XTable in Action: Seamless Interoperability in Data Lakes
CoRR(2024)
摘要
Contemporary approaches to data management are increasingly relying on
unified analytics and AI platforms to foster collaboration, interoperability,
seamless access to reliable data, and high performance. Data Lakes featuring
open standard table formats such as Delta Lake, Apache Hudi, and Apache Iceberg
are central components of these data architectures. Choosing the right format
for managing a table is crucial for achieving the objectives mentioned above.
The challenge lies in selecting the best format, a task that is onerous and can
yield temporary results, as the ideal choice may shift over time with data
growth, evolving workloads, and the competitive development of table formats
and processing engines. Moreover, restricting data access to a single format
can hinder data sharing resulting in diminished business value over the long
term. The ability to seamlessly interoperate between formats and with
negligible overhead can effectively address these challenges. Our solution in
this direction is an innovative omni-directional translator, XTable, that
facilitates writing data in one format and reading it in any format, thus
achieving the desired format interoperability. In this work, we demonstrate the
effectiveness of XTable through application scenarios inspired by real-world
use cases.
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