Open Table Format: A New Standard for Data Management

Read more: Choosing the Best Data File Format for Your Needs

Introduction?

For modern data architecture, efficient data management solutions are more important than ever. And with that comes the rapid demand for enterprise to harness the power of their data.??

In previous articles in our data series, we talked about data storage formats. This is crucial for data lake as part of data lake’s characteristics is that it stores data in its raw form.??

Data Lake is a popular choice in the data stack for enterprises, due to scalability, flexibility, cheap storage, and enables advanced analytics for AI and ML. However, Data Lake also lacks crucial functionality compared to Data Warehouse, in particular lack of ACID transaction for data consistency, slow query performance, minimal schema enforcement, and failure to meet regulatory compliance requirements.?

Then, Open Table Format emerged as a new technology to help address some primary problems with Data Lake storage system. While Open Table Format is not new, recent developments in new table formats have pushed data lake to a new direction, improving its performance, efficiency, and functionality that are on par with Data Warehouse.?

Let’s explore!??

What is Open Table Format (OTF)??

Source: Delta Lake

Open Table Format (OTF) refers to open-source, standardized table formats built on top of standard data storage, designed to efficiently handle large datasets.??

It consists of three core components. First, data files are the actual files stored in a destination, often using columnar formats like Parquet or ORC. Second, the metadata layer is the most critical part of OTF, maintaining essential information such as the table’s schema, partitioning, and file locations. This layer enables systems to track changes and manage data, functioning similarly to tables in a data warehouse or database. Third, the transaction log is a chronological record of all data modifications, including insertions, updates, and deletions. It plays a key role in ensuring ACID compliance, making data operations reliable. Some of the most well-known open table formats today include Apache Hudi, Apache Iceberg, and Delta Lake.?

Key Features of OTF:?

Open Table Format (OTF) acts as a metadata and abstraction layer on top of Data Lakes, bringing database and data warehouse-like functionalities while maintaining the flexibility of a Data Lake. One of its key advantages is ACID transaction support, ensuring data reliability and consistency for complex operations. Schema evolution allows for seamless modifications to table schemas without disrupting existing data. Additionally, full CRUD operations enable row-level updates and deletions, which are essential for data accuracy and regulatory compliance. OTF also offers time travel, allowing users to roll back to previous data versions for auditing, debugging, or failure recovery. Its enhanced performance stems from efficient metadata and data handling, leveraging techniques like data skipping and indexing to significantly improve query execution.?

Beyond improving Data Lakes, OTF provides multi-system support, integrating with various data processing engines (e.g., Spark, Trino, Flink) and storage solutions (e.g., GCS, Azure ADLS2, S3). It is also optimized for both analytical and transactional workloads, combining ACID properties for transactional use cases with high-performance querying for analytics—offering speeds comparable to traditional data warehouses. Lastly, OTF is open-source and community-driven, promoting vendor independence, transparency, and continuous innovation through community collaboration.?

Benefits of OTF?

Open Table Format (OTF) enhances Data Lakes by introducing a structured table abstraction, bringing significant benefits in performance, data management, and reliability.?

One of the key advantages is improved performance and scalability. Traditional Data Lakes store files in their raw format, making data retrieval slow. However, OTF optimizes this process by implementing indexing, skipping, and partitioning techniques, which reduce computational workload and significantly accelerate query performance.?

OTF also provides enhanced data management by enabling full CRUD (Create, Read, Update, Delete) operations within the Data Lake environment. Unlike conventional approaches that require extensive rewrites, OTF allows seamless modifications, improving ease of data handling and maintenance.?

Another critical feature is time travel and schema evolution. OTF supports time travel, allowing users to rollback to previous versions of a data table for auditing and debugging purposes. Additionally, schema evolution enables changes to table structures without modifying the original data, ensuring greater flexibility in handling evolving data requirements.?

Lastly, OTF introduces ACID transaction support, ensuring data consistency, reliability, and integrity during complex data operations. By incorporating ACID properties, OTF enables Data Lakes to function more like traditional databases while retaining their scalability and flexibility.?

Popular Open Table Format: Hudi, Iceberg, and Delta Lake?

Currently, there are three widely used Open Table Formats, each offering similar features but differing in their development approaches.?

Apache Hudi was originally developed at Uber as an open-source data management framework designed to enhance Data Lakes for both batch and streaming operations. Over time, it has evolved into a primarily streaming-focused data lake platform, integrating streaming primitives and providing database-like capabilities to Data Lakes.?

Apache Iceberg was initially developed by Netflix to address limitations in Apache Hive and the broader Hadoop ecosystem. In 2018, it was donated to the Apache Foundation and became an openly managed project. Iceberg resolves many of the shortcomings of its predecessors, offering a scalable, flexible table format with broad integration across modern storage and computing systems.?

Delta Lake, developed by Databricks—the creators of Apache Spark—was open-sourced in 2019. It serves as a storage layer for big data workloads, bringing traditional database and warehouse functionalities to Data Lakes. Delta Lake extends the Parquet format and provides seamless integration with Apache Spark, ensuring ACID compliance, schema enforcement, and time travel for reliable big data processing.?

Each of these Open Table Formats enhances Data Lakes with structured table management, improving performance, consistency, and scalability while catering to different use cases.?

Future of Open Table Format?

As AI and machine learning continue to grow, the demand for advanced analytics in data is expected to rise. Open Table Formats (OTF) are likely to integrate more closely with AI frameworks and compute engines specialized for ML workflows. Future developments may focus on enhancing ML training efficiency and improving the inference process by enabling direct integration between storage platforms and AI-driven applications.?

Interoperability and standardization are also becoming key priorities for OTF. One of the main objectives is to reduce vendor lock-in, ensuring seamless interactions between different data systems and even across various OTF implementations like Hudi, Iceberg, and Delta Lake. By promoting open standards, OTF allows businesses to select the best tools for their needs without being restricted to a single vendor’s ecosystem.?

Additionally, the open-source community continues to drive innovation in OTF. With an increasingly vibrant developer ecosystem, ongoing contributions are leading to the rapid development of new features and improvements. This collaborative approach ensures that OTF can adapt to emerging challenges in the data space and keep pace with advancements in data technology.?

Conclusion?

Open Table Format (OTF) is reshaping how enterprises manage data in Data Lakes, bridging the gap with traditional Data Warehouses. Key features like ACID transactions, schema evolution, time travel, and improved query performance make it essential for modern data management. As AI and analytics advance, OTF's role grows, driven by its ability to boost performance, ensure reliability, and offer flexibility with large datasets. Its open-source nature and community support reduce vendor lock-in and foster innovation. With platforms like Apache Hudi, Apache Iceberg, and Delta Lake, OTF is becoming a core element in scalable, adaptable data architectures.?