Choosing an open table format is one of the first real architecture decisions in any lakehouse project, and it’s also one of the decisions teams most often get stuck debating. Delta Lake, Apache Iceberg, and Apache Hudi all solve the same core problem – adding ACID transactions, schema enforcement, and time travel to data sitting in cloud object storage – but they come from different origins, optimize for different things, and fit different environments better than others.
This post compares all three directly: where each came from, what each is genuinely best at, and how to actually decide between them for your specific situation, rather than defaulting to whichever one is most talked about. For the broader architectural context these formats fit into, our companion piece on data lakehouse architecture: a complete technical breakdown covers the full layer stack.
If you’re making this decision for your own architecture right now, our Data Lake Services team at Algoscale works through exactly this evaluation with clients regularly.
The Quick Answer
Delta Lake is the strongest choice if you’re building primarily on Databricks and want the deepest, most integrated experience. Apache Iceberg is the strongest choice if you need broad multi-engine flexibility across platforms like Snowflake, AWS, and Google Cloud, or want to minimize vendor lock-in. Apache Hudi is the strongest choice if your workload is dominated by high-frequency streaming ingestion or upsert-heavy change data capture patterns. All three now offer reasonably mature versions of the core capabilities – the differences that actually matter are in ecosystem fit and specific workload strengths, not raw feature completeness.
What All Three Have in Common
Before getting into differences, it’s worth being clear about the shared baseline. All three formats provide ACID transactions on data stored as files in cloud object storage, schema enforcement and evolution, time travel to query or roll back to previous table versions, and the ability to handle large-scale tables more efficiently than raw file-based querying. If you’re choosing between them, you’re not choosing between “has reliability” and “doesn’t” – you’re choosing which one fits your specific technical environment and workload best.
Delta Lake
Delta Lake originated at Databricks and remains most tightly integrated with the Databricks platform, though it’s open source and usable elsewhere. Its transaction log design – a sequential record of every change made to a table – is straightforward to reason about and integrates deeply with Spark, which is unsurprising given its origins. Delta Lake’s ecosystem strength is real: it has the most mature tooling specifically within Spark-based and Databricks-centric environments, strong support for Change Data Feed (tracking row-level changes over time), and increasingly broad adoption beyond Databricks itself through Delta Universal Format.
Apache Iceberg
Apache Iceberg originated at Netflix, built specifically to handle very large-scale tables with a strong emphasis on multi-engine interoperability from the start. Iceberg’s approach to partitioning – often called “hidden partitioning” – lets you change how a table is partitioned without rewriting the underlying data, which is a genuine advantage for tables whose query patterns evolve over time. Iceberg has also become the format with the broadest adoption across platforms that aren’t Databricks: Snowflake, AWS (Athena, Glue, EMR), and Google Cloud (BigLake) have all built native support for Iceberg tables, making it a strong choice for organizations that want to avoid being tied to a single compute platform.
Apache Hudi
Apache Hudi originated at Uber, built around the specific problem of handling high-frequency, upsert-heavy data – the kind of workload common in change data capture pipelines replicating from operational databases. Hudi’s merge-on-read and copy-on-write table types give teams explicit control over the tradeoff between write latency and read performance, which matters more in streaming-heavy contexts than in most batch-oriented ones. Hudi’s ecosystem is smaller than Delta Lake’s or Iceberg’s, but for genuinely streaming-first, CDC-heavy architectures, its incremental processing model is often a more natural fit than either alternative.
Key Technical Differences
Partitioning Approach
Delta Lake uses more traditional, explicit partitioning, similar to Hive-style conventions. Iceberg’s hidden partitioning lets the engine handle partition pruning automatically and allows partition schemes to evolve without a full table rewrite. Hudi supports both approaches depending on configuration, with particular strength in handling partition-level upserts efficiently.
Streaming and Incremental Processing
Hudi was built streaming-first and offers the most mature incremental processing model of the three, particularly for CDC-style upsert-heavy workloads. Delta Lake supports streaming well within Spark Structured Streaming specifically. Iceberg has added streaming support but is generally considered less optimized for extremely high-frequency upsert patterns than Hudi.
Catalog and Multi-Engine Support
Iceberg has the broadest native multi-engine support today, with genuine first-class adoption across Snowflake, AWS, and Google Cloud in addition to Spark-based engines. Delta Lake’s strongest integration remains within the Databricks and Spark ecosystem, though Delta Universal Format is closing this gap. Hudi supports multiple engines as well but has historically had a smaller ecosystem of native integrations compared to the other two.
Ecosystem and Vendor Backing
Delta Lake is primarily driven by Databricks, which means extremely fast feature development within that ecosystem but a perception of being “Databricks’ format” for some organizations. Iceberg is governed as an Apache Software Foundation project with contributions from many vendors, which has helped its broad, vendor-neutral adoption. Hudi is also an Apache project, with strong contributions from Uber, Amazon, and others, but a comparatively smaller contributor base than Iceberg.
Performance Characteristics
For typical batch analytical workloads, all three now perform comparably well when properly configured and maintained. The meaningful performance differences show up at the edges: Iceberg’s metadata design tends to scale particularly well for very large numbers of files and partitions; Hudi’s merge-on-read tables offer faster writes at some cost to read performance until compaction runs; Delta Lake’s Photon-accelerated queries (within Databricks specifically) can outperform the others in that specific environment.
Choosing the Right Format for Your Situation
If your organization is already committed to Databricks and most of your workloads run through Spark, Delta Lake’s deep integration makes it the path of least resistance, and increasingly the path of most feature velocity. If you need to support multiple compute engines – perhaps Snowflake for BI and Spark for data engineering, or a multi-cloud strategy – Iceberg’s broad ecosystem support makes it the safer long-term choice. If your architecture is dominated by streaming ingestion or frequent upserts from operational database replication, Hudi’s purpose-built incremental processing model is worth serious consideration even if your broader ecosystem leans toward one of the others for less demanding tables.
Our comparison of Snowflake vs. Databricks for enterprise analytics is a useful companion read if the table format decision is tied to a broader platform choice, since the two decisions often influence each other.
Can You Switch Formats Later?
This is worth addressing directly, since it affects how much weight to put on getting the decision exactly right the first time. Interoperability between formats has improved significantly – Delta Universal Format allows Delta tables to be read by Iceberg-compatible engines, and cross-format projects like Apache XTable aim to let the same underlying data be represented in multiple table formats simultaneously. This doesn’t mean the choice doesn’t matter, but it does mean an initial decision isn’t as irreversible as it might have been a couple of years ago. Prioritizing what fits your current, known workloads well is more valuable than trying to future-proof against every possible format migration.
Common Mistakes When Choosing a Format
Choosing based on hype rather than ecosystem fit. The format with the most conference buzz in a given year isn’t necessarily the one that fits your actual compute platform and workload best.
Underestimating the operational side. All three formats require ongoing table maintenance – compaction, cleanup of old snapshots, metadata management. Choosing a format without planning for this maintenance leads to the same performance problems a plain data lake has, just with extra steps.
Assuming the decision is permanent and irreversible. While it’s worth choosing carefully, growing interoperability between formats means this decision carries somewhat less long-term risk than it used to.
For a broader look at how a well-chosen table format supports consistent, trustworthy reporting across an organization, see our post on creating a single source of truth using data lakehouse architecture.
Making This Decision for Your Architecture
Choosing between Delta Lake, Iceberg, and Hudi is a decision worth making deliberately, based on your actual platform commitments and workload patterns rather than general industry momentum. At Algoscale, our Data Lake Services team has implemented all three across different client environments and helps organizations make this call based on what will actually work well for their specific situation.
To see the broader range of data engineering and analytics work we do beyond table format selection specifically, take a look at what Algoscale builds across the data stack.
Why Algoscale
A few things shape how we actually deliver on data lake and data engineering work, beyond the architecture and practices covered above:
● Pre-built accelerators. We don’t start every engagement from a blank slate – proprietary accelerators built from prior implementations speed up common data source integration and analytics patterns.
● Faster time to value. For a focused initial scope covering core data sources and first analytics use cases, our accelerators typically compress development timelines to around four weeks, rather than the several months a from-scratch build often takes.
● Built on a scalable framework. Our implementation approach follows a repeatable, scale-ready framework, so the architecture built for your first use case extends cleanly as data volume and teams grow, rather than requiring a redo.
● Microsoft Solution Partner for Data & AI. Algoscale holds Microsoft Solution Partner status for Data & AI, including specific expertise implementing Microsoft Fabric as a modern data warehouse.
Frequently Asked Questions
1. Is one of these three formats objectively better than the others?
No – each was designed with different priorities, and each is genuinely strong in the context it was built for. Delta Lake excels within Databricks and Spark, Iceberg excels at multi-engine flexibility, and Hudi excels at streaming and upsert-heavy workloads. “Best” depends entirely on your specific environment.
2. Can I use more than one table format in the same organization?
Yes, and this is increasingly common, particularly for organizations with varied workloads or multiple compute platforms. Using Iceberg for broadly-shared analytical tables while using Hudi for a specific high-frequency CDC pipeline is a reasonable pattern, though it does add some operational complexity to manage multiple formats.
3. Does choosing Iceberg mean I can’t use Databricks?
No. Databricks supports reading and writing Iceberg tables, even though Delta Lake remains its most deeply integrated native format. You can use Iceberg on Databricks; you just won’t get every Databricks-specific optimization that’s built specifically around Delta Lake.
4. How much does table maintenance differ between the three formats?
All three require regular maintenance – compacting small files, cleaning up old snapshots or versions, and managing metadata growth. The specific commands and automation tooling differ, but none of the three is maintenance-free, and skipping this maintenance causes similar performance problems regardless of which format you chose.
5. Is Delta Lake only usable if I’m on Databricks?
No, Delta Lake is open source and can be read and written by other engines including Apache Spark independently, Trino, and increasingly others through Delta Universal Format. Databricks offers the deepest, most optimized experience, but it’s not a strict requirement.
6. How do I know which format fits a streaming-heavy workload specifically?
If your primary need is handling frequent, high-volume upserts – like continuous change data capture from an operational database – Hudi’s purpose-built merge-on-read and incremental processing model is worth prioritizing in evaluation, even if the rest of your architecture leans toward Delta Lake or Iceberg for other tables.
