streaming
  • Use upsert to overwrite updated records in place
  • Sync with AWS Glue catalog for dashboarding via Athena

CDC + Hudi allows you to maintain a live replica of OLTP databases in your lakehouse with support for slowly changing dimensions (SCD) and surrogate keys.

Use Case 4: Time-Travel and Auditing

Goal: Support historical comparisons and rollback scenarios.

Hudi tracks commit history, allowing queries against specific points in time:

-- Query as of a past commit
SELECT * FROM users_hudi
WHERE _hoodie_commit_time <= '20240411113045';

This is valuable for:

  • Regulatory compliance
  • Debugging production issues
  • Generating training data snapshots for ML

Use Case 5: Incremental ETL Workflows

Goal: Optimize ETL pipelines by only processing newly changed data.

Instead of full table scans, Hudi supports incremental queries:

spark.read.format("hudi") \
.option("hoodie.datasource.query.type", "incremental") \
.option("hoodie.datasource.read.begin.instanttime", "20240410100000") \
.load("s3://datalake/hudi/sales")

This enables:

  • Daily/hourly batch jobs with minimal overhead
  • Downstream deduplication logic
  • Efficient data merges for reporting layers

Best Practices for Real-Time Workloads

  • Choose MOR for frequent updates, COW for read-optimized dashboards
  • Use streaming compaction (async) to reduce latency in MOR
  • Enable metadata table for faster file listings
  • Optimize partitioning to match access patterns (e.g., event_date, region)
  • Monitor Hudi commit timeline and retry failed writes
  • Tune write batch size and memory configs for streaming jobs

Conclusion

Apache Hudi enables real-time analytics at scale by combining the flexibility of data lakes with transactional features of databases. From CDC pipelines and fraud detection to interactive dashboards and time-travel queries, Hudi can serve as the backbone for your modern analytics platform.

By leveraging Hudi’s powerful ingestion, storage, and query capabilities, you can build low-latency, scalable, and resilient pipelines that deliver business insights as data lands.