Understanding Data Lake vs. Data Warehouse: How to Store Data?

Data comes from diverse sources and formats, making processing and integration challenging. Without structure in data storage, valuable information can be lost, and analysis times can increase. Traditional data warehouses can be cumbersome and expensive to scale, while more flexible solutions risk becoming a veritable "junk drawer" where finding relevant data is difficult.

As a result, data analysts struggle with poor performance, inefficient resource use, and the risk of errors due to incomplete or poorly organized information.

This article explores two approaches to these challenges: data lakes and data warehouses. We'll break down their key differences, advantages, and disadvantages to help you determine the best solution for your business needs.

What Is a Data Warehouse?

A data warehouse is a centralized repository for structured information, designed for analysis and reporting. It aggregates data from disparate sources, cleanses and transforms it into an analyzable format, and organizes it according to a strict schema (e.g., star or snowflake models).

Think of a data warehouse as a well-organized library, where each book is cataloged by author, genre, and year of publication. This structured approach makes data easy to locate and use.

Data Warehouse Architecture

A standard data warehouse architecture consists of several key components:

• ETL (Extract, Transform, Load). Extracts data from various sources like CRM, ERP, and databases, transforms it into the required format and loads it into the warehouse.
• Storage Layers. Organizes data into raw, integrated, and aggregated layers.
• OLAP (Online Analytical Processing). Enables complex queries, reporting, and trend forecasting through multidimensional data analysis.
• BI Tools. Interfaces like Power BI, Tableau, and Looker for data visualization and reporting.

Thanks to this structure, data warehouse solutions ensure high query processing speed, reliability, and precise data organization, making them an ideal choice for corporate analytics.

A data warehouse can also have a multi-tier architecture. A single-tier approach minimizes the amount of data stored, while a two-tier approach separates physically accessible data sources from the data warehouse itself. However, the two-tier model is not widely used due to its limited scalability and difficulty in supporting many users.

The three-tier approach is the most popular and consists of:

1. Bottom layer. Data warehouse servers collect, cleanse, and transform data from various sources within the organization. During transformation, metadata is created to speed up searches and queries. ETL processes also help aggregate the transformed data into standardized formats.
2. Middle tier. This tier utilizes the online analytical processing (OLAP) model, which organizes and displays large amounts of data. OLAP allows analysts to examine data from multiple angles using a simple query language.
3. Top layer. The client interface layer, which often includes powerful dashboard software for visualizing, analyzing, and presenting the results of your data analysis efforts.

Main Use Cases

In finance, data warehouses help analyze transactions, detect fraud, and forecast profits. In retail, they help manage inventory, analyze customer behavior, and personalize recommendations.

In industry, data warehouses optimize supply chains, control quality, and monitor production processes.

Implementation of Data Warehouse

Building a data warehouse requires a well-defined data structure and strict organization of data loading processes. The first step is defining business requirements based on which a data schema (star, snowflake, or a combination of both) is designed. Next, the ETL process is organized, which involves extracting data from various sources (ERP, CRM, web analytics), cleansing it, and transforming it into a format suitable for analysis.

Performance optimization is a key challenge – data warehouses must provide fast access to information for analysts and BI tools. This is achieved through indexing, caching, and building aggregated tables. Security is also a crucial task, which involves managing differentiated access rights and encrypting data. The best option for security is in-house hardware or a dedicated server.

Popular data warehouse tools for implementation include:

• Google BigQuery — A cloud-based data warehouse with high scalability and SQL query support.
• Amazon Redshift — A robust analytical warehouse integrated with AWS services.
• Snowflake — A cloud platform offering separate storage and compute resources for large-scale analytics.
• Microsoft Azure Synapse Analytics — A hybrid data warehouse and big data analytics platform.

What Is a Data Mart?

A data mart is a subset of a data warehouse that focuses on a specific business area or function. Unlike a large data warehouse, which can contain all of an organization's information, a data mart provides a narrower, optimized set of data for a particular department or group, making it faster and easier to access.

What is unique about data mart?

• A data mart is typically created for a specific department, such as financial analysis, marketing, or sales. This allows the focus to be on the essential data for that particular team or business process.
• Because of the smaller volume of data, a data mart can offer faster query processing and reporting, which is especially important for users who need real-time data.

A data mart can be created as a stand-alone entity or part of a more extensive system — a data warehouse. It is often used for operational analysis and decision-making at the level of individual company departments.

What Is a Data Lake?

A data lake is a storage system designed to store large amounts of heterogeneous data in its original form. Unlike a data warehouse, a data lake can store both structured and unstructured data (logs, images, videos, JSON files, data from IoT sensors, etc.).

Simply put, a data lake is a vast digital reservoir where streams of information flow unfiltered, preserving all kinds of data. However, it requires tools to process and structure the data before it can be analyzed.

Data Lake Architecture

The main components of the data lake include:

1. Data sources. Any information flows, including databases, files, APIs, and IoT devices.
2. Data storage. Distributed storage systems such as Hadoop, Amazon S3, and Google Cloud Storage.
3. Metadata and cataloging. Metadata management systems (AWS Glue, Apache Atlas) help navigate through massive data.
4. Processing tools. Spark, Presto, and Hive enable big data analytics.
5. Machine learning and AI. Integration with analytics platforms (TensorFlow, Databricks) for advanced analytics.

This approach allows for the storage of large amounts of data without prior transformation, providing flexibility in information processing and the ability to apply advanced analytical tools.

Frequent Usage Scenarios

Data lakes are often used to analyze user behavior, such as collecting and processing web traffic data, clicks, and page views. In the Internet of Things (IoT), they help process sensor data and predict hardware failures.

In machine learning, data lakes store and train training samples. In cybersecurity, they analyze logs, detect anomalies, and prevent threats.

Implementation of Data Lake

Unlike a data warehouse, a data lake is designed as raw storage, so its implementation begins with choosing a reliable storage platform. Options include Amazon S3, Google Cloud Storage, Azure Data Lake Storage, or Hadoop HDFS.

Managing unstructured data is one of the biggest challenges in implementing a data lake. Without proper organization, a data lake can become a "data swamp," making it difficult to locate and process the correct information. Metadata catalogs like AWS Glue, Apache Atlas, or Databricks Unity Catalog help organize stored data.

Another important aspect is the performance of analytics. Since a data lake is not designed for fast SQL queries, it’s essential to use processing engines like Apache Spark, Presto, Trino, or Databricks, along with analytics acceleration technologies like Delta Lake or Apache Iceberg.

Data Lake vs. Data Warehouse: How to Organize?

Organizing a data lake vs. a data warehouse requires different approaches. A data warehouse begins with clearly defining business requirements, creating a data structure, and setting up ETL processes. The data is rigorously processed before being uploaded, ensuring high quality but reducing system flexibility. This approach is ideal for organizations that rely on predictable reports and require strict data quality control.

A data lake, on the other hand, functions as a flexible repository where data arrives in raw form. This requires advanced management mechanisms such as cataloging systems, analytics engines, and machine learning tools. The biggest challenge in managing a data lake is preventing it from turning into a "data swamp," where disorganized and unstructured data makes it challenging to extract useful information. A clear metadata management strategy and practical analytics tools are essential for good organization.

Key Differences: Data Lake vs. Data Warehouse

Both data warehouse and data lake solutions support business analytics but use different approaches. The key differences between them include:

The decision between a data lake and a data warehouse depends on specific business objectives. A data warehouse is ideal for structured data and predictable reporting, while a data lake is better for handling large volumes of heterogeneous data and advanced analytics technologies.

Hybrid Approach

In recent years, companies have been faced with the need to combine the benefits of a data lake with those of a data warehouse. A data warehouse provides clean, fast data processing but is limited to structured information. In contrast, a data lake allows you to store vast amounts of any type of data, but often struggles with management issues and processing complexity.

The solution to these problems is the data lakehouse, a hybrid approach that combines the best aspects of both solutions.

What Is a Data Lakehouse?

A data lakehouse is an architecture where data can be stored in its original form while still being available for high-performance analytics, machine learning, and business reporting.

In other words, while a data warehouse is an organized library and a data lake is a chaotic archive, a data lakehouse is a library that can store not only books but also manuscripts, notes, videos, and other materials, with efficient search and cataloging.

Key features of a data lakehouse include:

• Unlike traditional storage, a data lakehouse can scale computing resources regardless of the amount of data stored. This increases system flexibility and reduces costs.
• Support for heterogeneous formats (CSV, JSON, Parquet, video, images, etc.) makes the data lakehouse a universal solution for working with Big Data and traditional business data.
• Unlike a classic data lake, where it’s easy to create a “data swamp,” a data lakehouse uses cataloging, data version control (ACID transactions), and metadata management mechanisms, making data more accessible and manageable.
• Data lakehouses support traditional SQL queries, making them convenient for analysts and BI tools. Previously, data lakes required complex analytic engines such as Apache Spark, but with solutions such as Delta Lake (Databricks) and Iceberg (Apache), SQL query processing has become affordable and faster.

Popular platforms implementing the data lakehouse concept include Databricks Delta Lake (an ACID-compliant extension to Apache Spark), Apache Iceberg (a SQL-enabled tabular data management system), Google BigLake (a hybrid cloud solution based on BigQuery and cloud storage), and AWS Lake Formation (a service for managing data lakes with security policies and data organization in mind).

Choosing the Right Solution: Data Lake vs. Data Warehouse or Both?

A data warehouse is a clean, organized library ideal for structured data and business intelligence. A data lake, on the other hand, is a messy but powerful data source that can handle any information but requires advanced tools to structure and analyze it. The debate of data lake vs. data warehouse has some limitations.

A data warehouse is an ideal solution for companies that rely on reports, KPIs, and forecasts and require high reliability and predictability in analysis.

However, the following limitations should considered:

• It is expensive to store large amounts of data.
• It does not support unstructured data (images, videos, logs, etc.).
• The ETL transformation process before uploading can be lengthy.

A data lake is ideal for working with raw and heterogeneous data, especially if an organization is actively using big data, machine learning, and IoT.

Keep in mind the following limitations:

• Slow search and analysis without additional tools.
• It requires advanced technology to process data.
• There is a risk of “data swamping” if not organized properly.

A hybrid model (data lakehouse) is the best option for organizations that need flexible data storage and fast analytics.

Using Artificial Intelligence for Data Processing

The sheer volume of data today makes it nearly impossible to process and make sense of without artificial intelligence (AI). Many AI tools help clean, analyze, and process massive amounts of data, speeding up routine processes and uncovering patterns that might otherwise go unnoticed. This is especially valuable in environments like data warehouses, data lakes, and data lakehouses, where terabytes of heterogeneous data are handled.

For example, in a data lake, AI can automatically categorize files, eliminating the need to manually search for the correct information.

AI can also analyze logs in real-time to identify anomalies, helping prevent data breaches and cyberattacks. Banks, for instance, use specialized algorithms to detect suspicious transactions and block them before fraudsters can access the funds.

In a data warehouse, artificial intelligence speeds up queries by predicting which data is most often needed. A data lake brings order to the chaos of unstructured data, making it more accessible.

Modern BI systems with AI can automatically assemble reports and dashboards without manually sifting through tables. For example, in Microsoft Power BI, you can simply ask a question in natural language, and the system will suggest a graph or table.

Popular AI solutions for data processing include::

• Google Vertex AI, Amazon SageMaker, and Azure ML are powerful cloud platforms for working with machine learning.
• Apache Spark MLlib, TensorFlow, and PyTorch are tools for deep analysis and building AI models.
• IBM Watson, DataRobot, and H2O.ai are systems for predictive analytics that use significant automation.

Conclusion

Both data lakes and data warehouses offer unique strengths and limitations. For many organizations, the ideal solution isn’t choosing one over the other, but rather finding the right combination of both approaches within a unified ecosystem. Professionals must consider factors like data structure, performance requirements, and the system's flexibility to handle structured and unstructured data.

Ultimately, the choice of a data lake vs. data warehouse depends on the business's unique needs and the technology platform's ability to adapt to an ever-changing environment.