Apache Hudi
UnclaimedAn open data lakehouse platform bringing database functionality to your data lakes.
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Reviews onG2
1 reviews trackedThe Bottom Line
Entry price
Free, no paid tier
Biggest pro
Battle-tested and proven in production at large scale
Biggest con
Requires a deeper understanding of data lakehouse concepts and Hudi-specific configurations compared to traditional data warehouses.
TL;DR - Apache Hudi
- Brings database functionality (ACID transactions, updates, deletes) to data lakes.
- Enables incremental processing for low-latency, minute-level analytics, replacing batch pipelines.
- Offers extensive integrations across data ecosystems and multi-cloud support for flexible data management.
Pricing: Free forever
Best for: Individuals & startups
What is Apache Hudi?
Apache Hudi is an open-source data lakehouse platform designed to bring robust database functionalities, such as transactional guarantees and incremental processing, to large-scale data lakes. It leverages a high-performance open table format to enable minute-level analytics and replaces traditional slow batch processing with an incremental processing framework.
This platform is ideal for organizations dealing with high volumes of streaming data, CDC (Change Data Capture) from databases, and those looking to build resilient data pipelines with ACID properties. It caters to data engineers, architects, and analysts who need to manage and query historical data, ensure data quality, and optimize performance across multi-cloud environments. Hudi's extensive integrations with various data streaming tools, databases, file formats, lake storage, data catalogs, data warehouses, interactive analytics engines, and data processing frameworks make it a versatile solution for modern data architectures.
Key benefits include significantly faster ingestion and lower processing times, the ability to update and delete data efficiently with pluggable indexing, and automatic table services for continuous optimization. It also supports schema evolution and enforcement, ensuring pipeline resilience and preventing data corruption.
Available on: Web
Pros & Cons
Pros
- Battle-tested and proven in production at large scale
- Thriving and growing open-source community
- Purpose-built storage format for continuous performance at scale
- Built-in CDC sources and tools for streaming ingestion
Cons
- Requires a deeper understanding of data lakehouse concepts and Hudi-specific configurations compared to traditional data warehouses.
- Performance optimization might require fine-tuning of table services and indexing strategies.
- While it simplifies many aspects, managing a Hudi-based data lakehouse still involves operational complexity, especially at scale.
Ratings Across the Web
4.5(1 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Mutability support for updates and deletes with fast, pluggable indexingIncremental processing for 10x efficiency and faster data pipelinesACID transactional guarantees (atomic writes, snapshot isolation, non-blocking concurrency)Time travel for querying historical data and auditing changesInteroperable multi-cloud ecosystem support with open data formatsAutomatic table services (clustering, compaction, cleaning, file sizing, indexing)Built-in tools for auto ingestion from services like Debezium and KafkaQuery acceleration through multimodal indexes
Pricing
Free
Apache Hudi is completely free to use with no hidden costs.
Reviews
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Apache Hudi FAQ
How does Apache Hudi support real-time analytics on large datasets?
Apache Hudi enables minute-level analytics by bringing database functionalities like transactional guarantees and incremental processing to data lakes. It replaces traditional slow batch processing with an incremental processing framework, allowing for significantly faster ingestion and lower processing times. This is achieved through a high-performance open table format and efficient update and delete capabilities with pluggable indexing.
Which teams benefit most from implementing Apache Hudi?
Apache Hudi is best suited for data engineers, architects, and analysts who manage and query historical data, ensure data quality, and optimize performance across multi-cloud environments. It is ideal for organizations dealing with high volumes of streaming data and Change Data Capture (CDC) from databases, especially those building resilient data pipelines with ACID properties.
How does Apache Hudi compare to Apache Kafka for data processing?
Apache Hudi is an open data lakehouse platform that brings database functionality to data lakes, focusing on transactional guarantees and efficient data management. In contrast, Apache Kafka is primarily a distributed streaming platform designed for publishing, subscribing to, storing, and processing streams of records. While Kafka handles data streams, Hudi focuses on managing and querying the data once it lands in the lake, offering features like updates, deletes, and schema evolution.
What kind of operational complexity is associated with Apache Hudi?
While Apache Hudi simplifies many aspects of data management, managing a Hudi-based data lakehouse still involves operational complexity, especially at scale. It requires a deeper understanding of data lakehouse concepts and Hudi-specific configurations compared to traditional data warehouses. Performance optimization might also necessitate fine-tuning of table services and indexing strategies.
Does Apache Hudi include a free tier?
Apache Hudi is free to use, as it is an open-source data lakehouse platform. There is no paid plan required to utilize its functionalities for bringing database capabilities to data lakes.
Can Apache Hudi handle schema changes in data pipelines?
Yes, Apache Hudi supports schema evolution and enforcement, which is crucial for maintaining pipeline resilience. This capability helps prevent data corruption by adapting to changes in data structure over time, ensuring data quality and consistency within the data lakehouse.
How does Apache Hudi ensure data quality and reliability?
Apache Hudi ensures data quality and reliability through several mechanisms, including transactional guarantees and ACID properties for data operations. It also supports schema evolution and enforcement, which helps prevent data corruption and maintains consistency. Automatic table services contribute to continuous optimization and data integrity.
Source: hudi.apache.org