The open-source nature of OTFs encourages collaborative innovation, allowing users to benefit from the latest developments in data management. Prominent OTFs like Apache Iceberg and Delta Lake offer advanced solutions for data integrity and management. With OTFs, organizations can significantly enhance their data analytics and data management capabilities.
Organizations can leverage OTFs to enhance their data processing capabilities, making data accessible and meaningful. The benefits of OTFs include:
- Compatibility
- Cost-effectiveness
- Efficiency
- Flexibility
- Governance
- Interoperability
- Security
These advantages make OTFs a strong choice for data-driven organizations.
Why Use an Open Table Format?
In data engineering, the selection of data storage and management solutions is crucial to the success of data-driven initiatives. OTFs offer a wide range of benefits that address many of the challenges data professionals face today. One of the key advantages of using OTFs is the streamlining of data management processes. This includes simplifying data input, storage, and access across diverse data ecosystems. By using OTFs, organizations can:
- Reduce complexity,
- Improve data quality, and
- Accelerate time to insight,
all of which enhance decision-making processes and operational efficiency.
Another significant advantage of OTFs is their support for schema evolution and multi-tenancy. As data structures evolve over time, the ability to adapt without extensive rework or downtime is invaluable. Since OTFs support multi-tenancy, multiple applications can simultaneously access data in an OTF table, making data management easier. This not only optimizes resource usage but also facilitates data security and governance.
Finally, the open-source nature of many OTFs fosters a collaborative environment where innovations and improvements are continuously integrated. This ensures that organizations using OTFs benefit from the latest advancements in data management technology. Popular open-source projects are supported by a large community of developers and data professionals who contribute to their development, stability, and security. This collective effort results in robust, advanced solutions that can adapt to the ever-changing landscape of data technology. By choosing OTFs, companies embrace a dynamic, forward-looking approach to data management that is both scalable and sustainable.
Features of Open Table Formats
OTFs are designed to significantly enhance data management capabilities. One of the most notable features of these formats is their support for full create, read, update, and delete (CRUD) operations. This comprehensive functionality allows for flexible data manipulation and ensures that data lakes and warehouses can be updated in real-time. The ability to perform updates and deletes sets OTFs apart from traditional file-based storage systems, where such operations are inefficient.
Scalability and the ease of using OTFs with multiple data engines are other features they offer. As a result, organizations can manage their data ecosystems more effectively, making data-driven insights more accessible and actionable..
Transactional support with ACID compliance is another important feature of OTFs. ACID stands for Atomicity, Consistency, Isolation, and Durability. It describes a set of expectations that ensure all database transactions are processed reliably and correctly. A database is considered ACID-compliant when it meets these expectations or principles. ACID compliance is especially crucial in scenarios where multiple transactions occur simultaneously or when the system needs to recover from partial failures. OTFs guarantee that each transaction is either successfully completed or fully rolled back, providing a high level of data reliability and integrity for critical business operations. This feature is instrumental in supporting complex data processing tasks and ensures that data lakes and warehouses can serve as a single source of truth for organizations.
Key Types of Open Table Formats
Apache Iceberg and Delta Lake are among the most prominent formats, offering advanced solutions for managing large-scale data lakes and ensuring data integrity.
Apache Iceberg focuses on enhancing data reliability and scalability in data lakes. It provides robust schema evolution capabilities, allowing seamless adjustments to data structures without disrupting existing data or queries. Iceberg’s table format is designed to improve query performance across data engines, making it easier to handle complex analytical workloads. Its compatibility with various data engines—including Apache Spark, Apache Flink, and Presto—further increases its versatility.
Delta Lake introduces a transactional storage layer that brings ACID transactions to Apache Spark and big data workloads. Delta Lake’s ability to ensure data integrity, even during concurrent read and write operations, makes it a powerful format for use by data engineers. Its support for schema enforcement and time travel (the ability to query previous versions of data) provides additional data management and analysis capabilities.
The choice of a particular format may depend on specific use cases and requirements.
Common Architectures of OTFs
The architecture of OTFs is crucial for how data is stored, accessed, and managed within an organization’s data ecosystem. These architectures are designed to optimize data processing and ensure seamless integration with existing data management tools and frameworks. A common architecture involves placing the table on a distributed file storage system, such as Amazon Simple Storage Service (S3), Microsoft Azure Data Lake Storage Gen2, or Google Cloud Storage. This setup enables efficient processing of vast amounts of data while leveraging the scalability and durability of object storage services.
Another important aspect of OTF architectures is the use of metadata to manage data files. Metadata—comprising information about data files such as schema details, partitioning information, and change logs—is used to optimize data access and query performance. By maintaining a centralized metadata repository, OTFs can efficiently track changes in the data, support schema evolution, and enable features such as time travel and incremental processing. These capabilities can facilitate new workloads, such as AI use cases and model training.
Frequently Asked Questions
How do OTFs improve data lakes?
OTFs emerged from the need to enhance the efficiency and effectiveness of data lakes. By providing a structured approach to data storage and management, OTFs introduce an organizational layer that is often lacking in traditional data lakes. They offer an abstraction layer on top of data lakes, bringing database-like functionalities. This structured approach enables more efficient data queries and analyses by storing data in a manner optimized for access patterns and query performance.
One of the main ways OTFs streamline data lakes is by enabling schema-on-read capabilities. This allows data lakes to make data from various sources with different formats and structures available without the need for prior schema definitions. As a result, data engineers and analysts can focus on deriving insights from the data, rather than spending time on data preparation and transformation tasks. Additionally, the ability to enforce schema validation at write-time ensures data quality and consistency, reducing the likelihood of errors in the data.
OTFs also introduce transactional support and ACID compliance to data lakes, ensuring data integrity and consistency. This is especially important in environments where data is frequently updated or where multiple users access and modify data simultaneously. By supporting atomic transactions, OTFs ensure that data lakes can serve as a reliable source for the organization, facilitating accurate and timely decision-making. Additionally, features like incremental processing and time travel enhance the flexibility of data lakes, allowing organizations to track changes over time and access historical data as needed. These capabilities make OTFs an essential tool for optimizing data lake operations and unlocking the full potential of data assets.
How to Choose an OTF
The three most common OTFs—Apache Iceberg, Linux Foundation Delta Lake, and Apache Hudi—offer similar functionalities. However, their ecosystems, developer communities, and contributor networks differ, so it is advisable to choose an OTF based on the ecosystem and support available for your specific use cases and workload requirements. All three OTFs support ACID transactions, versioning, schema evolution, and time travel, and all can handle complex, high-performance query workloads and concurrent write operations.
A general drawback of open source also applies to OTFs: the abundance of options means that a different OTF might be more suitable for each specific use case. Finding the right fit requires balancing these factors carefully.
Teradata VantageCloud Lake and OTFs
Teradata engineers continuously explore emerging trends and software, leading in their contributions to open-source projects. Teradata was the first to introduce a Multi-Parallel Processing relational database on Linux, incubated Presto into Starburst, and contributed to Jupyter, PyTD, R, and many other innovations. Teradata Engineering always follows our customers' lead. When they identify a useful innovation, we strive to incorporate it into our software as quickly as possible. Cutting-edge Teradata customers keep us informed and help us identify new trends and innovations. Therefore, in 2019, we added read and write functionality to Teradata Native Object Storage (external tables on object storage), and in 2022, we added support for reading Delta Lake format tables on AWS, Azure, and Google Cloud. By the end of 2023, we will also support Iceberg. Starting this year, VantageCloud Lake will be able to read from and write to these table formats. We have not yet seen significant demand for Hudi from our customers.
