Data Lake Development Trends, Architecture, Integration, Lakehouse Core Capabilities, and Open Design

Data lakes have become a core component of modern data platforms, typically consisting of a storage layer, stream processing (e.g., Flink), and OLAP engines (e.g., Doris, StarRocks, ClickHouse). Traditional architectures keep these three platforms separate, leading to higher cost and data duplication.

To address these issues, the industry is moving toward a fused data lake that combines storage, compute, and analytics in a single Lakehouse architecture, enabling stream‑batch unified processing, eliminating data movement, and reducing operational complexity.

The Lakehouse reference architecture includes data sources, data integration (batch via Sqoop/DataX and real‑time via Flink CDC), storage using open formats such as Parquet and ORC, compute engines supporting both batch and streaming (Spark, Flink), interactive query engines (Presto, Trino), and an OLAP layer that can query lake data directly.

Key Lakehouse capabilities include enhanced DML (update, upsert, merge), schema evolution, ACID transactions with multi‑version support, concurrency control, time‑travel queries, storage‑format optimization, built‑in indexing, and automated management of data compaction and cleanup.

Open design principles ensure support for open data formats, a wide range of compute engines, integrated metadata and fine‑grained access control, and multi‑cloud deployment flexibility.

Additional concepts such as real‑time OLAP, lake‑internal warehousing, and stream‑batch unified ETL further improve query latency, concurrency, and data consistency across batch and streaming workloads.