How Open Table Formats (Delta, Iceberg, Hudi) Are Changing the Data Game

Modern enterprises are generating more data than ever before. From customer interactions and IoT sensors to real-time transactions and internal logs, data is growing not only in volume but also in velocity and variety. Traditionally, organizations have relied on data warehouses or basic cloud data lakes to store and analyze this information. However, both approaches have their limitations. Warehouses are expensive and inflexible, while conventional data lakes lack the structure and reliability needed for enterprise-grade analytics.

This is where open table formats come in. Technologies like Delta Lake, Apache Iceberg, and Apache Hudi are transforming cloud storage from passive data repositories into active, transactional data platforms. These formats bring features like ACID compliance, time travel, and schema evolution to cloud-native architectures — unlocking faster, safer, and more reliable data analytics.

In this blog, we’ll explore what these open table formats are, how they differ, and why they’re quickly becoming essential components of modern data infrastructure.

The problem with traditional data lake architecture

While cloud object storage like Amazon S3 and Azure Data Lake is cheap and scalable, it’s not designed for structured, reliable analytics. Traditional data lakes that store files in formats like Parquet or ORC offer flexibility, but fall short in several key areas:

• No support for ACID transactions – concurrent reads and writes can lead to data corruption

• No versioning or time travel – analysts can’t query past states of data or roll back mistakes

• Weak support for schema evolution – small changes to data structure can break entire pipelines

• Poor performance at scale – query engines often have to scan thousands of files, increasing compute costs

• Complex ETL and data reliability challenges – updates and deletes are clunky and hard to orchestrate

These limitations make traditional data lakes difficult to trust for mission-critical reporting, real-time analytics, and machine learning. As data grows more central to business operations, teams need a better foundation.

Enter open table formats – what are they?

Open table formats (OTFs) are a new layer that sits between raw data files and analytics engines. They bring database-like capabilities to cloud object storage, enabling reliable, fast, and structured access to massive datasets.

Unlike plain Parquet or ORC files, open table formats manage metadata, transaction logs, and schema evolution natively. This allows for:

• ACID transactions across large, distributed datasets

• Version control and time travel to analyze historical states

• Efficient updates, deletes, and merges without rebuilding datasets

• Seamless integration with multiple query engines (e.g. Spark, Trino, Flink)

• Better governance and auditing through metadata management

Think of open table formats as giving your data lake a brain — they make it intelligent, consistent, and operationally robust.

Delta Lake, Apache Iceberg, and Apache Hudi – a comparative overview

There are three major open table formats dominating the modern data ecosystem: Delta Lake, Apache Iceberg, and Apache Hudi. Each has a unique approach and is suited for specific use cases.

Delta Lake

Delta Lake was developed by Databricks and is tightly integrated with Apache Spark. It introduces a transaction log called the delta log, which records every change to the data over time. This enables features like ACID transactions, time travel, schema enforcement, and upserts.

Delta Lake is widely adopted in environments that rely on Databricks, Spark Streaming, and machine learning pipelines. It’s particularly effective for combining batch and streaming data into a unified architecture.

Apache Iceberg

Iceberg was originally built by Netflix and is now a top-level Apache project. It takes a table-first approach with robust metadata layers that support snapshot isolation, schema evolution, and hidden partitioning.

One of Iceberg’s key strengths is its engine-agnostic design — it integrates well with Spark, Flink, Trino, Hive, and Dremio. It’s ideal for environments where large datasets need to be queried and updated across multiple processing engines.

Apache Hudi

Hudi (Hadoop Upserts Deletes and Incrementals) was developed at Uber to enable near real-time ingestion and updates. It supports two storage modes: Copy-on-Write for read-optimized workloads and Merge-on-Read for write-heavy pipelines.

Hudi excels at handling frequent data changes and supports features like incremental queries, change data capture (CDC), and rollback. It’s best suited for use cases involving transactional systems, such as fraud detection, logistics, or user activity tracking.

Use cases and when to use which

Choosing the right open table format depends heavily on your data workloads, existing infrastructure, and latency requirements. Here’s a quick overview of when each one shines:

• Delta Lake is ideal if you are working within the Databricks ecosystem or heavily use Spark for both batch and streaming. It offers a seamless experience for machine learning and BI workloads where data consistency is critical.

• Apache Iceberg is your go-to option when dealing with massive datasets and complex schema changes. It supports hidden partitioning and is more open to multiple engines, making it a good choice for decentralized, cloud-native architectures.

• Apache Hudi is perfect for real-time ingestion and incremental processing. If your data changes frequently or you need CDC-style workflows, Hudi’s merge-on-read strategy can dramatically simplify pipeline complexity.

Here’s a quick feature comparison:

No single format is perfect for every situation. Many enterprises adopt a hybrid approach based on department needs, cloud platforms, and analytical tools. What’s clear is that open table formats are no longer just emerging trends — they are foundational to the modern data stack.

Strategic impact on modern data architecture

The rise of open table formats is reshaping how organizations build their data platforms. Instead of maintaining separate systems for raw data lakes and governed warehouses, teams can now create lakehouses — unified architectures that support both analytical and operational workloads.

By enabling ACID transactions, updates, and rich metadata on top of cloud storage, formats like Delta, Iceberg, and Hudi make it possible to:

• Run high-performance BI tools directly on the data lake

• Power real-time dashboards without complex replication

• Enable consistent data sharing across teams and engines

• Reduce reliance on expensive proprietary data warehouses

• Build governed, scalable, multi-tenant data platforms

This architectural flexibility supports advanced use cases such as machine learning pipelines, real-time monitoring, data product creation, and data mesh implementation, without constantly moving or duplicating data.

More importantly, it empowers data teams to deliver value faster — by simplifying operations, improving trust in data, and making analytics infrastructure more adaptable.

Adoption challenges and considerations

Despite their benefits, open table formats are not plug-and-play solutions. Adopting them requires thoughtful planning and alignment across teams. Here are a few challenges to keep in mind:

• Ecosystem compatibility – Not all tools support all formats equally. You may need to wait or invest in custom connectors to achieve full interoperability.

• Operational complexity – Managing transaction logs, compactions, and metadata tables introduces new operational overhead.

• Learning curve – Data engineers must become familiar with each format’s terminology, behavior, and tuning strategies.

• Storage trade-offs – Some formats like Hudi’s merge-on-read offer performance at the cost of higher storage or compute.

• Security and governance – Features like row-level access control or column-level lineage are still evolving and may not match warehouse capabilities.

It’s crucial to evaluate not only technical fit but also team readiness, cloud provider support, and roadmap alignment before choosing a format.

Real-world examples

Across industries, companies are already leveraging open table formats to modernize their data stacks. Here are a few anonymized examples:

• A telecom provider migrated their legacy EDW workloads to Apache Iceberg, enabling multiple teams to query the same data using Spark, Trino, and Flink — with reduced duplication and improved freshness.

• A retail chain adopted Apache Hudi for near real-time inventory analytics, ingesting tens of millions of records daily and supporting microservices that adjust promotions and stock levels on the fly.

• A fintech company built a Delta Lake-based architecture for customer behavior analytics and fraud detection, using time travel to recreate sessions and backtest algorithms.

These organizations didn’t just gain technical improvements — they saw faster insight generation, lower infrastructure costs, and improved collaboration between data teams.

Future trends

As adoption of open table formats grows, several trends are shaping the future:

• Convergence around Iceberg – Many vendors, including Snowflake, AWS, and Google, are aligning more closely with Apache Iceberg due to its neutrality and broad compatibility.

• Delta Lake going fully open – Databricks has open-sourced the Delta Lake protocol, signaling greater community involvement and interoperability.

• Catalog integration – Services like AWS Glue, Apache Hive Metastore, Project Nessie, and Unity Catalog are evolving to better manage table versions, governance, and access control.

• Cross-cloud querying – With open formats, querying the same data across different cloud environments or tools becomes more feasible — supporting hybrid and multi-cloud strategies.

• Deeper integration with ML and AI pipelines – Expect open table formats to become the default for managing labeled datasets, model training logs, and real-time inference outputs.

The pace of innovation in this space is rapid — and it’s increasingly clear that open, modular, and cloud-native data architectures are the way forward.

Conclusion

Open table formats like Delta Lake, Apache Iceberg, and Apache Hudi are changing the data game by solving fundamental limitations of traditional data lakes. They bring the best of data warehouses — consistency, reliability, and query performance — into the world of low-cost, scalable cloud storage.

Whether you’re building a modern BI platform, enabling real-time analytics, or laying the foundation for data mesh or lakehouse, choosing the right table format can unlock speed, agility, and value for your data teams.

These formats are not just technical upgrades — they’re architectural enablers that help data-driven organizations move faster and smarter.

At Datahub Analytics, we help organizations modernize their data platforms using best-in-class open technologies like Delta, Iceberg, and Hudi. Whether you’re exploring a new lakehouse strategy or looking to improve data reliability and performance, our team can help.

Get in touch today for a tailored assessment or proof-of-concept that aligns with your business goals.