“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard
Open Table Formats: Unlocking the Future of Data Architecture
In the ever-evolving world of data engineering and analytics, one innovation quietly reshaping modern architectures is the rise of open table formats. Behind the buzzwords and the technical jargon lies a transformative approach to managing massive datasets in scalable, flexible, and vendor-neutral ways. To truly appreciate the revolution these formats represent, we must start with their historical context.
Historical Context: From Data Silos to Open Tables
The early 2000s saw a proliferation of proprietary data warehouses — think Teradata, Oracle, and early Hadoop ecosystems. Data was often siloed within organizations, and even more troubling, siloed within the technical constraints of specific tools. Moving or integrating data across systems was expensive, brittle, and complex.
The rise of cloud data storage (Amazon S3, Azure Blob Storage, Google Cloud Storage) decoupled compute from storage. Data lake architectures promised cheap, scalable storage, but a critical problem emerged: data lakes turned into data swamps without robust metadata management, schema evolution, transaction guarantees, or interoperability.
Enter open table formats — a new class of technologies designed to bring order, reliability, and interoperability to data lakes.
What Are Open Table Formats?
Open table formats are specifications and protocols that define how data files, metadata, and schemas are organized on disk — independent of any particular compute engine. They enable ACID transactions, schema evolution, partition management, versioning, and time travel across diverse compute environments.
Some of the most prominent open table formats include:
- Apache Iceberg
- Delta Lake (originally from Databricks)
- Apache Hudi
- Table formats emerging from projects like Nessie and Project Nessie
They all aim to answer the same fundamental question: How can large-scale data be safely and efficiently shared across different tools and environments?
Thought Leaders in Open Table Formats
Several individuals and organizations have pioneered the open table movement:
- Ryan Blue and Dan Weeks (formerly Netflix, now Tabular) — architects of Apache Iceberg.
- Burak Yavuz, Michael Armbrust, and Matei Zaharia — core contributors to Delta Lake at Databricks.
- Sundararajan “Sunda” Sekar — driving innovation at Apache Hudi.
- Organizations like Netflix, Uber, and Databricks have also significantly contributed to adoption and technical maturity.
Architectures That Benefit from Open Table Formats
Modern data architectures leverage open table formats in several ways:
- Data Lakes and Lakehouses: They provide the ACID guarantees missing from traditional data lakes.
- Multi-engine Interoperability: Use Spark for heavy batch processing, Presto/Trino for ad-hoc querying, Flink for streaming — all on the same underlying data.
- Data Mesh Architectures: Enable decentralized ownership and federated governance across data domains.
- Streaming and Batch Unification: Open formats support both streaming ingestion and batch analytics seamlessly.
Deep Technical Dive: Under the Hood
- Metadata Management
- Iceberg maintains a metadata tree with snapshots, manifests, and manifest lists.
- Delta Lake stores transaction logs in a _delta_log folder.
- Hudi separates between Copy-on-Write and Merge-on-Read tables for different latency and freshness needs.
- Schema Evolution and Enforcement
- All major formats allow adding, deleting, and renaming columns without rewriting entire datasets.
- Schema validation ensures readers and writers agree on expectations.
- Partitioning and File Pruning
- Partition evolution without rewrites (Iceberg) is a huge advancement.
- Predicate pushdown and metadata pruning enable high performance reads.
- ACID Transactions
- Multi-operation commits are handled via atomic writes and versioned snapshots.
- Rollbacks and time travel allow restoring datasets to consistent previous states.
- Time Travel and Versioning
- Queries can specify “as of” timestamps or version numbers.
- Great for debugging, audits, and reprocessing historical datasets.
Tools and Tool chains that Support Open Table Formats
- Compute Engines: Apache Spark, Trino, Presto, Apache Flink, Dremio, Snowflake (Iceberg support), Starburst.
- Catalog Services: AWS Glue Catalog, Apache Hive Metastore, Project Nessie, Unity Catalog (Databricks).
- Data Orchestration: Apache Airflow, Dagster, Prefect.
- ETL/ELT Platforms: dbt (data build tool), Fivetran (through adapters), Matillion.
- Data Observability: Monte Carlo, Databand, OpenLineage integrations.
- Storage Backends: Amazon S3, Azure Data Lake Storage, Google Cloud Storage, Hadoop Distributed File System (HDFS).
These tools allow organizations to manage, query, govern, and process open table format data across multiple environments without vendor lock-in.
Patterns for Using Open Table Formats
Pattern 1: Bronze-Silver-Gold Layers
- Bronze: Raw ingestion
Silver: Cleansed, conformed data - Gold: Curated, business-ready datasets
- Each layer benefits from open table features like schema evolution and transactional writes.
Pattern 2: Incremental Processing
- CDC (Change Data Capture) streams can be ingested and managed efficiently.
- Hudi and Iceberg have native support for upserts and incremental views.
Pattern 3: Multi-modal Access
- Data Scientists query via notebooks (Spark, Pandas-on-Arrow).
- Business Analysts explore data via BI tools (Tableau, PowerBI).
- Data Engineers run transformations with distributed compute engines.
Pattern 4: Unified Streaming and Batch
- Streaming engines ingest and compact data in near-real-time.
- Batch engines consume the same tables for large aggregations.
Business Cases for Open Table Formats
1. Cost Optimization
- Separation of storage and compute drives down cloud costs.
- Avoids vendor lock-in; move between engines without rewriting data.
2. Operational Simplicity
- One set of data with consistent access and transaction management.
- Better governance and compliance via audit trails.
3. Innovation Enablement
- Data products can be built faster, reused across teams, and evolved safely.
- Supports data mesh strategies with federated ownership.
4. Future Proofing
- As new engines and tools emerge (e.g., AI/ML-specific engines, real-time graph query engines), open table formats provide a universal interoperability layer.
When Open Table Formats Are Done Well
Netflix with Iceberg:
- Enabled massive scalability.
- Supported streaming and batch together.
- Open sourced back to the community.
Databricks with Delta Lake:
- Powered massive customer bases (Shell, HSBC, Comcast).
- Integrated seamlessly into ML pipelines and BI workflows.
Uber with Hudi:
- Solved ingestion at scale for transactional ride data.
- Achieved low-latency updates and incremental analytics.
When Open Table Formats Are Done Poorly
Anti-pattern 1: Monolithic Metadata Repositories
- Trying to centralize everything without scalability considerations leads to slow queries.
Anti-pattern 2: Misusing Partitioning
- Over-partitioning kills query performance due to file system overhead.
- Under-partitioning leads to huge file scans.
Anti-pattern 3: Over-reliance on Single Engine Features
- Using Delta Lake-specific optimizations in a way that prevents migration to Iceberg or Hudi defeats the “open” spirit.
Anti-pattern 4: Lack of Governance
- Without cataloging and access control, open table formats can still devolve into messy “swamps” even with ACID support.
How to Get Started with Open Table Formats
Step 1: Choose Your Open Format
- Iceberg if you prioritize schema evolution and multi-engine interoperability.
- Delta Lake if you already use Databricks heavily.
- Hudi if you need frequent upserts and streaming ingest at scale.
Step 2: Set Up Storage
- Use cloud object storage (S3, GCS, ADLS) or HDFS.
Step 3: Deploy a Catalog Service
- Glue, Hive Metastore, or Project Nessie.
Step 4: Select a Processing Engine
- Spark, Flink, Trino, or Presto.
Step 5: Build Ingestion Pipelines
- Write pipelines to land raw, incremental, and transformed data into open tables.
Step 6: Enable Observability
- Track data quality, lineage, and freshness with observability tools.
Step 7: Iterate and Govern
- Implement robust governance practices — schema management, access control, data documentation.
Wrapping up…
In a world where data fuels AI, analytics, and digital transformation, open table formats are becoming the backbone of modern data architectures. They represent not just a technical innovation, but a philosophical shift toward openness, interoperability, and long-term sustainability.
Organizations that embrace open table formats wisely — combining technical rigor with business strategy — will be poised to unlock massive value from their data assets.
The table is open; it’s time to sit down and build.
