Apache Iceberg vs Hudi: Key Features, Performance & Use Cases

Effectively storing, managing, and analyzing large volumes of complex datasets has become crucial for many organizations across industries. While traditional data warehouses are robust data solutions, they cannot keep up with modern data scale, variety, and velocity. 

On the other hand, data lakes offer massive, cost-effective storage capabilities but need more structure and governance of data warehouses. This has led to the rise of data lakehouses, a new type of storage solution that bridges this gap.

A lakehouse offers a data lake's scalability and flexibility with a warehouse's structure and reliability. It provides a unified platform for storing, processing, and deriving insights from organizational data assets.

Building and managing this architecture requires particular data management frameworks like Apache Hudi and Iceberg. While these tools differ in their data models and specific use cases, they also share similarities.

This article explores these platforms, offering a detailed comparison of Iceberg vs. Hudi, including their key features, differences, and best use cases.

Understanding Data Lakehouse Table Formats

Data lakehouses combine the capabilities of data lakes and warehouses, offering a unified platform for storing, processing, and analyzing data. However, understanding table formats is essential to effectively manage and query this data.

Table formats are metadata layers built on formats like Parquet or ORC files that enhance data structure and accessibility.

Some examples of modern table formats include Apache Iceberg, Delta Lake, and Apache Hudi. Table formats are crucial for data lakehouse architecture for several reasons:

• ACID Transactions: Table formats ensure data consistency and integrity, even during concurrent updates and deletes, without data corruption.
  
• Schema Evolution: They allow flexible schema changes without disrupting existing data or applications.
  
• Performance Optimization: These formats store data in a way tailored to specific access patterns. This helps with efficient query performance, especially for complex analytical workloads.
  
• Unified Storage Layer: Table formats provide a unified view of data, regardless of source or format. This facilitates easier data integration and access.

You can build robust, scalable, and efficient data lakehouse solutions by understanding and selecting the appropriate table format. This will enable you to derive faster, data-driven insights, improve data governance, and make more agile decisions.

If you're interested in a detailed comparison of Apache Iceberg and Delta Lake, including their unique features and use cases, check out this comprehensive guide on Apache Iceberg vs Delta Lake.

Apache Iceberg Overview

Apache Iceberg is an open table format well-suited for managing petabyte-scale datasets. It helps you determine how to organize, manage, and track files that make up your tables. As an open table format, Iceberg isn’t tied to any specific file format and can work with multiple formats like Avro, Parquet, and ORC.

Originally developed at Netflix, Iceberg was open-sourced as an Apache Incubator project in 2018. It was built to address Hive's data consistency and performance issues when used with data in S3. This solution involves Iceberg using a persistent tree structure to keep track of the complete list of files within a table. The table state is stored in different metadata files, which speeds up queries involving data in the table.

With its capabilities to easily integrate with big data processing frameworks like Flink, Trino, Hive, and Spark, Iceberg lets you manage complex, evolving datasets. This ensures efficient data operations and analytics across various environments with enhanced accessibility.

Key Features of Apache Iceberg

• Snapshot Isolation: With Iceberg, you can ensure that concurrent reads and writes don’t interfere with each other by using optimistic concurrency and a snapshot-based architecture. This guarantees consistent query results and reliable data processing updates and downstream tasks.
  
• Partition Evolution: The table format facilitates easy partition updates with its partition evolution feature. With Iceberg, you can add new data to your dataset while preserving the integrity of your old data. This is possible because Iceberg implements split planning for each partition spec.
  
• Time Travel: Iceberg maintains a historical log of table snapshots and supports time-travel queries. This allows you to access and query older dataset versions by specifying a snapshot or timestamp. You can also perform table rollbacks and revert to past data states. This feature is helpful during auditing, debugging, or data recovery.
  
• Data Compaction: Iceberg consolidates small files into larger, more efficient ones to improve query performance and reduce storage overhead. This process is helpful in distributed environments and high-frequency write scenarios like streaming. 

Apache Iceberg Architecture Overview

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Iceberg tables are organized into three layers: catalog, metadata, and data.

1. Catalog: The Iceberg catalog stores the location of the pointer referencing the current metadata file for each table. It supports atomic operations to update this pointer, ensuring data correctness and consistency.
   
2. Metadata: The metadata file contains information on schema, partition, snapshots, and the current snapshot ID. Each snapshot listed in the metadata file points to a manifest list. This manifest list contains all the file manifests that comprise the snapshot. Here, file manifests refer to a collection of files that track data files and their statistics. This helps in parallelism and improves data reuse efficiency.
   
3. Data: Organized into manifest files, Manifest files also contain details about column values' lower and upper bounds, record contents, and partition membership. The query engine retrieves the current metadata file from the catalog when executing a query. Then, it fetches the manifest list and the data files specified in the manifest.

Apache Hudi Overview

Apache Hudi (Hadoop Upsert Delete and Incremental) is an open-source data management framework initially developed by Uber in 2016. It is highly adaptable; you can use it with various cloud storage platforms, including Google Cloud Storage, Alibaba Cloud OSS, IBM Cloud Object Storage, and Azure.

Hudi also integrates with the Amazon Elastic MapReduce (EMR) platform, an AWS service for big data processing. When setting up an EMR cluster, if you select Spark, Hive, or Presto, Hudi is automatically included in the installation. Integrating Hudi with Amazon EMR makes your data accessible from multiple query engines, avoiding vendor lock-in.

Apache Hudi brings the transactional capabilities of warehouses to data lakes. It simplifies data ingestion, real-time processing, and pipeline development by supporting flexible data formats like Parquet or ORC. These formats allow you to use Hudi to perform efficient upserts, deletes, and queries directly on the data lake. 

Key Features of Apache Hudi

• Incremental Data Processing: With Apache Hudi, you can track changes at the record level. This allows you to process only modified data instead of entire datasets and perform efficient updates and deletes.
  
• ACID Transactions: Hudi supports ACID (Atomicity, Consistency, Isolation, Durability) transactions on large datasets in distributed environments (Hadoop) and cloud-based data lakes. This ensures consistent data reads and writes, enabling you to work with real-time data without conflicts.
  
• Efficient Indexing: The indexing mechanism in Hudi maps a record key to a file ID, ensuring quick data retrievals during upserts and deletes. This helps Hudi to locate the correct files without scanning the entire dataset. Hudi supports index types, including BLOOM, SIMPLE, HBASE, and more, to reduce merge costs and provide faster response times.
     
• Built-in Data Versioning and Timeline Management: Hudi maintains a detailed timeline of all data operations, including commits, cleanups, and compactions. This allows you to roll back changes or access historical table versions for data recovery, debugging, or auditing.

Apache Hudi Architecture Overview

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Apache Hudi captures data changes by organizing timestamps and operation types into a structured timeline. Its architecture revolves around three key components: Metadata, Base and Log Files, and Special Fields.

1. Metadata: Each Hudi table is stored in a directory containing partition folders for data files and a .hoodie folder with metadata. The metadata folder includes indexes like the files index (file details), column stats index (column statistics), and Bloom filter index (bloom filters of data files). Metadata is stored in the HFile format for efficient and scalable operations.
   
2. Base and Log Files: Hudi maintains two primary file types:
   1. Base Files: Stored in Parquet or ORC format, representing the latest state of the data.
   2. Log Files: Track incremental changes to base files, enabling efficient updates and deletes. Data changes progress through a timeline of stages: requested, inflight, and commit.
      
3. Special Fields: Hudi adds fields like _hoodie_commit_time (commit timestamps) and _hoodie_record_key (unique record identifiers) to each table. These fields support effective transaction management by tracking data versions and ensuring consistency.

These components work cohesively to provide Apache Hudi's structured timeline architecture. This architecture ensures efficient data management, enabling real-time ingestion, updates, and reliable transaction consistency in modern data lakes.

Key Differences Between Apache Hudi vs Iceberg

While Apache Hudi and Iceberg are built to enhance data management within data lakehouses, they have distinct purposes. With Iceberg, the primary aim is to create a portable table format that can replace Hive's catalog and metadata management. Contrarily, Hudi enables you to perform comprehensive database operations on data lakes. Here’s a detailed explanation of how Apache Iceberg and Hudi differ in certain aspects.

Iceberg vs. Hudi: Underlying Architecture

Hudi employs a log-structured storage architecture where data is appended to a write log. It relies on a timeline-based strategy involving log files, base files, and metadata tables. The metadata tables containing information about file splits, indexes, and commit history are all stored in metadata folders. This architecture is well-suited for streaming real-time data ingestion and updates.

Conversely, Iceberg employs a three-tier metadata tree structure that separates data files from their metadata. This architecture allows Iceberg to skip unnecessary data files during queries and achieve scalability, flexibility, and efficient query optimization.

The three tiers include metadata files, manifest lists, and manifests. Metadata files store information about partitions, schema references, and data statistics. Manifest lists point toward the most recent data manifests containing records of all data files associated with a table.

Apache Iceberg vs Hudi: Schema Evolution

Both Hudi and Iceberg support schema evolution, but the mechanisms differ slightly. Hudi supports schema evolution, which allows changes to the data schema over time without disrupting queries and ensuring backward compatibility. 

On the other hand, Iceberg uses in-place schema evolution to add, remove, and rename columns in a table without requiring table rewrites. It records changes within the table's metadata and maintains a transaction log for each table. This log captures snapshots of data files and tracks detailed histories of schema changes.

Iceberg vs Hudi: Data Ingestion

Regarding data ingestion, Hudi is optimized for near-real-time data ingestion. It has built-in support for efficient bulk inserts, updates, and deletes with its log-structured design. Hudi also features a robust data ingestion utility, DeltaStreamer. It helps with incremental data ingestion from various sources like database changelogs, JDBC, Kafka, and S3 events. 

Conversely, Iceberg’s flexibility in data formats and storage systems makes it a preferred choice for complex ingestion pipelines. While it is not as efficient as Hudi, Iceberg supports real-time data ingestion using streaming sources or in batches. However, it is more optimized for batch data ingestion.

Hudi vs Iceberg: Read and Write Optimization

Hudi optimizes data storage and retrieval through two primary approaches: copy-on-write (CoW) and merge-on-read (MoR). CoW is suitable for read-heavy workloads as it allows you to create new file versions for each update. Conversely, MoR is suited for write-heavy scenarios; it involves logging changes to delta files and merging them into the base file during read or compaction operations.

Iceberg focuses more on read optimization with its data layout, such as hidden partitioning, and avoids costly rewrites. It optimizes read and write operations by managing file sizes through compaction, deleting old snapshots, and utilizing appropriate write isolation levels.

Hudi vs Iceberg: Performance

The performance of Hudi and Iceberg depends on various factors, including data volume, query complexity, and hardware resources. Hudi generally offers better write performance, especially for real-time data ingestion, updates, and simple query execution. 

On the contrary, Iceberg leverages techniques like file-level operations, Z-order partitioning, and bloom filters to enhance query performance. It outperforms Hudi in read-heavy, analytical workloads and complex queries on large datasets.

A Feature Comparison: Apache Hudi vs Iceberg  

Use Cases and Adoption Examples

Apache Hudi and Iceberg offer several features that allow you to achieve faster time-to-insights. These tools have many use cases and can help you get maximum value from your data assets.

Here are some use cases for you to explore:

Use Cases of Apache Hudi

• Streaming Data Lake: Hudi offers near real-time ingestion in data lakes and a resource-efficient alternative to bulk data uploads from OLTP sources. For streaming sources like Kafka, Hudi helps you de-duplicate events by comparing incoming data to what’s already stored, ensuring data freshness and reducing redundancy.
  
• Cloud-Native Tables: Apache Hudi simplifies creating and managing cloud-native tables. It allows you to define tables, track metadata, manage schema, and query data using SQL-like syntax. With Hudi’s multi-dimensional partitioning, compaction, and clustering, you can ensure optimal performance with minimal operational overhead in cloud environments.

Use Cases of Apache Iceberg

• Enhancing Data Lakes: Iceberg combines data lakes' flexibility with modern data warehouses' transactional guarantees. It supports ACID transactions and allows you to reliably run complex ETL workflows and transformations in data lakehouse architectures.
  
• Real-Time Analytics: Iceberg is best suited for real-time analytics on large datasets. It efficiently handles frequent data updates from sources such as IoT devices or online platforms. Features like time travel and snapshot isolation ensure faster query implementation even with high-volume data ingestion.

Streamlining Data Workflows with Estuary Flow

Estuary Flow is a real-time ETL data integration tool. It helps automate complex data pipeline tasks like high-volume data ingestion, schema change management, and data synchronization across systems.

With Estuary Flow, you can ensure clean and consistent data is available to Apache Hudi and Iceberg. This enhances their capabilities to streamline lakehouse operations and improve performance. By utilizing Estuary Flow to load data into Iceberg, you can also develop a streaming data lakehouse (a regular lakehouse with a streaming ingestion layer).

Estuary Flow offers a library of over 200 pre-built connectors, letting you transfer your data incrementally, in batches, or in real-time. These no-code connectors allow even your non-technical team members to explore data effectively. The streaming Change Data Capture (CDC) connectors can capture data incrementally with sub-100ms end-to-end latency. 

The connectors facilitate automated data collection from various sources, in-flight transformations, and loading into your preferred destination. Using Estuary Flow to integrate your data with Apache Hudi or Iceberg, you can create a scalable and efficient architecture for your data flows.

Key Features of Estuary Flow

• Multiple Deployment Options: Estuary Flow caters to your organization's data needs by providing three deployment options: Public Deployment, Private Deployment, and Bring Your Own Cloud (BYOC). You can pick one of these variations based on the scale of your organization, security and compliance requirements, and infrastructure support. 
  
• Change Data Capture (CDC): With Estuary Flow's CDC feature, you can capture real-time changes, such as inserts, deletes, and updates, in your source system. This continuous stream of changes ensures data flowing into Apache Hudi or Iceberg stays synchronized. CDC allows you to enhance scalability and access historical data changes for recovery or backfilling.
  
• Many-to-many Connections: You can implement flexible data integration using Estuary Flow’s many-to-many connections feature. It lets you connect various data sources and destinations using a single pipeline. You can effectively join tables and leverage foreign vital references for efficient data access and analysis.
  
• No-code Configuration: Estuary Flow provides a powerful CLI and a UI-forward web application. This caters to both technically proficient teams and teams with non-coding backgrounds. The tool simplifies the process of building and managing data pipelines, enabling you to easily perform downstream analytics. 

Choosing Between Apache Iceberg and Apache Hudi

Apache Hudi and Iceberg are equally capable tools for building and managing data lakehouses. They both support schema evolution, time travel, and ACID transactions. However, their distinction lies in their architectures, partitioning methods, and data versioning.

Apache Hudi is ideal for streaming data environments. It leverages a log-structured design for efficient data appends and offers robust data ingestion utilities. These capabilities make Hudi suitable for environments with continuous data updates and write-heavy scenarios.

On the other hand, Iceberg is optimized for large-scale batch processing and complex querying. Iceberg would be a good choice if your priority is high-performance read operations, especially for analytical workloads requiring complex data computations.

Based on your specific use case, you can compare Iceberg vs Hudi and decide which framework works best for you.

Closing Thoughts

Apache Hudi and Iceberg are appropriate tools to create robust, scalable, and efficient data lakehouses. Each option offers various features to help you accommodate your growing data volumes.

Hudi is known for its incremental data processing and advanced indexing. Hudi can efficiently handle streaming data and real-time updates, particularly in environments requiring frequent data refreshes. This enables you to access the latest data insights quickly.

Similarly, Apache Iceberg offers schema evolution and snapshot isolation features to manage large-scale data batches and complex querying. It helps enhance data recovery efforts and ensures consistent performance, even for heavy data processing tasks.

To integrate your data into Apache Hudi or Iceberg, you can use Estuary Flow, making it easier to build and manage data lakehouses. It offers minimal latency during data transfers and facilitates real-time ETL, ELT, and CDC workflows. Estuary Flow’s extensive library of pre-built connectors simplifies the data pipeline creation process.

To learn more about leveraging Estuary Flow within your customized use cases, you can connect with our experts on Slack. The official documentation also provides detailed explanations for further reference. 

FAQs

What are the disadvantages of the Apache Iceberg?

Apache Iceberg relies heavily on metadata for data storage and retrieval. This makes it vulnerable to errors if the metadata is not maintained correctly. It also supports a limited set of complex query types, which can make it less useful during complex data analysis.

When not to use Apache Hudi?

Apache Hudi is not suitable for OLTP workloads where low-latency transactions are crucial. It is also not ideal for scenarios requiring sub-minute processing delays, as Hudi prioritizes efficient batching over low latency. This makes it less practical for real-time transactional processing.   

How does Iceberg support multiple concurrent writes using optimistic concurrency?

Iceberg uses optimistic concurrency by allowing each writer to assume exclusive access. Writers create a new metadata version and try to swap it with the current version atomically. If the swap fails due to a conflict, the writer has to retry using the latest table state. This method assumes rare conflicts and allows writers to modify data concurrently without preliminary locking.