You want to make the best data-driven decision while avoiding excessive data costs for your business. Understanding the differences between data lakes and data warehouses is vital for efficient, cost-effective data storage and management. Learn about their most critical aspects and how to determine when to choose the data lake or a data warehouse.
In Brief: What Is Data Lake vs. Data Warehouse?
Data lakes can store all data, while data warehouses store highly structured data for a defined purpose. Running a data warehouse is usually more costly but far more efficient. Data lakes are easier to scale and typically require data engineers to manage and extract the value from the raw, unstructured data.
A data lake is like a grocery store. Inside, you’ll find fruits, vegetables, meats, dairy products, and seasonings. On the other hand, a data warehouse is like a restaurant. The ingredients are processed, prepared, and ready to serve as defined dishes (structured data).
What Is a Data Lake?
A data lake is a centralized storage depository that lets you store any data with any structure type at any scale. File types, schema, format, source, and purpose don’t limit the data lake’s use. You can store audio, video, text, IoT sensor data, social feeds, programming language files, and anything else in a data lake.
Like lakes in nature, data lakes have multiple structured and unstructured data sources that flow in the storage system. Thanks to the data lake’s flexibility, you can use this data in countless ways. However, it’s up to data engineers to find patterns, structures, and use cases with machine learning, data discovery, profiling, and other analytical methods to gain insights for key business decisions.
For efficient data lake use, you’ll need appropriate storage. vTECH io offers various storage methods that can fit your needs. For example, Dell’s PowerStore storage can quickly scale to accommodate the growing data lake needs. Or, a cloud solution may fit your business better. Comparing physical vs. cloud storage for data lakes or other purposes requires a thorough analysis of your business data needs. If you need help, contact our team, and we’ll guide you to the most appropriate solution.
A data lake use case is a manufacturing company that stores data for quality control. They could store machine sensor data, video capture from robotics, production line data, machine maintenance and error logs, inventory data, staff logs, and other data. Their data engineers can use analytical methods to find drivers for growth or discover underlying issues in their manufacturing processes using all or some of their captured data.
Data Lake Pros:
- Low effort to build
- Can have low latency
- Supports nearly unlimited data analysis
- Can store structured and non-structured data
- Supports all file types
- Lower upfront and use costs
- Easier to scale
- Decoupling of computing and storage
Data Lake Cons:
- Less efficient than a data warehouse for routine processing
- Requires expert engineers to analyze the data
- Complex data processing may escalate costs
- It’s easier to accumulate irrelevant or redundant data
- May pose security risks
- Raw data may increase the risk of poor data quality
What Is a Data Warehouse?
A data warehouse consolidates storage and serves as a processing hub for structured data defined by specific schemas. Only unified data exists in a data warehouse serving specific business needs.
Like an actual warehouse, contents are processed and organized into sections, providing a structured framework for efficient data reporting and analytics. This structured environment allows you to “ask a question” about a relationship between different data sets to uncover critical information for your business.
Unlike databases, data warehouses don’t necessarily collect data but are used for analytical processing (OLAP). Data warehouses keep historical data and can be refreshed from source systems. You’ll typically have a rigid schema to match the purpose of the data warehouse, so you have to plan and design your data warehouse for your particular reporting needs.
Data warehouses are optimized for swift and highly efficient SQL data querying. You can centralize multiple subject areas under one data warehouse if they fit your schema design. For example, storing relational data like customer and business process data.
Dell PowerStore is an on-premise (or cloud-hybrid) solution that supports many advanced features for data warehouses. For example, its data deduplication, pattern matching, and reduction can achieve a 5:1 data reduction. Likewise, its fast query processing enables exceptional performance in Business Intelligence (BI) tools and reporting systems.
A data warehouse use case is an e-commerce business consolidating data for reporting and analysis. Data like sales transactions, inventory data, and product catalogs can be cleaned, structured, and stored in a data warehouse to provide a primary source for accurate business analysis, inventory management, product demand forecasting, or identifying trends.
Data Warehouse Pros:
- Highly efficient for specific tasks
- Quality data acts as a “single source of truth,” improving trust in data insights
- Quick data access
- Low effort for analysts and business users to use data
Data Warehouse Cons:
- Higher cost than data lakes
- Require more time to manage
- Must define schema before storing the data
- Limited to its predefined business purpose

When To Use Data Lake vs. Data Warehouse
Choose a data storage method that best serves both user needs and the purpose of data use. So, while reviewing the comparison chart below, ask yourself: who will use these systems? What are their goals? Efficiently handling big data storage for analytics and reporting is quite complex. Feel free to contact our engineers if you need help finding the best storage method for your business and its unique challenges.
Data Lake | Data Warehouse | |
Use case | Generalized, broad data storage for research | Specialized data storage with predefined analysis purposes |
Users | Data scientists and data engineers | Data and business analysts |
Structure and Schema | Unstructured, semi-structured, and structured | Highly structured |
Data Sources and Types | All data, all sources | Relational data that fits the schema design |
Data Quality | Less reliable | More reliable |
Data Flexibility and Agility | Higher | Lower |
Analysis | Exploratory analytics, data discovery, profiling, machine learning, big data, BI, streaming, and operational analytics | BI, batch reporting, and visualizations |
Cost | Lower | Higher |
Use Case
Data lakes store all data that might be helpful after a thorough analysis using data engineering methods. However, data lakes require significant storage space, considering the broad data capture and the fact you are unlikely to use all the stored data.
Data warehouses contain data used for a pre-defined purpose. You may define this purpose after finding a key driver in the data lake and refining the data to fit the data warehouse schema. For example, exploring your construction business data lake through raw project data may uncover reasons for project completion delays, like inconsistent material delivery. Designing the data warehouse to use structured tables that track supplier reliability to improve supplier selection can prevent project delays.
Structure and Schema
The schema in data lakes is only used for the organization and structure of data stored in the lake. There are no strict rules for the data itself. On the other hand, data warehouses are designed with a schema to structure data for query performance and meeting the warehouse’s purpose. You’ll typically have to clean and transform the data before storing it in the data warehouse for schema conformity. This makes warehouses more rigid but also improves query performance.
Data Quality
Since data lakes include all data, even raw and non-curated, their data reliability is lower than that of the data warehouse. Your data stored in the data warehouse acts as a central version of the truth, providing reliability and trust for making critical business decisions. This is achieved through refining, cleaning, and curating the data before it enters the data warehouse.
Performance
Data lakes usually decouple storage and computing, providing you with cost savings while allowing real-time querying and data streaming. Likewise, you can use distributed computation to improve performance and parallel data processing.
When it comes to specialized queries, data warehouses provide the fastest results. They are designed for the quickest reporting possible. While you can’t use the data warehouse for all data analytics, you get the best performance for its pre-defined purpose.
Security
With petabytes of data and a lack of data filtration/selection, data lakes are more vulnerable to cybersecurity breaches. A data warehouse stores data in a structured environment, making it more secure. Likewise, the warehouse technology is more established and has more mature data security.
Analysis
Data lake technology can be used to explore patterns and correlations in your data that you may never even consider analyzing, which is a job for data experts. Businesses are still adapting to big data and how it can benefit them. Sometimes, a simple cross-reference will yield a winning approach, while other times, you may need highly complex analytical methods involving machine learning and others.
In contrast, the data warehouse isn’t suitable for experimental analysis. You’d typically make a warehouse based on the concept you envisioned through data lake analysis. It serves as a system for ongoing decision-making, reporting, and pre-defined analysis.
Costs
Thanks to their agile nature, storing large amounts of data in a data lake is less costly. No need for a fixed schema means eliminating the filtration and added work for your staff. Likewise, you can use less costly storage solutions that are decoupled from compute resources.
While creating and depositing information into a data warehouse is more expensive, analyzing data within a predefined schema is far more efficient. So, it’s less resource-intensive to analyze data for a limited number of factors you know are critical in a warehouse rather than attempting to do the same with a data lake.
vTECH io Helps You Grow With the Right Data Storage Technologies
Using Big Data effectively to find or improve your edge in the market requires setting up proper physical or cloud storage. Considering the size and complexity of data lakes and warehouses, several key factors can influence your informed decision. Choosing the right technology for the storage method is crucial to maximizing efficiency and insights.
vTECH io offers storage solutions for both applications. Contact us today, and let our experts guide you to the right choice. As a Dell Platinum Partner, we’re well-equipped to help you navigate the complex world of IT.
