In today’s wave of digital transformation, data has become a core enterprise asset. Managing and leveraging it efficiently is more critical than ever. To address this, WhaleOps is launching a series of articles focused on lakehouse design and best practices, offering in-depth insights into data architecture and development standards.
From the challenges of traditional data warehouses to the convergence of lakes and warehouses, from layered architecture design to key considerations at each layer, and from DataOps standards to scheduling and integration best practices—this series provides a comprehensive roadmap. Our goal is to help readers master the fundamentals of lakehouse construction, enhance data management capabilities, and build a solid foundation for data-driven decision-making.
This article serves as Chapter 0 of the series. It explores the pain points of traditional data warehouses, compares the characteristics of warehouses, lakes, and lakehouses, and explains the “unification” vision behind the lakehouse approach—laying the groundwork for future practical implementation.
Why Traditional Data Warehouses Are Struggling
Explosion of Data Sources
As digitalization accelerates, traditional data warehouses face mounting challenges. Data sources now include operational databases, logs, tracking events, and external systems, all integrated through diverse methods. The growing number of sources leads to frequent schema changes, and instability upstream directly impacts downstream systems.
Fragmented Requirements and Faster Iteration
Business demands have become increasingly fragmented and iterative. The same metric is often defined differently across multiple reports, making it difficult to maintain consistent definitions. Disputes over metric definitions have become routine—especially at year-end, when “metric wars” often break out.
Growing Data Pipelines, Declining Stability
As the business evolves, data pipelines grow longer and more complex, reducing overall stability. Heavy interdependencies make troubleshooting difficult. Reprocessing data is rarely simple—an issue in one layer can cascade across multiple layers. Identifying the root cause may take half a day; backfilling data may take an entire day.
Diversification of Data Formats
Data formats are becoming increasingly diverse, including structured, semi-structured (such as JSON), and file/document-based data. Relying solely on traditional warehouse paradigms makes ingestion and governance costly. Teams often find themselves in a situation where data cannot be stored properly—or cannot be governed effectively.
Real-Time Expectations Becoming the Norm
Real-time expectations have shifted from T+1 batch processing to hourly or even minute-level latency. Converting traditional batch pipelines into real-time systems often requires rebuilding them from scratch. Worse, inconsistencies between batch and streaming definitions can further complicate data governance.
Rising Cost Pressure
Costs continue to rise across computation, storage, and engineering resources. Redundant development, duplicated storage, and inconsistent metric definitions create significant hidden costs. In many cases, “becoming more expensive over time” is more damaging to the business than “failing to build it at all.”
Lagging Governance
Governance efforts often fall behind. Data lineage is unclear, access permissions are chaotic, and data quality is hard to measure. Once data is widely adopted, retroactive governance becomes extremely costly. Governance is not a luxury—it is the foundation of sustainable data growth.
Differences Between Data Warehouses, Data Lakes, and Lakehouses
Strengths and Limitations of Data Warehouses
Data warehouses excel at strong governance, consistency, and high-performance analytics. They are ideal for operational analysis, standardized reporting, and core metric systems. With clearly defined schemas and tightly controlled definitions, data quality can be well maintained.
However, they are slow to onboard new data, costly to scale, and sensitive to change. Upstream schema modifications often require extensive refactoring. They also struggle to handle semi-structured and unstructured data efficiently.
Characteristics and Risks of Data Lakes
Data lakes offer low-cost storage, multi-format ingestion, and a “store first, compute later” paradigm. They are well suited for raw data retention, exploratory analytics, and AI/feature engineering workloads. Lakes provide fast ingestion, strong compatibility, and elastic scalability.
But without governance, a data lake can quickly turn into a “data swamp”—with disorganized directories, missing definitions, and rampant duplication. Data becomes difficult to find, understand, and trust.
The Goal of the Lakehouse
The lakehouse aims to combine the “breadth of the lake” with the “stability of the warehouse.” Built on top of a data lake foundation, it introduces transactional capabilities, version control, incremental processing, quality management, and access control. The objective is to integrate data storage, transformation, serving, and governance into a unified lifecycle.
The “Unification” Vision of the Lakehouse
Unified Storage Layer and Data Organization
The system should support structured, semi-structured, and file-based data while enabling partitioning, hot/cold tiering, and lifecycle management—keeping costs under control.
Unified Transactions and Versioning
Data must be usable and trustworthy. The platform should support incremental reads, historical traceability, rollback, and replay capabilities. Schema evolution should not destabilize the system.
Unified Computing and Batch-Streaming Collaboration
To prevent metric fragmentation, batch processing provides stable, traceable, and cost-efficient computation, while streaming enables low-latency, event-driven, incremental updates. The key is ensuring that both share the same data definitions and metric standards.
Unified Metadata and Data Catalog
Data should be discoverable and understandable. Tables and fields need clear definitions, ownership, update frequency, and lineage tracking. Impact analysis should reveal which downstream systems are affected by upstream changes.
Unified Data Quality and Observability
Problems must be detectable, traceable, and recoverable. Quality rules—such as completeness, uniqueness, range validation, and reconciliation—should be defined. Observability should include monitoring delays, failures, reruns, and data volume fluctuations.
Unified Security and Compliance
Data must be used in a controlled manner. This includes classification, access control, data masking, and auditing—especially critical when sharing data across departments or externally.
Unified Delivery Mechanisms
Data should be easier to consume. Whether for BI/reporting, APIs/applications, or algorithms/features, delivery paths should be standardized—reducing inefficient practices like each team maintaining its own export scripts.
👀👉 Coming next: Chapter 1 – Overall Data Architecture
Top comments (0)