Data capturing, processing and deriving insights from it has become crucial for businesses. To cater this need organizations are adopting sophisticated storage and processing solutions. One of the most discussed architectures for data management is the Data Lakehouse — a next-generation data platform with the flexibility of data lakes along with the structure and performance of data warehouses. Nevertheless, whereas lakehouses are best suited for today’s multi-faceted, huge-scale data demands, data lakes, as well as data warehouses, remain valuable depending on use cases. We will discuss below the differences, strength, and shortcomings of one over the other, as well as how to decide when to use what.

What is a Data Warehouse?

The need for data warehouses emerged in the past from the growing necessity to analyse business data in a structured and efficient way. A data warehouse is a centralised repository that stores structured data from multiple sources in a highly organised manner. Before data enters a warehouse, it is typically cleaned, transformed, and integrated to fit a well-defined schema — ETL (Extract, Transform, Load).

Data warehouses are designed to support complex queries, business intelligence, and reporting tasks with high performance. They power dashboards, metrics, and trend analyses that decision-makers rely on. Famous data warehouse options are - snowflake, aws redshift.

Limitations of Data Warehouses

Expensive to scale: Storage and compute can become costly as data volume grows. For example with Snowflake you do not get direct control on underlying storage layer, and you have to pay as snowflake charges you for storage.

Closed and proprietary: Many warehouses use proprietary formats and engines, creating vendor lock-in. Snowflake again is a typical example which uses its own proprietary file storage format.

Limited data support: Typically handle structured data only — do not natively support semi-structured (JSON, XML) or unstructured data (videos, images, logs). In aws Redshift, you can’t directly store or analyse Video/audio files, Images, Raw logs or documents.

Rigid schema: Schema must be defined up-front, making it harder to adapt to new data types or changes in schema for new incoming data.

What is a Data Lake?

Organisations required a more adaptable and scalable method of information storage as big data and a variety of data types, such as clickstreams and IoT sensor data, proliferated. Let's talk about the data lake.

You can store raw, unstructured, semi-structured, and structured data in its original format in a data lake, which is a centralised repository. Schema-on-read is supported, which means that the data structure is only used when you read or process it—not when it is being stored.

Advanced analytics and machine learning are made possible by data lakes, which are frequently constructed on top of cloud-based object storage and are used to economically gather vast volumes of data. Typical examples of data lakes - https://delta.io/ , https://iceberg.apache.org/

Limitations of Data Lakes

Data swamps: No out of the box data governance is provided unlike data warehouses. Without proper governance, a data lake can quickly become a data swamp — a chaotic, unusable mess of unstructured data. Data warehouses has sophisticated data governance at there disposal, one can grant/revoke the permissions on many data objects like databases, and this out of the box data governance prevents data warehouse from dirty data.

Difficult to query efficiently: Query performance is often poor compared to data warehouses, especially for large-scale analytics. Main culprits are - Lack of Built-in Indexing, schema on read adds overhead, detached compute could cause cold start issues, manual tuning needed per use case basis, need to handle small / fragmented files manually by tuning.

Data Lakehouse = Data warehouse 🤝 Data Lake

A Data Lakehouse is a modern data architecture that combines the best of both data warehouses and data lakes. It builds directly on top of a data lake but adds data management, governance, and performance features typically associated with warehouses.

Lakehouses support any data type (structured, semi-structured, unstructured) and implement open formats like Parquet, Delta Lake, or Iceberg — avoiding vendor lock-in. They're also optimized for cloud-native infrastructure, making them cost-effective and highly scalable. Typical example of data lakehouse is databricks.

Benefits of Data Lakehouses

Governance & security: Provides fine-grained access control, auditing, and lineage tracking. Example - unity catalog in databricks

Open and cloud-native: Uses open-source file formats and cloud object storage, reducing cost and increasing flexibility. For example databricks workspace deployed on aws / gcp / azure

High performance: Modern lakehouse engines use caching, indexing, and advanced query optimisations.

Schema enforcement and evolution: Supports both schema-on-write and schema-on-read paradigms.

When to use what?

While data lakehouses represent the most advanced, versatile, and scalable architecture for modern data needs, there are still scenarios where data warehouses or data lakes might be more appropriate.

Conclusion

Organisations require adaptable and future-proof solutions as data continues to increase in volume, variety, and velocity. Data lakehouses offer a single platform for managing deep learning and dashboards. Before selecting the best platform, it's crucial to assess your data types, team capabilities, objectives, and financial constraints. A hybrid strategy that uses a warehouse or lakehouse for analytics and a data lake for raw ingestion may be the most appropriate in some cases.