What are the best open format data storage solutions?

Summary

• Open table formats like Delta Lake, Apache Iceberg, and Apache Hudi add ACID transactions, schema evolution, and time travel on top of columnar file formats such as Parquet, enabling portable lakehouse architectures.
• Organizations should evaluate open table formats based on engine compatibility, workload type, governance integration, community momentum, and migration complexity rather than defaulting to a single vendor.
• Databricks treats Delta Lake, Iceberg, and Parquet as first-class citizens, unifying governance through Unity Catalog and delivering warehouse-grade performance with AI-powered optimizations like Photon and Predictive IO.

Best open format data storage solutions for modern data architectures

Organizations building modern data platforms face a choice: store data in proprietary formats tied to a single vendor, or adopt open formats that keep data portable and interoperable. The format you choose determines which engines can query your data, how efficiently pipelines run, and how easily your schema evolves. As organizations move away from traditional data lakes toward more advanced architectures, selecting the right open format becomes a strategic decision.
This guide breaks down leading open format storage solutions and how to evaluate them.

What are open file formats and open table formats?

Open file formats and open table formats serve different but complementary roles in a data architecture.
Open file formats like Apache Parquet and ORC define how data is physically stored on disk. Parquet organizes data in columns rather than rows, improving analytical query performance and reducing storage costs compared to row-based formats like CSV or JSON.
Open table formats add a metadata layer on top of these files, bringing database-like features to data lakes. A table format typically supports:

• ACID transactions for reliable reads and writes
• Schema evolution to safely change table structure over time
• Time travel for querying historical snapshots
• Metadata management for efficient file tracking and partition pruning

The three leading open table formats are:

Format	Origin	Primary strength
Delta Lake	Linux Foundation	Unified batch and streaming with ACID transactions
Apache Iceberg	Netflix → Apache Software Foundation	Engine-independent specification with rich metadata
Apache Hudi	Uber → Apache Software Foundation	Row-level upserts and near real-time incremental ingestion

All three build on Apache Parquet data files, so organizations adopting any of them retain a shared open foundation.

Key factors for choosing an open table format

Selecting a table format is a long-term architectural decision. Evaluate these criteria independently of any vendor:

1. Engine compatibility, Which query engines and processing frameworks need to read and write the data?
2. Workload type, Batch-heavy analytics, streaming ingestion, or frequent upserts and deletes?
3. Governance integration, Does your catalog or governance layer support the format natively?
4. Community and ecosystem, How active is development, and which cloud providers offer native support?
5. Migration complexity, How much existing data needs conversion, and what tooling is available?

Many organizations adopt more than one format. A platform that governs multiple formats under a single catalog reduces the pressure to standardize on one.

Why open table formats are going mainstream

Major cloud providers and data platforms are standardizing on Iceberg and other open formats, signaling an industry-wide shift toward lakehouse-style architectures. In the 2025 Hype Cycle for Data Management, Gartner rated open table formats such as Apache Iceberg as having a "Transformational" benefit, the highest impact rating the firm assigns, underscoring the strategic weight these formats now carry.
Open formats prevent vendor lock-in by storing data in community-governed specifications any engine can access. Organizations retain ownership of their data across clouds and services, reducing migration risk.

Performance benefits of open file formats

Columnar formats like Parquet and ORC outperform row-based alternatives for analytical workloads. Column pruning reads only relevant columns, and predicate pushdown skips irrelevant row groups entirely. These optimizations cut I/O and accelerate queries without additional infrastructure.

How Databricks supports open formats

On the Databricks Platform, open formats, Delta Lake, Apache Iceberg™, and Parquet, are first-class citizens, not bolt-ons. Unity Catalog provides one catalog for all data, managing these formats with a single set of permissions, lineage, and business definitions that flow into every tool.
AI-powered optimizations like Photon, Predictive IO, and Intelligent Workload Management deliver warehouse-grade performance without the trade-offs of proprietary systems. With governance, semantics, and analytics unified on a lakehouse, the platform gains AI that learns the meaning, context, and usage of data, keeping metrics consistent and powering AI agents with trusted answers.

Best practices for migrating to open formats

• Inventory existing assets and map them to open format equivalents like Parquet, Delta Lake, or Iceberg.
• Pilot with a non-critical workload to validate performance and tooling compatibility.
• Avoid new proprietary dependencies during migration, plan regular exports and format checks.
• Establish governance early so permissions and lineage carry over to the new format.
• Benchmark before and after to confirm query speed and storage efficiency gains.

FAQs

What are open data formats and why do they matter?

Open data formats are non-proprietary specifications any tool can read and write. They keep data portable, prevent lock-in, and enable lakehouse architecture.

How does Apache Parquet work for columnar storage?

Parquet organizes data by columns, enabling column pruning and predicate pushdown. This produces faster analytical queries and lower storage costs than row-based formats.

What are the advantages of Delta Lake?

Delta Lake unifies batch and streaming with ACID transactions and supports structured, semi-structured, and unstructured data. On the Databricks Platform, Unity Catalog adds lineage and permissions.

How does Apache Iceberg handle table management?

Iceberg uses an open specification for metadata, manifests, and file tracking. It adds ACID transactions, time travel, and safe schema evolution independent of any compute engine.

What are key features of Apache Hudi?

Hudi provides row-level upserts and deletes, near real-time incremental ingestion, multimodal indexing, and time travel for historical analysis.

What should organizations consider when choosing an open table format?

Evaluate engine compatibility, workload type, governance integration, community momentum, and migration complexity. Many teams adopt more than one format behind a unified catalog.

How do open formats prevent vendor lock-in?

They store data in portable, community-governed specifications. Any compatible engine can access the data, reducing risk from vendor changes or failures.

What are the performance benefits of open file formats over proprietary formats?

Columnar formats like Parquet and ORC enable column pruning and predicate pushdown, cutting I/O significantly. These optimizations deliver faster queries and lower storage costs without proprietary infrastructure.

How do open formats support ACID transactions and schema evolution?

Delta Lake and Apache Iceberg both maintain transaction logs that guarantee atomicity, consistency, isolation, and durability. Schema changes are tracked in metadata so readers and writers stay compatible.

What are best practices for migrating to open formats?

Start by inventorying existing data assets and piloting with a non-critical workload. Establish governance early, avoid adding proprietary dependencies, and benchmark results to confirm performance gains.
Explore how the Data Lakehouse unifies your data on open formats with built-in governance, performance, and AI-ready analytics.