For decades, enterprise data strategy was defined by vendor lock-in. Organizations ingested data into proprietary data warehouses (like Teradata, Oracle, or later, Snowflake and Redshift). Once the data was inside, it was locked in a proprietary format. If the organization wanted to use a different tool to analyze that data, they had to pay to extract it, move it, and load it into another system.
Open Data Architecture represents a fundamental rejection of that model. It is the core philosophy underpinning the modern data lakehouse, built on the principle that an organization's data should be an independent, modular asset, not a captive byproduct of the compute engine.
The Three Pillars of Open Data
A true Open Data Architecture is built on three essential pillars:
- Open Storage: Data is stored in low-cost, durable cloud object storage (like Amazon S3, Azure ADLS, or Google Cloud Storage) rather than local, proprietary disks attached to a compute cluster.
- Open File Formats: The raw data is written using widely adopted, open-source columnar file formats, predominantly Apache Parquet. These formats are highly optimized for analytics and can be read by almost any modern programming language or data tool.
- Open Table Formats: This is the crucial layer that turns a folder of Parquet files into a structured database table with ACID transactions. Apache Iceberg is the industry standard open table format. Because Iceberg is governed by the Apache Software Foundation, no single vendor controls the specification.
The "Bring Your Own Compute" Paradigm
When data is structured using an Open Data Architecture, the power dynamic shifts from the vendor to the data owner. The data rests at the center of the ecosystem, and compute engines become interchangeable, modular services.
An organization can use Apache Spark for heavy ETL processing, Apache Flink for real-time streaming ingestion, Dremio for sub-second BI dashboarding, and Python-based AI agents for exploratory analysis. All of these engines can read and write to the exact same Apache Iceberg tables simultaneously, without moving or duplicating a single byte of data. If a faster or cheaper compute engine enters the market tomorrow, the organization can simply point the new engine at their existing open data, immediately realizing value without a massive migration project.
