A Comparative Analysis of Open Table Formats for the Modern Data Lakehouse: Apache Hudi, Delta Lake, and Apache Iceberg

Posted on by uplatzblog

Section 1: Executive Summary

The State of the Lakehouse in 2025

The modern data architecture has coalesced around the data lakehouse, a paradigm that merges the scalability and cost-effectiveness of data lakes with the performance and reliability of data warehouses. At the heart of this evolution are open table formats (OTFs), which provide the foundational metadata layer to enable these advanced capabilities. The intense competition between the three leading formats—Apache Hudi, Delta Lake, and Apache Iceberg—once characterized as the “format wars,” has matured into a new era of coexistence and interoperability.1

In 2025, the conversation has shifted decisively. The question is no longer which single format will achieve universal dominance, but rather how to strategically leverage the unique strengths of each within a heterogeneous data ecosystem. The rise of interoperability projects, most notably Apache XTable (incubating), signals a market acknowledgment that no single format is optimal for every workload.3 Organizations are now empowered to select a primary format best suited for their most critical write workloads while enabling seamless, multi-format access for diverse consumption patterns. This report provides a definitive, in-depth technical comparison to guide architects and engineers in making this strategic selection.

Synopsis of Core Strengths

While the formats are converging in functionality, their core design philosophies and architectural trade-offs remain distinct, making each uniquely suited for different strategic objectives.

  • Apache Hudi: Hudi has evolved beyond a mere table format into a comprehensive data lakehouse platform, distinguished by its rich suite of integrated table services.3 Its architecture is fundamentally optimized for high-throughput, low-latency write operations, particularly for streaming ingestion, incremental data processing, and Change Data Capture (CDC) workloads.6 Key differentiators include a sophisticated multi-modal indexing subsystem for accelerating updates and deletes, flexible write modes (Copy-on-Write and Merge-on-Read), and advanced concurrency control mechanisms designed for complex, multi-writer scenarios.8
  • Delta Lake: Developed and strongly backed by Databricks, Delta Lake offers a deeply integrated and highly optimized experience within the Apache Spark ecosystem.11 Its architectural simplicity, centered on an atomic transaction log, provides robust ACID guarantees and a straightforward model for unified batch and streaming data processing.6 Delta Lake excels in environments where Spark is the primary compute engine, benefiting from performance enhancements like Z-Ordering and tight integration with managed platforms like Databricks, which simplifies data management and governance.11
  • Apache Iceberg: Iceberg has emerged as the de facto open standard for large-scale analytical workloads, prized for its engine-agnostic design and broad industry adoption.15 Its core strengths lie in its architectural elegance and unwavering focus on correctness and reliability. A hierarchical, snapshot-based metadata model enables highly efficient query planning and data skipping, while innovative features like hidden partitioning and safe, non-disruptive schema and partition evolution provide unparalleled long-term table maintainability.17 Its wide support from vendors like Snowflake, AWS, Google, and Dremio makes it the safest choice for organizations prioritizing flexibility and avoiding vendor lock-in.16

 

Key Findings and Strategic Recommendations

 

The selection of an open table format is a foundational architectural decision with long-term consequences. This report concludes that the optimal choice is not absolute but is contingent on a careful evaluation of an organization’s primary workloads, existing technology stack, and overarching data strategy.

  • For streaming and CDC-heavy workloads requiring frequent, record-level updates and deletes, Apache Hudi presents the most advanced and feature-rich solution.
  • For organizations building an open, multi-engine analytical platform and prioritizing long-term maintainability and vendor neutrality, Apache Iceberg is the recommended foundation.
  • For enterprises deeply invested in the Databricks and Apache Spark ecosystem, Delta Lake provides the most seamless, optimized, and managed experience for unified data engineering and analytics.

Ultimately, the most forward-looking strategy involves choosing a primary write format aligned with these recommendations while actively planning for a multi-format data lakehouse. The adoption of interoperability tools like Apache XTable is critical, as it dissolves data silos and ensures that data remains a universal, accessible asset across all current and future tools in the organization’s data stack.4

 

Section 2: The Lakehouse Foundation: Understanding Open Table Formats (OTFs)

 

To fully appreciate the distinctions between Hudi, Delta Lake, and Iceberg, it is essential to first understand the fundamental problems they were designed to solve. Their emergence marks a pivotal architectural shift, transforming unreliable data swamps into structured, reliable, and performant data lakehouses.

 

The Evolution from Data Lakes to Lakehouses

 

Traditional data lakes, typically built on cloud object storage like Amazon S3 and using open columnar file formats like Apache Parquet or ORC, offered immense scalability and cost-effectiveness.21 However, when managed with early table abstractions like Apache Hive, they suffered from critical limitations that mirrored those of a simple file system, hindering their use for many enterprise workloads.6

The primary challenges of the Hive-based data lake included:

  • Lack of ACID Transactions: Operations were not atomic. A failed write job could leave a table in a corrupted, partial state, while concurrent writes could lead to inconsistent and unpredictable results.6
  • Difficult Schema Evolution: Modifying a table’s schema, such as adding or renaming a column, was a brittle and often destructive operation that could break downstream pipelines or lead to data corruption.6
  • Performance Bottlenecks: Query planning in Hive relied on a central metastore and often required slow and expensive list operations on the file system to discover data files. This became a significant bottleneck for tables with thousands of partitions.11
  • No Support for Fine-Grained Updates/Deletes: Parquet and ORC files are immutable. To update or delete a single record, an entire data file—often containing millions of other records—had to be rewritten. This made handling transactional data or complying with data privacy regulations like GDPR prohibitively expensive.24

Open table formats were created to solve these problems by introducing a crucial metadata layer that sits between the compute engines and the raw data files, effectively bringing database-like reliability and management features to the data lake.24

 

Core Tenets of Modern OTFs

 

All three major OTFs provide a common set of foundational capabilities that transform raw data files into reliable, manageable tables. These features are the bedrock of the modern data lakehouse.21

  • ACID Transactions: The most critical feature is the guarantee of Atomicity, Consistency, Isolation, and Durability (ACID) for data operations.21 OTFs achieve this by maintaining a transaction log or an atomic pointer to the table’s state. This ensures that any write operation (e.g., an INSERT, UPDATE, or MERGE) either completes fully or not at all, preventing data corruption. It also provides isolation, allowing multiple users and applications to read and write to the same table concurrently without interference.13
  • Schema Evolution: OTFs provide robust mechanisms to safely evolve a table’s schema over time. This includes adding, dropping, renaming, and reordering columns, or even changing data types, without needing to rewrite existing data files.21 This flexibility is invaluable for agile development and long-term table maintenance, as data structures inevitably change with business requirements.27
  • Time Travel and Data Versioning: By tracking every change to a table as a new, atomic version or “snapshot,” OTFs enable powerful time travel capabilities.17 Users can query the table as it existed at any specific point in time or at a particular transaction ID. This is critical for auditing, debugging data quality issues, rolling back erroneous writes, and ensuring the reproducibility of machine learning experiments and reports.21
  • Scalable Metadata Management: A key innovation of OTFs is their method of tracking data at the individual file level, rather than just at the partition (directory) level like Hive.11 Each table format maintains a manifest of all the valid data files that constitute a given table version. Query engines can read this manifest directly to get a complete list of files to process, completely avoiding slow and non-scalable directory listing operations. This enables tables to scale to petabytes of data and billions of files with high performance.23

The development of OTFs represents more than just an incremental improvement over Hive; it signifies a fundamental change in data platform architecture. Historically, to achieve reliability and performance, organizations were forced to move data from an open, low-cost data lake into a proprietary, coupled storage-and-compute data warehouse. OTFs invert this model. They bring the essential features of reliability, transactionality, and governance directly to the data where it lives—in open formats on open cloud storage. This enables a truly decoupled architecture where multiple, specialized compute engines can operate on a single, consistent, and reliable copy of the data, fulfilling the core promise of the data lakehouse.21

 

The Anatomy of an OTF

 

It is crucial to understand that an OTF is a specification for a metadata layer, not a file format itself.27 The actual data continues to be stored in efficient, open columnar file formats like Apache Parquet or ORC.23 The OTF acts as a wrapper or an intelligent index over these files. It consists of a set of metadata files that:

  1. Track the table’s current schema and partition specification.30
  2. Maintain a complete and explicit list of all data files belonging to the current version of the table, along with file-level statistics.30
  3. Log a chronological history of all changes (DML and DDL) applied to the table, enabling versioning and time travel.21

By providing this structured layer of abstraction, OTFs transform a simple collection of files in a directory into a robust, high-performance, and manageable database table.22

Table 2.1: Core Capabilities of Open Table Formats

 

Feature Apache Hudi Delta Lake Apache Iceberg
ACID Transactions Available 11 Available 11 Available 11
Time Travel Available 11 Available 11 Available 11
Schema Evolution Available 29 Available 15 Available 15
Concurrency Control MVCC, OCC, NBCC 11 Optimistic Concurrency Control (OCC) 11 Optimistic Concurrency Control (OCC) 11
Primary Storage Modes Copy-on-Write (CoW) & Merge-on-Read (MoR) 11 Copy-on-Write (CoW) 11 Copy-on-Write (CoW) 11
Managed Ingestion Available (via DeltaStreamer) 11 Not Available 11 Not Available 11

 

Section 3: Architectural Deep Dive: Metadata, Transactions, and Data Layout

 

The fundamental differences in philosophy and capability among Hudi, Delta Lake, and Iceberg stem directly from their distinct core architectural designs. Understanding how each format manages metadata, records transactions, and lays out data is critical to appreciating their respective strengths and weaknesses.

 

Apache Hudi: The Timeline-centric Architecture

 

Apache Hudi’s architecture is designed around the concept of a central “timeline,” making it exceptionally well-suited for managing incremental data changes and a rich set of automated table services.26 It functions less like a simple format and more like an integrated database management system for the data lake.

  • The Timeline: At the heart of every Hudi table is the timeline, an event log that maintains a chronological, atomic record of all actions performed on the table.8 Stored within the .hoodie metadata directory, this log consists of files representing “instants,” where each instant comprises an action type (e.g., commit, deltacommit, compaction, clean), a timestamp, and a state (requested, inflight, completed).8 This timeline is the source of truth for the table’s state, providing atomicity and enabling consistent, isolated views for readers.
  • File Layout and Versioning: Hudi organizes data into partitions, similar to Hive. Within each partition, data is further organized into File Groups, where each file group is uniquely identified by a fileId.8 A file group contains multiple File Slices, each representing a version of that file group at a specific point in time (a specific commit). A file slice consists of a columnar base file (e.g., a Parquet file) and, for Merge-on-Read tables, a set of row-based log files (e.g., Avro files) that contain incremental updates to that base file since it was created.8 This Multi-Version Concurrency Control (MVCC) design is fundamental to how Hudi handles updates and provides snapshot isolation.8
  • Base and Log Files: The physical storage model directly reflects Hudi’s dual write modes. In Copy-on-Write mode, only base files exist. In Merge-on-Read mode, new updates are appended quickly to log files, deferring the expensive process of rewriting the columnar base file to a later, asynchronous compaction job.11 This architectural separation of base and incremental data is a key enabler of Hudi’s low-latency ingestion capabilities.

 

Delta Lake: The Transaction Log Architecture

 

Delta Lake’s architecture is characterized by its simplicity and robustness, centered on a sequential, file-based transaction log that is deeply integrated with Apache Spark’s processing model.32

  • The Delta Log (_delta_log): The definitive component of a Delta table is its transaction log, stored in a _delta_log subdirectory.15 This log is an ordered record of every transaction that has ever modified the table. It is composed of sequentially numbered JSON files (e.g., 000000.json, 000001.json), where each file represents a single atomic commit.13 A commit file contains a list of actions, such as “add” a new data file or “remove” an old one, that describe the transition from one table version to the next.22
  • Commit Protocol: To perform a transaction, a writer generates a new JSON file and attempts to write it to the log. The sequential numbering and the atomic “put-if-absent” semantics of most cloud storage systems ensure that only one writer can create a given commit file, thus guaranteeing serializability and atomicity.13 When a query engine reads the table, it first consults the log to discover the list of JSON files, processes them in order, and thereby determines the exact set of Parquet data files that constitute the current, valid version of the table.11
  • Checkpoints: A long series of JSON commit files would be inefficient for query engines to process. To ensure metadata management remains scalable, Delta Lake periodically compacts the transaction log into a Parquet checkpoint file.15 This checkpoint file aggregates the state of the table up to a certain point in time, allowing a reader to jump directly to the checkpoint and then apply only the subsequent JSON logs. This mechanism is critical for maintaining high read performance on tables with long histories.11

 

Apache Iceberg: The Snapshot-based, Hierarchical Metadata Architecture

 

Apache Iceberg employs a fundamentally different architecture based on a tree-like, hierarchical metadata structure. This design prioritizes correctness, read performance, and engine agnosticism by completely decoupling the logical table state from the physical data layout.29

  • Three-Tier Structure: An Iceberg table is defined by a hierarchy of immutable metadata files 23:
  1. The Catalog: This is the entry point to the table, a metastore (like Hive Metastore or AWS Glue) that holds a reference—a simple pointer—to the location of the table’s current top-level metadata file.22 Transactions are committed by atomically updating this single pointer.
  2. Metadata Files: A metadata file represents a “snapshot” of the table at a specific point in time.23 It contains essential information such as the table’s schema, its partition specification, and a pointer to a manifest list file. Every write operation creates a new metadata file, producing a new snapshot.
  3. Manifest Lists: Each snapshot points to a manifest list, which is a file containing a list of all the manifest files that make up that snapshot.23 Crucially, each entry in the manifest list also stores partition boundary information for the manifest it points to, allowing query engines to prune entire manifest files without reading them.
  4. Manifest Files: Each manifest file contains a list of the actual data files (e.g., Parquet files).22 For each data file, the manifest stores its path, its partition membership information, and detailed column-level statistics (such as min/max values, null counts, and total record counts).11

This architecture was born from the need to solve the reliability and performance issues of petabyte-scale Hive tables at Netflix.11 The primary challenge was not rapid updates but ensuring correctness and enabling efficient query planning at massive scale. This led to a design that explicitly tracks every data file in immutable snapshots, which completely eliminates the need for slow and unreliable file system list operations.25 This explicit file tracking and the rich statistics stored in the manifests allow query planning to be parallelized and distributed, removing the central metastore as a bottleneck and making Iceberg a truly engine-agnostic open format. In contrast, Hudi’s architecture reflects its origin at Uber, where the need to handle high-volume, record-level “upserts, deletes, and incrementals” drove the creation of a sophisticated, service-oriented platform with its timeline and file-slicing mechanisms.18 Delta Lake, born at Databricks, naturally adopted a design mirroring a classic database transaction log, making it a seamless and powerful extension for the Spark ecosystem.11

 

Section 4: Core Feature Implementation and Trade-offs

 

The architectural foundations of each format directly influence how they implement core features like updates, concurrency, and schema management. These implementation details reveal critical trade-offs in performance, flexibility, and complexity.

 

Write and Update Strategies: Copy-on-Write (CoW) vs. Merge-on-Read (MoR)

 

The strategy for handling record-level updates and deletes is a primary differentiator, with significant implications for write latency versus read performance.

  • Apache Hudi: Hudi offers the most mature and flexible implementation, supporting two distinct table types from its inception 8:
  • Copy-on-Write (CoW): In this mode, any update to a record requires rewriting the entire data file (e.g., Parquet file) that contains that record. This incurs higher write amplification and latency, as a small update triggers a large file rewrite.11 However, it optimizes for read performance, as queries only need to read the latest, compacted base files without any on-the-fly merging.8 This makes CoW ideal for read-heavy, batch-oriented analytical workloads.29
  • Merge-on-Read (MoR): This mode is optimized for write-heavy and streaming ingestion scenarios. Updates are written rapidly as new records into smaller, row-based log files (also called delta files).11 This minimizes write latency. At query time, the engine must merge the data from the base Parquet file with the records in its associated log files to produce the latest view of the data.8 This read-side merge adds some query overhead, which is managed by an asynchronous compaction process that periodically merges the log files into a new version of the base file.26
  • Apache Iceberg and Delta Lake: Both formats were initially designed primarily around a CoW model. To handle updates or deletes, they would identify the affected data files and rewrite them. However, recognizing the need for lower-latency updates, both have evolved to incorporate MoR-like functionality. Iceberg achieves this by writing delete files (either position deletes, which specify rows to delete by file and position, or equality deletes, which specify rows to delete by value) that are applied at read time.34 Similarly, Delta Lake has introduced delete vectors, a feature that marks rows as deleted within existing Parquet files without rewriting them.4 While this demonstrates a convergence of capabilities, Hudi’s dual-mode architecture is more deeply integrated and offers more granular control over the write/read performance trade-off.6

 

Concurrency Control and Isolation

 

How a format manages simultaneous writes is critical for multi-user and multi-pipeline environments.

  • Apache Hudi: Hudi provides the most sophisticated and configurable concurrency control system, reflecting its focus on complex, high-throughput write environments.10 It supports multiple models:
  • Multi-Version Concurrency Control (MVCC): Provides snapshot isolation between writers and background table services (like compaction and cleaning), ensuring they do not block each other.11
  • Optimistic Concurrency Control (OCC): Allows multiple writers to operate on the table simultaneously. It uses a distributed lock manager (like Zookeeper or DynamoDB) to ensure that if two writers modify the same file group, only one will succeed, and the other must retry.10
  • Non-Blocking Concurrency Control (NBCC): An advanced model designed specifically for streaming writers to prevent starvation or livelock, where conflicts are resolved by the reader and compactor rather than failing the write job.10
  • Delta Lake: Delta Lake uses Optimistic Concurrency Control based on its transaction log.11 When a writer commits, it checks if any new commits have appeared in the log since it started its transaction. If so, and if the new commits conflict with the files the writer read or wrote, its commit will fail, and the operation must be retried. Delta offers two isolation levels 39:
  • WriteSerializable (Default): Ensures that write operations are serializable but allows for some anomalies on the read side for higher availability.
  • Serializable: The strongest level, guaranteeing that both reads and writes are fully serializable, as if they occurred one after another.
    More recently, Delta has introduced row-level concurrency, which can reduce conflicts by detecting changes at the row-level instead of the file-level for UPDATE, DELETE, and MERGE operations.39
  • Apache Iceberg: Iceberg employs a pure Optimistic Concurrency Control model that is elegant in its simplicity and designed for engine agnosticism.25 A commit is finalized via a single atomic compare-and-swap (CAS) operation on the pointer to the current metadata file in the catalog.19 If two writers attempt to commit simultaneously, the CAS operation ensures only one succeeds. The writer that fails must then re-read the new table metadata, re-apply its changes on top of the new state, and retry the commit.25 This simple contract—that the catalog must support an atomic CAS operation—is what allows Iceberg to be easily supported by a wide variety of engines and metastores. While effective, this model can lead to increased retries and contention in workloads with many frequent, small commits to the same table.40

Table 4.1: Concurrency Control Mechanisms Compared

 

Feature Apache Hudi Delta Lake Apache Iceberg
Primary Model OCC, MVCC, NBCC 10 Optimistic Concurrency Control (OCC) [38] Optimistic Concurrency Control (OCC) 25
Isolation Levels Snapshot Isolation 26 WriteSerializable (Default), Serializable 39 Serializable (via atomic commits) 25
Conflict Granularity File-level [37] File-level; Row-level (with delete vectors) 39 File-level 25
Key Differentiators Non-Blocking model for streaming; Separate controls for writers vs. services [31] Tunable isolation levels; Deep Spark integration 39 Simple, engine-agnostic atomic swap on catalog pointer 19

 

Schema Evolution

 

The ability to safely modify a table’s schema is a core advantage of OTFs over Hive.

  • Apache Iceberg: Iceberg’s approach is widely considered the most robust and flexible.6 It tracks all columns by a unique field ID that is assigned when the column is added and never changes.17 This allows for safe ADD, DROP, RENAME, and REORDER operations, as well as type promotion (e.g., int to long), all without rewriting any data files.17 The schema for any given data file is stored with it in the metadata, ensuring that data is always interpreted correctly, regardless of schema changes.
  • Delta Lake and Apache Hudi: Both also provide strong support for schema evolution.15 Delta Lake, by default, enforces the schema on write, preventing accidental writes with mismatched schemas.13 It supports schema evolution to allow for adding new columns. More advanced operations like renaming or dropping columns are supported through a column mapping feature, which, similar to Iceberg, decouples the physical column name from the logical one.32 Hudi also supports schema evolution, ensuring backward compatibility for queries.29

 

Partitioning Strategies

 

Partitioning is a key technique for improving query performance by pruning data.

  • Apache Iceberg: Iceberg revolutionizes partitioning with two unique features:
  • Hidden Partitioning: Iceberg can generate partition values from a table’s columns using transform functions (e.g., days(ts), bucket(16, id)).17 These partition values are managed internally by Iceberg. Users can write queries with simple filters on the raw columns (e.g., WHERE ts > ‘…’), and Iceberg automatically handles pruning based on the transformed partition values. This abstracts away the physical layout, simplifying queries and preventing user errors.19
  • Partition Evolution: A table’s partitioning scheme can be changed over time without rewriting old data.18 New data will be written using the new partition scheme, while old data remains in its original layout. Iceberg’s query planner understands the different partition layouts and processes queries correctly across all data. This is a powerful feature for long-lived tables where query patterns evolve.42
  • Delta Lake and Apache Hudi: Both use a more traditional, Hive-style partitioning approach where partitions correspond directly to directories in the file system.3 While effective, this approach is less flexible than Iceberg’s. Delta Lake enhances this with performance features like Z-Ordering, which can improve data skipping on non-partitioned columns within a partition.14 Hudi’s philosophy encourages using coarser-grained partitions and leveraging its indexing and file clustering capabilities for fine-grained performance tuning, avoiding the “too many partitions” problem that plagues Hive.3

 

Section 5: Performance Optimization and Data Management

 

Beyond core features, the three formats offer distinct approaches to performance tuning and ongoing data management. These capabilities, including compaction, data skipping, and deletion, are crucial for maintaining the health and efficiency of a data lakehouse at scale.

 

Compaction and Small File Management

 

Streaming ingestion and frequent updates can lead to the “small file problem,” where a table consists of a vast number of small files. This degrades query performance because of the high overhead associated with opening and reading each file.43 All three formats provide mechanisms to compact these small files into fewer, larger ones.

  • Apache Hudi: Hudi provides a comprehensive and highly configurable set of asynchronous table services for managing table layout. For MoR tables, compaction is a core process that merges the incremental data from log files into new, optimized columnar base files.8 Hudi offers a rich set of trigger strategies (e.g., run compaction after a certain number of commits or after a specific time has elapsed) and compaction strategies (e.g., prioritizing partitions with the most uncompacted data).45 This allows operators to fine-tune the balance between data freshness and query performance, showcasing Hudi’s platform-like capabilities.
  • Delta Lake: Delta Lake addresses the small file problem with the OPTIMIZE command.46 This user-triggered operation uses a bin-packing algorithm to coalesce small files into larger, optimally-sized files (defaulting to 1 GB).46 The operation is transactional and can be targeted to specific partitions to avoid rewriting the entire table.48 This provides a simple and effective mechanism for table maintenance.
  • Apache Iceberg: Iceberg provides a similar capability through its rewrite_data_files action, which can be invoked via Spark or other engines.34 This action also supports bin-packing to compact small files and can additionally apply sorting or Z-ordering during the rewrite process to optimize data layout for better query performance.34 Like all Iceberg operations, compaction is an atomic transaction that creates a new table snapshot, ensuring that concurrent reads are not disrupted.43

 

Data Skipping and Query Pruning

 

Minimizing the amount of data read from storage is the most effective way to accelerate analytical queries. Each format employs different techniques to prune unnecessary data files during query planning. The philosophical difference is stark: Hudi focuses on optimizing writes, while Iceberg and Delta focus on optimizing reads.

  • Apache Hudi: Hudi’s primary performance feature is its sophisticated, multi-modal indexing subsystem, which is designed to accelerate write operations like upserts and deletes.9 The index maintains a mapping between a record key and the file group where that record is stored. This allows Hudi to quickly locate the specific file that needs to be updated without scanning the entire table, which is a massive performance gain for transactional workloads.8 Hudi offers several index types, including:
  • Bloom Filters: A probabilistic data structure stored in file footers to quickly rule out files that do not contain a specific key.9
  • Simple Index: An in-memory index suitable for smaller tables.
  • Record-level Index: A powerful global index, backed by Hudi’s internal metadata table, that provides a direct mapping of record keys to file locations, significantly speeding up lookups in large deployments.9
    While designed for writes, this indexing can also benefit read-side point lookups.
  • Apache Iceberg: Iceberg excels at read-side performance through powerful data skipping capabilities built into its metadata structure. The manifest files store detailed, column-level statistics (min/max values, null counts) for every data file.17 During query planning, the engine can use these statistics to compare the predicate of a query (e.g., WHERE region = ‘East’) against the min/max values for the region column in each data file. If the value ‘East’ does not fall within a file’s range, that entire file can be skipped without being opened or read. Crucially, this works even for non-partitioned columns, providing a significant advantage over traditional Hive-style partition pruning.52
  • Delta Lake: Delta Lake also implements data skipping by storing column-level statistics in its transaction log.12 Query engines use this information to prune files that do not contain relevant data. Delta Lake further enhances this with Z-Ordering, a data layout technique applied via the OPTIMIZE ZORDER BY command.14 Z-Ordering co-locates data with similar values across multiple columns within the same set of files. This multi-dimensional clustering improves the efficiency of data skipping when queries filter on multiple columns that have been included in the Z-order index.14

 

Data Deletion and Compliance (GDPR/CCPA)

 

The ability to efficiently handle record-level deletes is a critical requirement for modern data platforms, driven by privacy regulations like GDPR and CCPA.

  • Apache Hudi: Hudi provides robust support for deletions. It can perform soft deletes, where specific fields (e.g., personally identifiable information) are nulled out via an upsert operation, and hard deletes, where the entire record is physically removed from the table.41 Hard deletes are typically implemented by writing a record with a special “empty” payload, which instructs Hudi to remove the record during the merge/compaction process.54 Hudi’s capabilities are frequently cited in use cases involving GDPR compliance.55
  • Apache Iceberg: Handling GDPR in Iceberg requires careful operational procedures due to its versioned, snapshot-based architecture.57 A delete operation creates a new snapshot where the data is no longer visible, but the data itself persists in older snapshots and their associated data files. To be fully compliant, an organization must 57:
  1. Execute the delete operation (either via CoW rewrite or by writing MoR delete files).
  2. Run the snapshot expiration procedure to remove old snapshots containing the deleted data from the table’s history.
  3. Run the orphan file cleanup procedure to physically delete the underlying data files that are no longer referenced by any valid snapshot.
    This multi-step process must be automated and monitored to ensure compliance within regulatory timelines.57
  • Delta Lake: Delta Lake supports DELETE operations, which, like updates, are recorded in the transaction log. The physical removal of data files that are no longer referenced by the current version of the table is handled by the VACUUM command.60 This command removes files that are older than a specified retention period (defaulting to 7 days), which is a critical step for ensuring that deleted data is physically purged from storage to meet compliance requirements.60

 

Section 6: The Broader Ecosystem: Integration, Interoperability, and Vendor Alignment

 

The value of a table format extends beyond its technical features to its integration with the broader data ecosystem. Engine support, vendor backing, and the ability to interoperate are critical factors in its long-term viability and utility within an enterprise data platform.

 

Query Engine Support

 

A table format’s utility is directly proportional to the number and quality of query engines that can read from and write to it.

  • Apache Spark: All three formats have first-class support for Apache Spark, as it is the dominant engine for large-scale data processing.62 Delta Lake, having been developed by Databricks, has the deepest and most native integration with Spark and its Structured Streaming APIs.11 Hudi and Iceberg also provide comprehensive Spark integrations for both batch and streaming workloads.5
  • Apache Flink: For real-time stream processing, Apache Flink support is crucial. Both Hudi and Iceberg have invested heavily in robust Flink connectors, making them strong choices for streaming-first architectures.5 Delta Lake’s Flink support is also available, contributing to its goal of being a unified format.28
  • Trino and Presto: For interactive SQL querying, the Trino and Presto communities have broadly embraced Apache Iceberg. Its engine-agnostic design and performant metadata scanning make it a natural fit, and it is often considered the best-supported and most feature-complete format within the Trino ecosystem.15 Hudi and Delta Lake also have connectors for Trino and Presto, enabling interactive queries on those formats as well.11
  • Cloud Data Services: The formats are increasingly supported natively by major cloud providers. AWS, for instance, offers native support for all three formats in services like AWS Glue and Amazon EMR, simplifying deployment and removing the need for users to manage dependencies.62 Cloud data warehouses and query services like Amazon Athena, Amazon Redshift Spectrum, and Google BigQuery are also adding read support, particularly for Iceberg and Delta Lake.11

 

Vendor Landscape and Community Dynamics

 

The strategic backing and community health of each project are strong indicators of its future trajectory.

  • Delta Lake: The project is primarily led and backed by Databricks. This provides it with significant engineering resources and a clear product vision, but also means that its development is closely tied to the Databricks platform’s roadmap.6 While Delta Lake is an open-source project under the Linux Foundation, its most advanced performance optimizations and features are often available first, or exclusively, within the proprietary Databricks environment.11
  • Apache Hudi: Originally developed at Uber, Hudi is now a top-level Apache project with a vibrant community. It has strong commercial backing from companies like Onehouse, which was founded by Hudi’s creators and offers a managed Hudi-as-a-service platform.1 The Hudi community’s focus has been on building out a comprehensive set of platform services, positioning Hudi as more than just a format but a full-fledged lakehouse management system.3
  • Apache Iceberg: Iceberg boasts the most diverse and powerful coalition of backers in the industry. It is a strategic technology for major data players including Snowflake, AWS, Google, Dremio, and Tabular.15 This broad support from competing vendors solidifies its position as a neutral, cross-platform standard. For organizations wary of vendor lock-in, Iceberg’s truly open governance and multi-vendor support make it the safest long-term bet.2

The market dynamics have shifted from a zero-sum competition to a multi-format reality. Major vendors, including Databricks, now recognize the need to support multiple formats to capture diverse workloads and cater to customer demands for openness.15 The strategic battleground is consequently moving up the technology stack, from the table format itself to the data catalog layer (e.g., Databricks Unity Catalog, Snowflake’s Polaris Catalog, open-source Nessie) and the managed compute services that can operate efficiently across these open formats.1 The table format is becoming a commoditized, foundational layer, while the value-added services built on top are the new frontier of differentiation.

 

The End of the Format Wars? Interoperability with Apache XTable

 

The most significant recent development in the lakehouse ecosystem is the emergence of tools that enable seamless interoperability between formats, effectively ending the “format wars” by allowing organizations to use them together.1

  • Apache XTable (incubating, formerly OneTable): This open-source project is a game-changer for lakehouse interoperability.4 It is crucial to understand that XTable is not a new table format. Instead, it is a metadata translator.4
  • Mechanism: XTable works by reading the native metadata of a table in a source format (e.g., Hudi’s timeline and file listings) and generating the equivalent metadata for one or more target formats (e.g., Delta Lake’s transaction log and Iceberg’s manifest files).20 This translation happens without copying or rewriting the underlying Parquet data files, which are largely compatible across the formats. The result is a single set of data files that can be read as a Hudi table, a Delta table, and an Iceberg table simultaneously.4
  • Implications: This capability is profoundly transformative. An organization can now choose a primary format that is best optimized for its write workload—for example, using Hudi for its superior CDC ingestion capabilities. Then, using XTable, it can generate Delta Lake metadata to allow data scientists to query the same data using optimized engines in Databricks, and also generate Iceberg metadata for business analysts to use high-concurrency SQL engines like Trino.4 This unlocks a “best-of-all-worlds” architecture, dissolving data silos and maximizing the utility of data across the entire organization. While still an incubating project with some limitations (e.g., limited support for MoR tables and Delta delete vectors), XTable represents the future of the open data lakehouse.4

 

Section 7: Use Case Suitability and Strategic Recommendations

 

Synthesizing the architectural, feature, and ecosystem analysis, this section provides actionable guidance for selecting the appropriate table format based on specific use cases and strategic priorities. The optimal choice depends on a clear understanding of an organization’s primary data workloads and long-term platform goals.

 

Streaming Ingestion and Change Data Capture (CDC): Apache Hudi

 

For workloads that involve high-volume, near-real-time data ingestion with frequent updates and deletes, Apache Hudi is the most capable and purpose-built solution.

  • Why Hudi is the Leader: Hudi’s architecture was designed from the ground up for incremental data processing.26 Its Merge-on-Read (MoR) table type is optimized for low-latency writes, allowing streaming jobs to append changes to log files quickly without the overhead of rewriting large columnar files.8 The efficiency of its upsert operations is dramatically enhanced by its multi-modal indexing subsystem, which allows Hudi to quickly locate the files containing records that need to be updated, a critical capability for CDC pipelines replicating changes from transactional databases.9 Furthermore, Hudi’s incremental query feature provides a native, efficient way to consume only the data that has changed since the last read, which is the exact requirement for building downstream streaming pipelines.26 The included DeltaStreamer utility is a robust, production-ready tool for ingesting data from sources like Apache Kafka or database change streams, further cementing its suitability for these use cases.29

 

Large-Scale Analytics and Open Ecosystems: Apache Iceberg

 

For organizations building a modern data warehouse or a large-scale analytical platform on the data lake, especially those prioritizing open standards and multi-engine flexibility, Apache Iceberg is the superior choice.

  • Why Iceberg Excels: Iceberg’s design prioritizes read performance, reliability, and long-term maintainability for analytical tables.6 Its hidden partitioning feature simplifies queries and improves performance by abstracting the physical data layout from analysts.17 Its powerful data skipping capability, which uses column-level statistics to prune files even on non-partitioned columns, can dramatically accelerate large analytical scans.52 Iceberg’s most compelling features for analytical use cases are its robust schema evolution and unique partition evolution, which ensure that tables can be safely and efficiently maintained over many years as data and query patterns change.19 Finally, its status as a true, engine-agnostic open standard with the broadest vendor support makes it the ideal foundation for building a flexible, future-proof data platform that avoids vendor lock-in.15

 

Unified Batch and Streaming in a Managed Ecosystem: Delta Lake

 

For organizations that are heavily invested in the Apache Spark ecosystem, and particularly for those leveraging the Databricks platform, Delta Lake offers the most seamless, integrated, and optimized experience.

  • Why Delta Lake is the Native Choice: Delta Lake’s tight integration with Spark provides a simple yet powerful platform for building reliable data pipelines that unify batch and streaming workloads.6 Its strong ACID guarantees, based on its simple transaction log architecture, are easy to reason about and ensure data integrity.13 Within the Databricks environment, Delta Lake benefits from numerous proprietary optimizations in the Delta Engine, such as advanced caching, Bloom filters, and auto-compaction, which enhance performance and simplify management.11 For teams that value a managed, end-to-end platform experience with strong governance features provided by tools like Unity Catalog, Delta Lake is the natural and most efficient choice.11

 

Decision Framework and Final Recommendations

 

The choice of a primary table format should be a deliberate one, guided by a clear assessment of priorities. The following matrix provides a framework for this decision.

Table 7.1: Strategic Decision Matrix for Open Table Format Selection

 

Decision Criterion Apache Hudi Delta Lake Apache Iceberg
Primary Workload Highly Recommended for Streaming, CDC, and incremental updates.[41, 68] Viable for batch analytics. Highly Recommended for unified batch and streaming ETL.[28] Highly Recommended for large-scale batch analytics and data warehousing.6 Increasingly strong for streaming.
Ecosystem & Engine Strong in Spark and Flink. Best for custom, service-oriented platforms.[5, 71] Highly Recommended for Databricks and Spark-centric environments.11 Highly Recommended for multi-engine environments (Trino, Flink, Snowflake, etc.).[15, 66]
Strategic Priority Best for write performance and advanced data management services.[3, 72] Best for a simplified, managed platform experience with strong performance optimizations.[12, 32] Best for avoiding vendor lock-in, ensuring long-term maintainability, and broad interoperability.[16, 18]
Key Feature Need Choose for advanced indexing, MoR/CoW flexibility, and incremental queries.[8, 9, 69] Choose for Z-Ordering, deep Spark integration, and managed features like auto-optimize.11 Choose for hidden partitioning, partition evolution, and robust, non-breaking schema evolution.[17, 19]

In conclusion, the modern data lakehouse is not a monolithic entity but a flexible platform built on open standards. The most effective strategy is not to declare a single winner but to choose a primary write format that aligns with the organization’s most critical workloads, using the framework above. Simultaneously, organizations should embrace the new paradigm of interoperability. By incorporating tools like Apache XTable, they can ensure that their foundational data asset remains open, accessible, and universally queryable by the best tool for every job, thereby future-proofing their data architecture and maximizing the value of their data.