Apache Iceberg, Delta Lake, or Hudi? Understanding Modern Lakehouse Technologies

The modern data lakehouse would not exist without open table formats. Before Apache Iceberg, Delta Lake, and Apache Hudi arrived, data lakes were large, cheap, and largely ungovernable. You could store anything, but querying it reliably, updating individual records, or maintaining consistent views across concurrent reads and writes required workarounds that undermined the efficiency the data lake was supposed to deliver. Open table formats changed this equation by layering ACID transaction semantics, schema evolution, and time-travel capabilities directly on top of open file formats like Parquet and ORC stored in cloud object storage, effectively turning a cloud data lake into a production-grade lakehouse architecture.

Choosing between Apache Iceberg, Delta Lake, and Hudi is one of the most consequential technical decisions a data engineering team makes when building a data lakehouse. Their feature sets have converged significantly over the past several years, which makes the comparison more nuanced than it was even two years ago. This guide examines where each format excels, where trade-offs exist, and how to match your workload requirements to the right technology choice, whether you are running an AWS data lakehouse, an Azure data lakehouse, or a multi-cloud environment.

What Open Table Formats Actually Do and Why They Matter

An open table format sits between your storage layer, typically Amazon S3, Azure Data Lake Storage, or Google Cloud Storage, and your compute engines such as Apache Spark, Presto, Trino, or Flink. It maintains a metadata layer that tracks which data files belong to a table, what schema those files conform to, and what the state of the table was at any historical point in time. This metadata layer is what enables ACID transactions on object storage, where native transactional support does not exist.

When you update a record or delete a row, the format does not modify the underlying Parquet file. Instead, it writes a new version of the file and updates the metadata to point to the new version, retiring the old one. This copy-on-write or merge-on-read pattern provides the atomicity and isolation that data lake pipelines previously lacked. Time travel, schema evolution, and partition evolution all flow from the same metadata architecture. Understanding this shared foundation helps clarify where the three formats actually differ and why those differences matter for specific workloads.

Apache Iceberg: Designed for Scale and Engine Interoperability

Apache Iceberg was originally developed at Netflix to solve the problem of managing petabyte-scale tables with billions of files. Its design philosophy prioritizes correctness, scalability, and engine interoperability above all else. Iceberg stores its metadata as a tree of manifest files that track individual data files along with their column-level statistics. This structure allows query engines to prune partitions and individual files very efficiently without scanning the entire table, which is critical for large-scale analytical workloads running against hundreds of terabytes of data.

Iceberg’s breadth of engine support is its most strategically important characteristic. Apache Spark, Trino, Presto, Flink, Dremio, Snowflake, StarRocks, and Apache Hive all read and write Iceberg natively. On an AWS data lakehouse, Amazon Athena v3, AWS Glue, and Amazon EMR support Iceberg without additional configuration. On an Azure data lakehouse, Azure Synapse Spark pools query Iceberg tables through the open catalog API. This multi-engine compatibility makes Iceberg the natural choice for organizations running heterogeneous compute environments where different teams use different query tools against the same datasets. Iceberg also supports hidden partitioning, where partition layout is abstracted from query writers and can be changed over time without rewriting any data, eliminating one of the most painful migration headaches in traditional Hive-partitioned tables.

Delta Lake: Deep Spark Integration with a Growing Open Ecosystem

Delta Lake was created by Databricks and open-sourced in 2019. It has the deepest integration with Apache Spark and the Databricks Lakehouse Platform, making it the default choice for organizations already running Databricks workloads. The Delta Log, a JSON-based transaction log, records every operation on a table and enables ACID compliance, time travel through version and timestamp queries, and a full audit history of all write operations. Delta Lake also introduced Optimized Writes and Auto Optimize, which compact small files automatically, a persistent pain point in cloud data lake environments with high-frequency ingestion.

Delta Live Tables, a Databricks-native pipeline framework built on Delta Lake, simplifies building reliable streaming and batch pipelines with built-in data quality expectations and automatic dependency management. Z-Ordering allows data to be co-located by multiple columns simultaneously, improving query performance for filtering on non-partition columns significantly. For organizations where Databricks is their primary data engineering and machine learning platform, Delta Lake offers the most seamless and feature-rich experience available within that ecosystem, with the closest integration between data storage, pipeline orchestration, and ML experiment tracking through MLflow.

Apache Hudi: Purpose-Built for High-Frequency Upserts and CDC

Apache Hudi was developed at Uber to solve the problem of efficiently updating and deleting individual records in a large data lake table. Before Hudi, the only way to update a record in a Parquet-based data lake was to rewrite the entire partition containing that record, which made changing data capture from operational databases extremely expensive at scale. Hudi’s merge-on-read table type maintains a base Parquet file layer supplemented by delta log files that record recent changes. This allows record-level upserts to complete very quickly because only the delta log needs to be written, while reads merge the base and delta layers on the fly.

For organizations running high-frequency CDC ingestion from transactional databases like PostgreSQL, MySQL, Oracle, or SQL Server, Hudi is often the most efficient option. The DeltaStreamer utility combined with Apache Flink provides a battle-tested pipeline for ingesting Debezium CDC events from Kafka directly into Hudi tables with exactly-once semantics. The Hudi Metadata Table eliminates the need to list millions of files in object storage during query planning by maintaining a cached index of the table’s file layout, which significantly reduces query latency on large tables and is a feature that addresses one of the most common performance complaints about large data lake deployments.

Table 1: Feature Comparison — Apache Iceberg vs. Delta Lake vs. Apache Hudi

Performance Characteristics and Query Optimization

Performance comparisons between these three formats depend heavily on the specific workload. For large-scale analytical reads with complex filtering on non-partition columns, Iceberg’s manifest-based file pruning and column statistics deliver the most efficient query plans in most benchmarks run against Trino or Presto. For environments running Databricks, Delta Lake’s Z-Ordering combined with the Databricks query optimizer frequently delivers excellent mixed-workload performance. For high-throughput upsert workloads where write latency is the bottleneck and reads can tolerate the merge-on-read overhead, Hudi’s architecture is typically the most efficient choice.

Small file compaction is a concern across all three formats and must be part of your operational runbook regardless of which format you choose. Cloud data lake environments that ingest data frequently accumulate large numbers of small Parquet files, which degrades query performance by forcing the query engine to open and close thousands of individual files during a single scan. All three formats offer compaction mechanisms, but they differ in how much automation they provide and what trade-offs they accept between compaction resource usage and query performance during the compaction window. 

Table 2: Workload Suitability and Platform Ecosystem Fit

Making the Choice That Fits Your Organization

There is no universally correct answer to the Apache Iceberg versus Delta Lake versus Hudi question. The right choice depends on the query engines your organization runs, the cloud platforms you operate on, the nature of your data ingestion patterns, and the depth of your existing vendor relationships. Organizations running Databricks as their primary platform should default to Delta Lake and evaluate Iceberg only if they need significant cross-engine interoperability. Organizations building an AWS data lakehouse or Azure data lakehouse with multiple query tools should strongly consider Iceberg for its engine-agnostic design and broadening native support. Organizations with high-frequency CDC workloads from transactional databases should evaluate Hudi’s merge-on-read architecture before assuming another format is sufficient for their write volume.

It is increasingly common for mature organizations to run multiple formats simultaneously. Many large enterprises use Iceberg for their core analytical tables and Hudi for their CDC pipelines, and both coexist comfortably in the same lakehouse architecture through a shared catalog service such as AWS Glue, Apache Nessie, or Unity Catalog. The proliferation of catalog services that support multiple formats makes managing a heterogeneous table format environment significantly more tractable than it was even two years ago, reducing the pressure to standardize on a single format across every use case at the cost of suboptimal performance.

Why Choose AlgoScale for Modern Data Lakehouse Implementation

Selecting the right open table format is a critical step in building a scalable and future-ready data lakehouse. The decision goes beyond comparing features it requires evaluating your data ingestion patterns, analytics workloads, cloud ecosystem, governance requirements, and long-term scalability goals. Whether your organization benefits most from Apache Iceberg, Delta Lake, Apache Hudi, or a combination of these technologies, the right implementation strategy is essential for maximizing performance and operational efficiency.

At AlgoScale, we help enterprises design and implement modern data lakehouse architectures that leverage the strengths of leading open table formats. Our data engineering experts assess your existing environment, workload characteristics, and business objectives to recommend the most suitable technology stack for your organization. From architecting cloud-native data platforms and optimizing streaming data pipelines to implementing governance frameworks and performance tuning, we deliver solutions that support analytics, AI, and real-time data processing at enterprise scale.

With extensive expertise across AWS, Azure, Apache Iceberg, Delta Lake, Apache Hudi, and modern data engineering frameworks, AlgoScale enables organizations to build secure, scalable, and interoperable lakehouse platforms that evolve with changing business demands. Connect with AlgoScale to build a high-performance data lakehouse powered by the right open table technologies and designed for long-term innovation.