Streaming‑Batch Integration

DataFunTalk

Apr 10, 2023 · Big Data

Interview on Data Lakehouse: Current Applications, Challenges, and Evolution

This interview with NetEase data‑lake technology manager Ma Jin explains the distinction between data lakes and lakehouses, reviews the evolution of table‑format technologies such as Iceberg, Hudi and Delta Lake, evaluates feature maturity and performance trade‑offs, and discusses systematic versus non‑systematic adoption in enterprises.

Data LakehouseDelta LakeHudi

0 likes · 13 min read

Interview on Data Lakehouse: Current Applications, Challenges, and Evolution

iQIYI Technical Product Team

Feb 3, 2023 · Big Data

Data Lake Concepts, Benefits, and Iceberg‑Based Implementations at iQIYI

iQIYI’s data lake combines public‑cloud and private storage with Apache Iceberg’s snapshot‑based table format to enable near‑real‑time, unified batch‑and‑stream analytics, reducing costs, simplifying architecture, and improving data freshness across use cases such as log collection, audit, pingback, and member order processing.

Apache IcebergBig DataReal-time Analytics

0 likes · 25 min read

Data Lake Concepts, Benefits, and Iceberg‑Based Implementations at iQIYI

DataFunTalk

Feb 12, 2022 · Big Data

NetEase Internal Data Lake Project Arctic: Architecture, Requirements, and Future Roadmap

This article introduces NetEase's internally incubated data lake project Arctic, explains the concept of data lakes, outlines NetEase's specific requirements for a unified streaming‑batch platform, details Arctic's core architecture, storage strategy, data‑merge mechanisms, current achievements, and future development plans.

Apache IcebergArcticFlink

0 likes · 10 min read

NetEase Internal Data Lake Project Arctic: Architecture, Requirements, and Future Roadmap