Data lakes and data warehouses are complementary data storage solutions enterprises use for business intelligence and analytics. While a data lake holds unprocessed structured and unstructured data, a data warehouse stores processed and vetted structured data for predetermined analytics purposes.
Enterprises manage these data storage repositories using data warehouse solutions and big data processing and distribution systems. Although they complement each other in an organization’s analytics ecosystem, data lakes and data warehouses differ in their schema, storage, analysis, processing, and cost.
What is the difference between a data lake and a data warehouse?
A data lake is a centralized, highly scalable data storage repository that stores vast volumes of raw structured, semi-structured, and unstructured data in their native format. It helps businesses build data pipelines and fuel data analytics for business insights.
Due to their open and scalable architecture, data lakes can store relational and non-relational data without sacrificing fidelity. Enterprises use data lakes to capture data from social media, streaming, business systems, mobile apps, and internet of things (IoT) devices and analyze them using data science and machine learning platforms.
A data warehouse is a specialized, subject-oriented data management system that organizes highly structured data using a data mart. While a data lake doesn’t define the data structure or schema until the data is read, a data warehouse applies a predefined schema before storing data. Data warehouses use relational databases and are ideal for prompt data analytics querying and supporting historical analysis.
The table below shows how a data lake and a data warehouse differ in terms of data processing, schema approach, and cost.
| Data lake | Data warehouse | |
| Definition | A data lake is a centralized data repository that ingests and holds structured, unstructured, or loosely assembled data for immediate or future use. | A data warehouse is a data storage unit that uses a predefined schema to store cleaned, processed, and organized structured data for a predetermined analytics purpose. |
| Users | Data scientists and engineers | Business intelligence teams, developers, managers, and end users |
| Data types | Data lakes store raw and unfiltered structured, unstructured, and semi-structured data in native formats. | Data warehouses hold processed cleansed and curated structured data. |
| Data readiness | A data lake stores data indefinitely, regardless of its immediate or future use. | Data in a data warehouse is analysis-ready and can be used for intended purposes via self-service business intelligence tools. |
| Data processing | Data lakes use the extract, load, and transform (ELT) approach to load data in its original format and transform it when needed. | Data warehouses use the extract, transform, and load (ETL) approach for data integration and preparation. |
| Schema approach | Data lakes use schema-on-read and don’t require pre-defined schema. | Data warehouses follow schema-on-write practices and define the schema before loading data. |
| Data storage | Data lakes store data using inexpensive cloud storage solutions. | Data warehouses use columnar or relational databases to store data with disk storage. |
| Data accessibility | Data lakes are agile and flexible, allowing easy addition of data models and applications. | Data warehouses contain data in ‘read-only’ format, making it difficult to modify the data. |
| Data security | Data lakes are less secure because of their large data volumes. | Data warehouses are more secure because of their robust and rigid structure. |
| Benefits | Data lakes help data scientists create analytical models critical for data analysis, business insights delivery, and strategic planning. | Data warehouses help business intelligence teams access and analyze structured data to support business operation decisions. |
| Use cases | Data lakes are ideal for data science applications, including machine learning, predictive modeling, and advanced analytics. | Data warehouses are ideal for data mining, ad hoc analysis, and business key performance indicator (KPI) tracking with data visualization and BI techniques. |
| Cost | Data lakes are less expensive as they use low-cost storage and servers. | Data warehouses are more expensive because they use large servers and disk storage systems. |
| When to use | Businesses use data lakes to store large volumes of raw and unfiltered structured, semi-structured, and unstructured data. | Data warehouses suit businesses looking to access and analyze structured data quickly. |
Learn the nitty-gritty of data modeling to establish and manage relationships among different data objects.
Sudipto Paul
Sudipto Paul is a Sr. Content Marketing Specialist at G2. With over five years of experience in SaaS content marketing, he creates helpful content that sparks conversations and drives actions. At G2, he writes in-depth IT infrastructure articles on topics like application server, data center management, hyperconverged infrastructure, and vector database. Sudipto received his MBA from Liverpool John Moores University. Connect with him on LinkedIn.
