Data Lake(house) vs Data Warehouse: a comparison

By: Carolina Morais
Tag(s): Data
Published: Nov 27, 2023
Data Lake(house) vs Data Warehouse: a comparison

What is a data warehouse? 

A data warehouse is the backbone of data-driven decision-making in modern organisations. It's a highly structured repository designed to store, consolidate, and manage data from various sources across the organisation. Think of it as a meticulously organised library where data is cleaned, transformed, and made readily accessible for analytical and reporting purposes. At Ancoris, we use Google Cloud Platform (GCP) to implement the ingestion, cleaning, transformation and serving of data (with a data warehouse) according to the diagram below:

Data warehouses, including Google BigQuery, are designed for complex querying and reporting, enabling users to extract insights from historical data. They provide a reliable single source of truth for stakeholders. The modelling process, critical in data warehousing, efficiently organises and models data for analysis using methodologies like star schema or snowflake schema. At Ancoris, dbt (data build tool) is used to complement these data warehouses, which allows for modelling business processes effectively. Additionally, GCP offers BI platforms like Looker, Looker Studio and Looker Studio Pro, which drive data-driven insights and informed decision-making. 

What is a data lake?

In the realm of data management, a data lake is a compelling concept that diverges from the structured nature of data warehousing. Picture it as an expansive reservoir where raw data from diverse sources, both structured and unstructured, is collected without immediate structuring or transformation. Google Cloud Platform provides an ideal ecosystem for building and managing data lakes, offering Google Cloud Storage (GCS) as a versatile storage solution for housing vast quantities of data.

One of the notable benefits of GCS within a data lake is its flexibility when it comes to accommodating changes in schema or data formats. This flexibility empowers organisations to seamlessly ingest and store data without being constrained by predefined structures. Furthermore, for querying and analytics within this data lake, technologies like DuckDB shine. DuckDB serves as a potent analytics engine that can efficiently execute SQL queries on data residing in GCS, enabling organisations to derive insights and perform advanced analytics with agility and cost-effectiveness. By combining the power of GCS and DuckDB, organisations can unlock the potential of their data lakes, making them invaluable assets for data exploration, machine learning, and in-depth analysis, all while retaining the flexibility needed for evolving data requirements. Below is how Ancoris would implement a data lake: 



Data lake vs data warehouse


Data Lake

Data Warehouse

Data Types

Accepts structured, semi-structured, and unstructured data in their raw format.

Primarily designed for structured data.


Schema-on-read, applied when data is read or processed.

Schema-on-write, enforced upon ingestion.

Storage Structure

Flexible, with no fixed structure.

Highly structured with predefined schema.

Data Transformation

Allows data transformation at any stage, including after data ingestion.

Data transformation occurs during ELT (Extract, Load, Transform) preferred.


Easily scalable to handle large volumes of data.

Scalable but may require additional effort to accommodate unstructured data at scale.

Data Quality

Requires careful management of data quality and metadata to ensure accuracy.

Emphasises data quality control through data validation after data transformation

Cost Efficiency

Cost-effective for storage but may incur additional costs for data transformation.

Cost-effective for structured data storage and query performance.


Supports a wide range of open-source and cloud-based tools for data processing.

Utilises specialised tools for ELT, data warehousing, and business intelligence.

Schema Evolution

Offers flexibility to evolve schemas as data requirements change.

Schemas are enforced, making schema changes more structured and complex.

GCP Tools

Google Cloud Storage (GCS)


Use cases for data lake and data warehouse 

Data lakes and data warehouses, each with their distinct applications and tools, are pivotal in modern data management, particularly in Google Cloud Platform and various open-source platforms. Data lakes excel in scenarios that require storage and exploration of raw, diverse data types, making them ideal for applications in data discovery, machine learning, and ad-hoc analysis. In a typical use case, an organisation might employ a data lake for comprehensively analysing unstructured customer interaction data from multiple sources to discern emerging trends and patterns. 

To support such functionalities, GCP's Google Cloud Storage offers a scalable and secure foundation. Complementing this are file formats like Apache Parquet and table formats such as Delta Lake & Apache Iceberg, which enhance data storage and retrieval efficiencies, while open-source query engines like DuckDB and Polars provide options to sit on top of data lakes, facilitating rapid query operations. In addition to these, BigQuery supports reading data from the open-source table format Apache Iceberg.

On the other hand, data warehouses are more tailored for structured data analysis and are indispensable in fields such as business intelligence, financial reporting, and real-time analytics. A prime example is an organisation using a data warehouse to aggregate structured data from diverse sources into a centralised repository, enabling efficient, insightful BI tools and reports. 

Google BigQuery stands out in this realm as a powerful, serverless, and highly scalable multi-cloud data warehouse solution on GCP, offering fast processing of structured data and facilitating real-time analytics and decision-making. Over time, BigQuery has enhanced its functionality to support semi-structured data types such as JSON columns types and to read open-source table formats like Apache Iceberg, further bridging the gap between structured and semi-structured data management. In many cases, organisations opt for a hybrid approach, leveraging the strengths of both data lakes and data warehouses to address a wide spectrum of data processing needs effectively, blending the flexibility of data lakes with the structured efficiency of data warehouses.

How to get the benefits of both

At Ancoris, data lakes are less frequently used due to a preference for modelling business processes. Nevertheless, the most valuable aspect of data lakes, their ability to accommodate schema evolution, is sometimes implemented. We attain this level of flexibility by landing the data in Google Cloud Storage GCS enables the ingestion of unstructured data in various formats into a bucket. 

This data strategy is called a data lakehouse. A data lakehouse is an architectural approach that combines the scalable and diverse data storage capabilities of a data lake with the structured, organised, and efficient data processing features of a data warehouse. This hybrid model allows for storing and managing large volumes of structured, semi-structured, and unstructured data while providing robust data modelling and analytical capabilities typically associated with data warehouses.

Concluding remarks

If you have any questions or want to get started on your data lake, data lakehouse or data warehousing journey, feel free to get in touch with us. As a Google Cloud Partner who works day in and day out with data, Ancoris is well-placed to help you unlock the full potential of your data and help you meet your business needs. Feel free to contact us for anything on Google Cloud – we’re here to help. Think big, start now.

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