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

01 Data Lake Development Trend Analysis

Data lakes have become a core component of modern enterprise data platforms. Traditional architectures separate the data lake, stream processing, and OLAP query engine, typically built on Hadoop for storage, Flink for real‑time streams, and engines such as Doris, StarRocks, or ClickHouse for analytical queries.

These separate platforms increase construction and maintenance costs and require redundant data copies for sharing. A fused data‑lake approach integrates the three functions, using a stream‑batch unified architecture to eliminate redundancy and simplify data movement.

02 Data Lake Overall Architecture

The reference architecture consists of several layers: data sources (business databases, message streams, logs), data integration (batch and real‑time), storage (Lakehouse on HDFS or object storage with Parquet/ORC formats), compute (Spark, Flink, or Hive for batch; Spark/Flink for unified stream‑batch), interactive analysis (Presto, Trino), and an OLAP layer that can query lake data directly or via synchronized warehouses.

03 Data Integration

Data integration bridges business systems and the lake. Batch integration uses periodic tools like Sqoop or DataX to move large volumes, while real‑time integration leverages CDC technologies such as Flink CDC to capture changes and stream them into Kafka or directly into the lake, then sync to OLAP stores like Doris.

Challenges of real‑time integration include ensuring data completeness, ordering, and stability under variable traffic.

04 Lakehouse Core Capabilities

Enhanced DML (UPDATE, UPSERT, MERGE) for mutable data.

Schema evolution via ALTER TABLE support.

ACID transactions and multi‑versioning for data consistency and time‑travel.

Concurrency control for safe multi‑user reads/writes.

File‑storage optimizations for fast OLAP queries.

Unified stream‑batch processing, allowing the same engine to handle both workloads.

Index construction to accelerate analytical queries.

Automated management (data compaction, cleanup, index building).

05 Open Design of Lakehouse

Open data formats (Parquet, ORC) enable seamless interaction with engines such as Spark, Presto, and Flink.

Support for multiple compute engines, both open‑source and commercial.

Integrated metadata and fine‑grained data‑access control.

Multi‑cloud deployment capability for private and public clouds.

06 Stream‑Batch Unified Architecture

Unified storage allows a single data copy to be read both as a stream and as a batch. Engines like Apache Flink and Apache Spark support both processing modes, reducing architectural complexity and development effort. ETL code can be written once and executed in either mode.

07 Real‑Time OLAP

Real‑time OLAP provides sub‑second query latency and high concurrency, scaling elastically with containerized deployments to handle workload spikes while optimizing resource usage during low‑traffic periods.

08 Lake‑Internal Warehousing

Lake‑internal warehousing embeds traditional warehouse capabilities inside the lake: storage optimizations (sorting, hashing), index layers, unified metadata services, and layered data models (ODS, DWS, ADS). Table designs such as snapshot and slowly‑changing dimension tables enable familiar warehouse functionality without data movement.

Overall, the fusion of data‑lake, stream, and warehouse technologies into a Lakehouse architecture improves efficiency, lowers cost, and enhances data sharing across the enterprise.