The Evolution of Data Storage: From Data Warehouses to Lakehouses

A growing e-commerce company generates vast amounts of data everyday from customer orders and payment transactions to website clicks, product reviews, product images, customer support chats and marketing campaign data.

This data comes in different forms. Some organized into tables, while the rest consists of text, images, videos and log files. A traditional relational database isn't designed to efficiently store every type of data at this scale.

To address this challenge, organizations developed specialized storage architectures such as data warehouses, data marts, data lakes and lakehouses.

In this article, we'll explore what each of these architectures is, why it exists and how they fit together in a modern data ecosystem.

DATA WAREHOUSE

It's a centralized repository that stores cleaned, transformed, integrated and structured data collected from multiple operational systems.

Example:
A supermarket chain collects sales data from hundreds of stores every day. At night, the data is cleaned, combined and loaded into a data warehouse, where managers use dashboards to compare sales, identify best-selling products and monitor business performance.

Characteristics

• Stores structured data - Organizes data into predefined tables with rows and columns.
• Clean and validated - Data is cleaned and verified before being stored.
• Historical data - Preserves data over time for trend analysis and reporting.
• Optimized for reading - Designed to retrieve and analyze large amounts of data quickly.
• Intergrated from multiple systems - Combines data from multiple sources into one central repository.
• Schema-on-Write - Data must fit a predefined structure before being stored.
• Supports dimensional modeling - Data is organized using star or snowflake schemas to improve query performance.

Advantages

• Fast reporting - Returns analytical queries and reports much faster than operational databases
• High data quality - Stores cleaned and standardized data, making reports more reliable.
• Consistent information - A centralized source ensures everyone works with the same data across the organization.
• Excellent for business intelligence - Powers dashboards, reports, KPIs and other analytical tools used for decision-making.
• Strong governance - Supports security, auditing, and data management policies.

Disadvantages

• Can become expensive as data volumes grow - Large-scale storage and processing increase infrastructure costs.
• Requires data transformation before loading - Raw data must be cleaned and prepared before entering the warehouse.
• Less flexible - Changes to the data structure often require additional planning and redesign.
• Less suitable for unstructured data - Data such as images, videos and documents is better suited to a data lake.

Use Cases

-Financial reporting

-Sales analysis

-Executive dashboards

-Regulatory reporting

-KPI monitoring

DATA MART

It's a smaller, specialized version of a data warehouse designed for a specific department or business unit.

Instead of storing all company data, it contains only the information relevant to one team.

Example:

The finance department doesn't need access to customer support or marketing data. Instead, they use a finance data mart that contains only revenue, expenses, budgets and profit information, allowing them to generate reports more quickly.

Types of Data Mart

1.Dependent Data Mart - Created from an existing data warehouse, ensuring consistent and trusted data across the organization.

2.Independent Data Mart - Built directly from operational systems, making it quicker to implement but more likely to produce inconsistent data.

3.Hybrid Data Mart - Combines data from both a data warehouse and operational systems to balance flexibility and consistency.

Advantages

• Faster queries - Contains only department-specific data, reducing the amount of data that needs to be processed.
• Easier management - Smaller datasets make the system simpler to maintain and administer.
• Department-specific insights - Provides metrics and reports tailored to the needs of a particular business unit.
• Lower cost - Requires less storage and computing resources than a data warehouse.
• Better performance - Smaller data volumes allow reports and dashboards to load more quickly.
• Simpler reporting - Users can find and analyze relevant data without navigating the entire data warehouse.
• Improved security - Access can be restricted so departments only see data relevant to their roles.

Disadvantages

• Data duplication - The same data may be stored in multiple data marts, increasing storage requirements.
• Requires ongoing maintenance - Each data mart must be updated and managed separately.
• Possible inconsistencies - Poorly synchronized data marts may produce conflicting reports.

Use Cases

-HR reporting

-Marketing analytics

-Finance dashboards

-Sales reporting

DATA LAKE

It stores vast amounts of raw data in its original format until it is needed.

A data lake doesn't require data to be cleaned or transformed before storage.

Example:

A streaming platform stores user watch history, movie ratings, search logs, subtitles, images and videos in a data lake. Data scientists use this data to build recommendation systems and analyze viewing habits.

Characteristics

It stores:

• Structured data - Organized data such as SQL tables
• Semi-structured data - JSON, XML, CSV files, sensor data
• Unstructured data - Images, videos, PDFs, audio, emails

Schema-on-Read - The structure is applied only when someone wants to analyze the data.

Highly scalable - Can store massive amounts of data by expanding storage as data volumes increase.

Cost-effective - Uses low-cost storage solutions, making it practical for storing large datasets.

Supports advanced analytics - Provides raw data for data science, machine learning and AI applications.

Advantages

• Cheap storage - Allows organizations to store large amounts of data without the high costs of traditional databases.
• Highly scalable - Can handle growing volumes of data from multiple sources.
• Stores diverse data types - Supports data types from database records to multimedia files.
• Supports AI and machine learning - Provides large datasets that data scientists can use to train and improve models.
• Flexible data exploration - Allows organizations to store data first and decide how to use it later.

Disadvantages

• Data quality varies - Since raw data is stored without cleaning, accuracy and reliability may differ.
• Can become disorganized - Without proper management, a data lake can turn into a data swamp.
• Slower querying - Raw and unstructured data may require additional processing before analysis.
• Requires strong governance - Needs rules for data ownership, security, quality and usage.
• Difficult to manage without metadata - Without proper descriptions and documentation, finding and understanding data becomes challenging.

Use Cases

-Machine learning

-Big data analytics

-Data science

-Log storage

-Image processing

-Video analytics

DATA LAKEHOUSE

Traditionally, organizations maintained both a data lake for storing raw, diverse data and a data warehouse for structured analytics. However, this led to:

• Data duplication – The same data had to be copied and stored in multiple systems.
• Higher storage costs – Maintaining separate platforms increased infrastructure expenses.
• Pipeline complexity – More systems required more data movement, integration, and maintenance.

To solve these challenges, organizations introduced the lakehouse architecture. It combines the flexibility of a data lake with the management and performance features of a data warehouse.

Example:

An online retailer stores product images, product reviews, website clicks and sales records in a lakehouse. Analysts use the same platform to build sales dashboards while data scientists train recommendation models without moving data between multiple systems.

Characteristics of a Lakehouse

• ACID transactions - Ensures reliable data updates and maintains consistency, similar to traditional databases.
• Data governance - Provides controls for security, access, quality and compliance.
• Data versioning - Keeps track of changes, allowing users to view or restore previous versions of data.
• Supports batch processing - Handles large volumes of data processed at scheduled intervals.
• Supports streaming data - Processes continuously generated data in near real time.
• Machine learning support - Enables data scientists to access and analyze large datasets for AI models.

Popular Lakehouse Technologies

• Delta Lake
• Apache Iceberg
• Apache Hudi

Cloud Platforms Supporting Lakehouse Architectures

• Databricks
• Snowflake
• Microsoft Fabric

Advantages

• Single data platform - Reduces the need to maintain separate data lakes and warehouses.
• Lower storage costs - Avoids unnecessary duplication by allowing data to be stored once and used for multiple purposes.
• High performance - Supports fast analytics while maintaining the flexibility of a data lake.
• Supports AI and machine learning - Provides large, accessible datasets for advanced analytics.
• Supports Business Intelligence - Enables dashboards, reporting and data analysis.
• Reliable data management - ACID transactions improve data consistency.

Disadvantages

• Newer technology - Lakehouse concepts and tools are still evolving compared to traditional warehouses.
• Higher complexity - Requires knowledge of data engineering, cloud platforms and modern data technologies.
• Requires modern tooling - Organizations need specialized platforms and skills to implement and maintain lakehouse systems.

Use Cases

-Modern analytics

-AI

-Unified platforms

-Enterprise data engineering

DATA SWAMP

A data swamp is not a planned architecture. It's what happens when a data lake is poorly managed and lacks proper governance. Although the data exists, nobody knows what's inside, who owns it, whether it's accurate or how to use it.

Common causes of a Data Swamp

• Missing metadata - Without information describing the data, users cannot easily find or understand datasets.
• Poor documentation - Lack of descriptions makes it difficult to know the purpose and meaning of stored data.
• Duplicate data - The same data may exist in multiple locations, creating confusion about which version is correct.
• Lack of ownership - No clear responsibility for maintaining, updating or managing datasets.
• Weak security controls - Poor access management can expose sensitive information or violate compliance requirements.
• No data catalog - Without a searchable inventory, users struggle to discover available data.
• Inconsistent naming standards - Different naming approaches make datasets harder to locate and understand.

Problems caused by a Data Swamp

• Difficulty discovering data – Users spend more time searching for data than analyzing it.
• Uncertain data quality – Teams cannot determine whether data is accurate or reliable.
• Slow analytics – Poorly organized data requires extra preparation before it can be used.
• Conflicting datasets – Multiple versions of the same data can produce inconsistent reports.
• Compliance risks – Lack of governance makes it harder to protect sensitive information and meet regulations.

How to prevent a Data Swamp

A healthy data lake should include:

• Metadata catalog – Provides information about available datasets and their meaning.
• Data governance – Defines rules for managing, protecting and using data.
• Data lineage – Tracks where data comes from and how it changes over time.
• Access controls – Ensures users only access appropriate data.
• Naming conventions – Creates consistent standards for organizing datasets.
• Data quality checks – Identifies errors and ensures data reliability.
• Documentation – Helps users understand the purpose and usage of datasets.

CONCLUSION

Now that we've explored the different ways organizations store data, next up we'll look inside one of the most widely used architectures. Understanding how a data warehouse is built will reveal how raw data is transformed into the reports and dashboards businesses rely on every day.