Apache Hudi Explained: Incremental Data Processing for Modern Data Lakes

Introduction

Organizations generate massive volumes of data every day from applications, websites, IoT devices, business systems, and cloud services. While data lakes have become a popular solution for storing large datasets, managing continuously changing data inside a data lake presents unique challenges.

Traditional data lakes are excellent at storing data, but they often struggle with handling updates, deletes, and incremental processing efficiently. As a result, organizations frequently build complex ETL pipelines to keep analytical systems synchronized.

This is where Apache Hudi comes in.

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an open-source data lake framework that brings database-like capabilities to data lakes. It enables organizations to efficiently manage changing datasets while supporting incremental processing, streaming ingestion, and near real-time analytics.

In this article, we'll explore what Apache Hudi is, how it works, and why it has become an important technology for modern data lake architectures.

What Is Apache Hudi?

Apache Hudi is an open-source transactional data lake platform that allows organizations to manage large analytical datasets efficiently.

Unlike traditional data lakes that primarily support append-only workloads, Hudi enables:

Inserts
Updates
Deletes
Incremental queries
Streaming ingestion
Change data capture (CDC)

By adding transaction management and record-level updates, Hudi helps bridge the gap between data lakes and traditional databases.

It works with popular storage systems such as:

Amazon S3
Azure Data Lake Storage
Google Cloud Storage
HDFS

And integrates with analytics engines including:

Apache Spark
Apache Flink
Trino
Presto
Hive

Why Traditional Data Lakes Face Challenges

Most data lakes store files in formats such as:

Parquet
ORC
Avro

While these formats are highly efficient for analytics, updating existing records is difficult.

Consider a customer dataset:

CustomerID | Name | Status
--------------------------------
101        | Alice | Active
102        | Bob   | Active

Suppose Bob's status changes.

In a traditional data lake, updating a single record often requires:

Reading an entire file.
Modifying the data.
Rewriting the file.
Updating downstream systems.

This process becomes expensive at scale.

Apache Hudi solves this problem by enabling efficient record-level updates.

Key Features of Apache Hudi

Apache Hudi introduces several capabilities that make data lakes more powerful.

Incremental Processing

Instead of reprocessing entire datasets, Hudi allows systems to process only newly changed records.

Upserts

Hudi supports insert and update operations in a single command.

Deletes

Records can be removed efficiently without rebuilding entire datasets.

Transaction Management

Hudi provides ACID-like guarantees that improve data consistency.

Change Data Capture

Applications can track data changes and synchronize downstream systems efficiently.

Understanding Hudi Architecture

A simplified Hudi architecture looks like this:

Data Sources
      |
      v
Apache Hudi
      |
      +---- Transaction Management
      +---- Metadata Management
      +---- Incremental Processing
      |
      v
Data Lake Storage

Hudi acts as a management layer on top of existing storage systems.

Instead of replacing the data lake, it enhances it with additional capabilities.

Core Concepts in Apache Hudi

To understand Hudi, it's important to know its core components.

Hudi Table

A Hudi table is the primary unit of storage.

It contains:

Data files
Metadata
Commit history
Change information

Commit Timeline

Every data operation creates a commit.

Example:

Commit 001
Commit 002
Commit 003
Commit 004

This timeline helps track changes over time.

Record Keys

Each record has a unique identifier.

Example:

CustomerID = 101

Hudi uses record keys to determine whether a row should be inserted or updated.

Partitions

Data can be organized into partitions.

Example:

year=2026/month=07

Partitioning improves query performance and storage efficiency.

Storage Types in Hudi

Apache Hudi supports two storage models.

Copy-on-Write (CoW)

In Copy-on-Write mode:

Updates rewrite affected files.
Read performance is optimized.
Query latency is lower.

Architecture:

Update
   |
   v
Rewrite File
   |
   v
New Version

This mode is ideal for analytics-heavy workloads.

Merge-on-Read (MoR)

In Merge-on-Read mode:

Updates are stored separately.
Data is merged during reads.
Write performance improves.

Architecture:

Base File
    +
Delta Changes
    |
    v
Merged During Query

This approach is useful for streaming and real-time systems.

Incremental Queries

One of Hudi's most valuable features is incremental querying.

Traditional analytics:

Process Entire Dataset

Hudi approach:

Process Only New Changes

Benefits include:

Faster processing
Lower compute costs
Reduced resource consumption
Improved scalability

Organizations can significantly reduce ETL workloads using incremental queries.

Example: Writing Data with Spark

Apache Spark is commonly used with Hudi.

Example configuration:

hudi_options = {
    "hoodie.table.name": "customers",
    "hoodie.datasource.write.recordkey.field": "customer_id",
    "hoodie.datasource.write.operation": "upsert"
}

Writing data:

df.write.format("hudi") \
    .options(**hudi_options) \
    .mode("append") \
    .save("/data/customers")

This enables automatic insert and update handling.

Common Use Cases

Apache Hudi is widely used in modern data platforms.

Data Lakes

Manage continuously changing datasets efficiently.

Customer Analytics

Keep customer records synchronized across systems.

Streaming Data Pipelines

Support near real-time data ingestion and processing.

Change Data Capture

Track database changes and propagate updates downstream.

Business Intelligence

Provide fresh analytical data without expensive full reloads.

Benefits of Apache Hudi

Faster Data Processing

Incremental processing reduces unnecessary computation.

Lower Infrastructure Costs

Organizations spend less on storage and processing resources.

Improved Data Freshness

Data becomes available for analytics much faster.

Simplified Pipelines

Fewer ETL jobs are required to maintain analytical datasets.

Better Scalability

Hudi handles large datasets efficiently as data volumes grow.

Best Practices

Choose the Right Storage Mode

Use:

Copy-on-Write for analytics-heavy workloads.
Merge-on-Read for streaming-heavy workloads.

Design Effective Partitioning

Poor partitioning can negatively impact performance.

Monitor Commit Activity

Track commits and storage growth to avoid performance issues.

Optimize File Sizes

Large and balanced file sizes improve query performance.

Use Incremental Queries

Take full advantage of Hudi's incremental capabilities to reduce processing costs.

Apache Hudi vs Traditional Data Lakes

Feature	Traditional Data Lake	Apache Hudi
Inserts	Yes	Yes
Updates	Limited	Yes
Deletes	Limited	Yes
Incremental Queries	No	Yes
Transaction Support	Limited	Yes
Change Data Capture	No	Yes
Streaming Support	Limited	Yes

Apache Hudi transforms static data lakes into dynamic data platforms capable of handling modern analytical workloads.

Conclusion

Apache Hudi brings powerful database-like capabilities to modern data lakes. By supporting updates, deletes, transactions, incremental processing, and change data capture, it enables organizations to build more efficient and scalable data platforms.

Whether you're managing customer data, streaming events, business intelligence workloads, or large-scale analytics systems, Hudi helps reduce processing costs while improving data freshness and operational simplicity. As organizations continue to rely on real-time insights and continuously changing datasets, Apache Hudi has become a valuable technology for building modern data lake architectures.