Traditional big data architectures often force a trade-off between real-time analytics and historical batch processing. While HDFS provides scalable storage for massive datasets, it is optimized for batch workloads. On the other hand, Apache Kudu is designed for fast analytics on changing data.

By combining HDFS and Apache Kudu, you can design a hybrid architecture that supports both real-time updates and long-term storage, enabling low-latency analytics on fresh and historical data.

In this post, we’ll explore how to integrate HDFS with Apache Kudu, and how this combination supports a modern, analytics-ready data platform.

What is Apache Kudu?

Apache Kudu is a columnar storage engine designed for real-time analytics. It complements HDFS by offering:

  • Low-latency inserts, updates, deletes
  • Fast columnar reads
  • Strong integration with Apache Impala and Apache Spark
  • Schema evolution support

It stores data on disk in columnar format while allowing row-level operations, which is ideal for time-series, IoT, fraud detection, and more.

HDFS vs. Kudu: When to Use What

Feature HDFS Kudu
Storage Format File-based (ORC/Parquet) Table-based column store
Update Support Append-only Inserts, Updates, Deletes
Latency High (batch-oriented) Low (real-time)
Query Engine Hive, Spark Impala, Spark
Ideal For Historical data, archives Real-time analytics, dashboards

Use HDFS for historical batch data and Kudu for hot, mutable data.

Hybrid Data Architecture: HDFS + Kudu

A typical architecture might look like this:

[ Ingestion Layer (Kafka/NiFi/Flink) ]
|
+--------+        +--------+
|  Kudu  | <----> | HDFS   |
+--------+        +--------+
|                |
[ Impala / Spark SQL ]
  • Write real-time data to Kudu for fast queries
  • Archive or compact older data into HDFS
  • Query both sources using Apache Impala or Apache Spark

Data Ingestion into Kudu and HDFS

Use Apache NiFi, Apache Flink, or Apache Spark Structured Streaming to ingest data:

Example (Flink writing to Kudu):

KuduTableInfo tableInfo = KuduTableInfo.forTable("kudu_master:7051", "events");

DataStream<Row> stream = env.addSource(kafkaSource);
KuduSink<Row> sink = KuduSink.sink(tableInfo, kuduSchema, kuduOptions);

stream.addSink(sink);

In parallel, you can archive cold data into HDFS:

stream
.filter(event -> isOlderThan30Days(event.timestamp))
.addSink(hdfsSink);

This dual write pattern enables hot/warm data separation.

Querying Across Kudu and HDFS with Impala

Impala supports querying both Kudu tables and HDFS-stored tables.

Kudu table:

CREATE TABLE real_time_events (
id STRING,
ts TIMESTAMP,
value DOUBLE
)
PRIMARY KEY(id, ts)
STORED AS KUDU;

HDFS (historical):

CREATE EXTERNAL TABLE historical_events (
id STRING,
ts TIMESTAMP,
value DOUBLE
)
STORED AS PARQUET
LOCATION '/data/historical_events/';

Union both:

SELECT * FROM real_time_events
UNION ALL
SELECT * FROM historical_events;

This enables seamless analytics across both fresh and old data.

Use Cases for HDFS + Kudu

  • IoT & telemetry: Ingest live data into Kudu, archive old records to HDFS
  • Fraud detection: Real-time scoring on Kudu + historical pattern mining on HDFS
  • Financial services: Trade activity in Kudu, compliance logs in HDFS
  • Web analytics: Real-time dashboards from Kudu + trend analysis from HDFS

Best Practices

  • Use time-based partitioning in HDFS and Kudu for scalable queries
  • Avoid large row updates in Kudu — it’s optimized for inserts and point updates
  • Enable auto compaction in Kudu to reclaim space
  • Use Impala or Spark SQL for federated queries
  • Monitor storage growth — archive old Kudu data to HDFS periodically

Conclusion

Combining HDFS and Apache Kudu enables the best of both worlds: real-time analytics on hot data and cost-effective storage of historical records. Whether you’re building a fraud detection system, a live dashboard, or an enterprise data lake, this hybrid approach provides the speed, scalability, and flexibility needed for modern analytics.

By using tools like Impala, Spark, and NiFi, you can build robust pipelines that leverage both systems — unlocking rich insights from streaming and batch data alike.