Data Lake Technology Maturity Curve: Architecture Modes, Design Principles, Core Functions, and Applications

In today's data‑driven business era, enterprises face exploding data volumes and diverse data types (logs, text, audio, video, features, objects), creating higher demands for data construction, management, and application.

Data warehouses, built through ETL processes, structure data into layered, subject‑oriented assets and enable AI/BI‑driven insights, but their architecture and technology limits struggle to keep up with complex, multi‑type data and advanced feature engineering.

To address these challenges, data lake architectures—supplemented by technologies like Delta Lake, Hudi, Iceberg, and Paimon—provide flexible, universal solutions for data construction, operation, and management, enabling iterative upgrades of data warehouses.

This article, compiled by several experts, offers a reference for those exploring data lake technologies to solve specific business scenarios.

The data lake technology maturity curve covers 85 key points across four dimensions: technical maturity, business value, technology lifecycle, and management difficulty, evaluating each point through phases such as foresight, growth, popularity, decline, and maturity, and introduces the four open‑source products.

01 Lake‑on‑Warehouse : Leverages the lake’s multi‑type storage advantages and the warehouse’s structured layering to achieve unified data integration and high‑performance data access.

02 Warehouse‑on‑Lake : Suits stable business domains where data is relatively fixed; focuses on data analysis and application, using lake features like schema evolution to improve efficiency.

03 Lake‑Warehouse Fusion : Combines the performance of warehouses with the low‑cost, flexible storage of lakes, while addressing challenges such as ACID support, indexing, metadata management, and avoiding data swamps.

04 Lake‑Warehouse One‑Stop : Builds on fusion by using unified data formats (e.g., Hudi, Iceberg, Delta Lake) to enable seamless data access, atomic row‑level operations, and efficient analytics.

Overall, most enterprises evolve from warehouse‑centric solutions toward lake‑warehouse fusion and eventually one‑stop architectures.

Design principles for data lakes include:

1. Integrated Architecture : Multi‑type storage, standardized data formats, ACID transactions, efficient data production (COW, MOR, merge), and unified metadata management.

2. Elastic High Availability : Horizontal scaling/shrinking of resources, stable compute engines (Spark, Flink).

3. Enhanced Data Governance : Fine‑grained lineage for column‑level updates, addressing higher governance complexity.

4. High Concurrency Support : Transactional data formats enable massive concurrent updates.

5. Observability in Operations : Small‑file management, metric‑based monitoring.

6. Openness : Current lake formats are not fully compatible; careful selection is required.

7. Support for All Data Types : Accommodates diverse and complex data structures.

8. Transaction and Consistency : Row‑level ACID guarantees are now standard.

Core Functions of Data Lakes :

Recent adoption of Delta Lake, Hudi, Iceberg, and Paimon brings upsert capabilities, ACID transactions, schema evolution, hidden partitions, generated columns, and unified batch‑stream processing, enabling real‑time ingestion, efficient queries, and collaborative data models.

Each open‑source lake technology follows a similar implementation direction but solves specific scenarios:

Hudi :

Iceberg :

Delta Lake :

Paimon :

Key capabilities include fast incremental writes, index‑based file location, and deletion vectors that avoid full file rewrites, especially in COW modes.

In the data domain, lake technologies enable real‑time ingestion, incremental partitioning, hidden partitions, and deletion vectors to build wide tables for state‑change entities, replace batch diff calculations, and support minute‑level OLAP services. Schema evolution and efficient merges facilitate multi‑feature lake tables for machine learning, audience segmentation, and other downstream services.

Thank you to the expert team

Producer: Chen Yuzhao – Onehouse Hudi Flink Lead

Authors: Jin Guowei – Kuaishou Data BP Lead; Weng Caizhi – Alibaba Cloud Technical Expert

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