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Schema Evolution

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DataFunSummit
DataFunSummit
Mar 17, 2024 · Big Data

OPPO Smart Data Lakehouse: Architecture, Real‑time Lakehouse, and Technical Practices

This article presents OPPO's smart data lakehouse solution, describing its massive EB‑scale architecture, the integration of batch and streaming engines, the Glacier service for table management, schema‑adaptive ingestion, performance optimizations, and future technical road‑maps for unified data processing.

Big DataData LakehouseFlink
0 likes · 15 min read
OPPO Smart Data Lakehouse: Architecture, Real‑time Lakehouse, and Technical Practices
DataFunSummit
DataFunSummit
Oct 1, 2023 · Big Data

Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans

This presentation introduces Iceberg's core capabilities, details Xiaomi's practical applications—including log ingestion, near‑real‑time warehousing, offline challenges, column‑level encryption, and Hive migration—and outlines future development directions such as materialized views and cloud migration, providing a comprehensive view of modern data‑lake engineering.

Big DataFlinkIceberg
0 likes · 22 min read
Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans
DataFunTalk
DataFunTalk
Jun 26, 2023 · Big Data

Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans

This presentation details Iceberg's core capabilities—transactional writes, schema evolution, implicit partitioning, and row‑level updates—while showcasing Xiaomi's real‑world applications such as log ingestion redesign, near‑real‑time warehousing, offline optimizations, column‑level encryption, Hive migration strategies, and outlining upcoming enhancements like materialized views and cloud migration.

Big DataColumn EncryptionFlink
0 likes · 20 min read
Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans
DataFunTalk
DataFunTalk
May 11, 2023 · Big Data

Scaling ByteDance Feature Store to EB‑Level with Apache Iceberg: Architecture, Practices, and Future Roadmap

This article describes how ByteDance tackled petabyte‑scale feature storage by adopting Apache Iceberg, detailing the problem background, design choices, implementation of COW and MOR back‑fill strategies, performance optimizations, and future plans such as lake‑cold‑layering and materialized views.

Apache IcebergBig DataFeature Store
0 likes · 16 min read
Scaling ByteDance Feature Store to EB‑Level with Apache Iceberg: Architecture, Practices, and Future Roadmap
Didi Tech
Didi Tech
Oct 8, 2019 · Databases

Design and Implementation of Fusion-NewSQL: A NewSQL System Built on Distributed NoSQL Storage

Fusion‑NewSQL is a NewSQL layer built atop Didi’s distributed KV store Fusion, translating MySQL queries into Redis‑style hashmaps, asynchronously maintaining secondary indexes, supporting fast Hive‑to‑Fusion loads and Elasticsearch integration, thereby delivering over 2 million QPS, 600 TB storage and flexible schema evolution for dozens of services.

Distributed StorageIndexingMySQL Compatibility
0 likes · 15 min read
Design and Implementation of Fusion-NewSQL: A NewSQL System Built on Distributed NoSQL Storage