Businesses are becoming more data-reliant as they generate and collect massive amounts of information from various sources. To turn this data into valuable insights, they need a clear data strategy along with systems that can store, manage, and analyse it effectively.
In this blog post, we'll explore the differences between data lake and data warehouse and help you determine which one is the best fit for your business.
Data lakes definition
A data lake is a centralised storage system that collects and holds large volumes of data in its original, raw format. It can handle all types of data, including structured (such as databases), semi-structured (like JSON or XML), and unstructured data (like images, videos, or text files).
Traditional data lakes are often used to support data analytics, machine learning, predictive modelling, and other advanced data-driven processes.
Typically, data in a lake is organised into stages or zones:
- Raw (untouched data as collected)
- Cleansed (cleaned and formatted)
- Curated (refined and ready for analysis or reporting)
Data lake architecture
Data lake architecture is the foundation of a data platform that makes data in a lake organised, accessible, integrated, and secure. It supports various data types and enables efficient storage and processing.
The architecture is built for big data workloads and can be a foundation for a data lakehouse, which combines features of data lakes and data warehouses.
It must be:
- Scalable to handle large volumes of data
- Capable of resource management and orchestration for smooth processing
Implementing a data lake: a step-by-step approach
1. Define business requirements: Start by identifying business goals, data needs, and the overall scope to ensure the data lake aligns with organisational objectives and supports agility.
2. Build a scalable architecture: Select a flexible and scalable infrastructure that can accommodate increasing volumes and a range of data types. Use DevOps and orchestration tools to automate big data processing.
3. Implement data governance: Ensure data accuracy and transparency by managing data quality, using version control, tracking data lineage, and applying access controls.
4. Monitor and maintain: Continuously monitor system performance, manage storage efficiently, and perform regular maintenance to keep the data lake optimised and compliant.
5. Provide training and support: Offer training for data scientists and data engineers to help them effectively access, understand, and use data from the lake.
Introduction to data warehouse
A data warehouse is a centralised system for storing structured, processed data used in reporting and analysis. Unlike data lakes, it stores clean, organised data, ready for queries and business intelligence (BI) tools.
Data is usually loaded through Extract, Load, Transform (ELT) processes. Businesses rely on data warehouses to make decisions based on accurate and accessible data.
Data warehouse architecture
Data warehouse architecture defines how data is collected, stored, and accessed. It usually includes:
- Data Sources – Databases, CRM systems, flat files, APIs
- ETL/ELT – Extracts, cleans, and loads data into the warehouse
- Staging Area – Temporary storage for raw or intermediate data
- Storage Layer – Structured data in schemas (e.g., star, snowflake)
- Metadata Layer – Info about data structure, lineage, and use
- BI Tools – For queries, dashboards, and reports
Modern warehouses are often cloud-based, offering better scalability and performance. Common platforms include Snowflake, Amazon Redshift, BigQuery, and Azure Synapse.
Data lake vs. data warehouse comparison
| Aspect | Data lake | Data warehouse |
|---|---|---|
| Data type | Raw, unprocessed data | Processed, structured data |
| Data formats | All types (structured, semi-structured, unstructured) | Highly structured and unified data |
| Primary use cases | Big data analytics, machine learning, predictive analytics, intelligent automation | Business intelligence, operational reporting |
| Typical users | Data scientists, engineers | Business analysts, decision-makers |
| Best for | Flexible storage and advanced analytics on diverse data types | Fast querying and consistent reporting on clean, structured data |
| Decision factor | Chosen based on need for flexibility and raw data handling | Chosen for structured reporting and data quality |
Benefits of data lakes
Data lakes offer a low-cost solution for storing large volumes of structured and unstructured data.
They support schema-on-read, allowing users to structure data at the time of access for building dynamic ETL pipelines.
Data lakes enable data science, machine learning, predictive analytics and intelligent automation by providing access to raw and diverse datasets.
They act as a single hub for all data types, simplifying data management and analysis across the organisation.
Challenges of data lakes
- Traditional data lakes lack support for transactions, data quality assurance, and governance.
- Poor performance optimisations can lead to data swamps, rather than data lakes.
- Data lakes can be prone to data corruption, quality control issues, and performance issues.
- Good governance and stewardship practices are necessary to run a data lake platform smoothly.
- Data lakes require careful planning and management to ensure data integrity and security.
Benefits of data warehouse
Data warehouses store cleaned and organised data, making it consistent, reliable, and ready for analysis and reporting.
They are optimised for complex queries and fast data retrieval, which supports real-time dashboards and business intelligence tools.
With defined schemas and integrated security protocols, data warehouses safeguard sensitive information and support compliance with data privacy and cybersecurity regulations.
Data warehouses are designed to serve business users with accurate, historical data that drives informed, strategic decisions.
Challenges of data warehouse
- High upfront costs – Setting up and maintaining a data warehouse can be expensive in terms of infrastructure and licensing.
- Limited flexibility – Data warehouses handle only structured data, making it harder to work with diverse or unstructured data sources.
- Complex ETL processes – Transforming and loading data into a warehouse requires time-consuming and rigid ETL pipelines.
- Scalability issues – Traditional on-premise warehouses can struggle to scale efficiently with growing data volumes.
- Slower adaptation to change – Modifying data models or integrating new data sources often requires significant effort and planning.
Data lakehouses
After having considered the details of data lakes and data warehouses, it is impossible to leave aside the term "data lakehouse".
A data lakehouse is a modern data management architecture that combines elements of both data lakes and data warehouses. It provides the flexibility and scalability of a data lake (allows storage of all types of data in raw form) and performance and governance typically associated with data warehouses.
Data lakehouses provide a reliable, single source of truth, eliminating data silos and making analytics accessible across the enterprise. Data lakehouses are ideal for organisations that need to store and analyse large volumes of structured and unstructured data. With the support of custom software development, lakehouses can be tailored to specific business needs and integrated seamlessly into existing ecosystems.
Cloud storage: scalable data lakes and warehouses foundation
Cloud data and storage offer scalable, flexible solutions that help organisations store and process large volumes of data without the need for expensive hardware investments. Because of this, many data lakes are built on cloud platforms, which provide benefits like stronger governance, enhanced security, data sovereignty, and faster performance with low latency.
<p=>Similarly, cloud data warehouses are fully managed and automated, allowing even beginners to set up and use them easily with just a few clicks. They operate on a pay-as-you-go pricing model, helping organisations control costs by paying only for the resources they use.
Together, cloud data lakes and warehouses free businesses from managing complex infrastructure, letting them focus on extracting meaningful insights and value from their data, making cloud migration a critical enabler of digital transformation.
Final thoughts
Choosing between a data lake and a data warehouse depends on your company's data strategy and business needs. From a data engineering perspective, data lakes offer flexibility and scalability for handling raw, diverse datasets. It is ideal for building data pipelines, streaming data ingestion, and supporting machine learning workflows. On the other hand, data warehouses provide a structured environment with optimised ETL processes, schema management, and fast SQL-based querying—essential for consistent reporting and business intelligence.
For organisations looking to combine both approaches, the data lakehouse architecture brings together the best of both worlds, offering performance, governance, and flexibility in a single platform.
Related Insights
