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:
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:
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:
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:
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:
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.