Data lake vs data warehouse: What’s the difference?
Knowing the differences between data lakes and data warehouses will help you choose the right cloud solution for your needs.
January 19, 2024 10 min read
Data lakes and data warehouses are similar in concept, but they perform different jobs. Both have their place in an increasingly data-driven landscape. Professional data scientists, cloud architects and engineers design data solutions around the business problems they're trying to solve, which may include some mix of warehousing, lake storage and sophisticated processing. These overlapping data models process troves of data to help deliver solutions to complex issues.
Key differences and similarities between a data lake vs data warehouse
To start the data lake vs data warehouse discussion, it's helpful to outline the distinctions between these related concepts:
|A vast reservoir of data, including raw and unprocessed information, drawn from available sources
|Structured storage for processed and semi-processed data that's ready to be analyzed
|Big data analytics, real-time processing
|Business reporting, decision-making
|Very wide scope of data from all available sources
|Data from specific sources, often within a single department
|Data scientists, maching learning engineers
|Business analysts, project managers
|Structured and unstructured data sources
|Primarily structured databases
|ELT (Extract, Load, Transform)
|ETL (Extract, Transform, Load)
|Evolves with data needs
|Structured around business objectives
|Highly scalable, petabytes and beyond
|Defined by server and architectural capacity, or by the needs of the users
|Varied, base don storage and processing
|Determined by infrastructure and licensing
Comparing the definitions of data lake vs data warehouse
What is a data lake?
A data lake is a centralized data repository that's designed to store a vast amount of raw data in its native format. This data can be structured, semi-structured or unstructured, which gives the data lake its characteristic flexibility. The philosophy behind a data lake is to have a single store of all data, from source systems to transformed data, that can be used for various business tasks. The idea is to facilitate big data and real-time analytics in a fluid, unstructured environment. The "lake" concept resonates because the data storage plan is fluid, deep and very large.
What is a data warehouse?
A data warehouse is a collection of business data used to help an organization make decisions. This is usually a smaller repository than a data lake, but for some purposes, it can get fairly large. It separates the analytical environment from the transactional environment, which helps ensure that the integrity and performance of operational systems aren't compromised. Data is extracted from heterogeneous sources, transformed to fit operational needs (often through ETL processes), and then loaded into the warehouse. It is highly structured, often by subject, to support the relevant business intelligence (BI) activities. The emphasis in this kind of storage is on data quality, accuracy and consistency and in ensuring that it serves the specific business operation's needs.
Use cases for data lakes vs data warehouses
Data lake use cases:
Big data & real-time analytics: Data lakes excel at taking in large volumes of data and processing it swiftly. As businesses and devices continually produce data, data lakes provide immediate insights, allowing for timely strategies and responses to market dynamics.
Advanced analytics: Data lakes can store diverse data types, facilitating complex tasks such as machine learning and predictive analytics. Businesses that are looking for continued innovation can capitalize on this, using varied data to anticipate market changes or product trends.
Data warehouse use cases:
Business reporting: Data warehouses are structured for consistent, reliable reporting. Multiple departments can access the same data, ensuring unified strategies and accurate insights across an entire organization.
Decision-making tools: Their structured design supports tools such as dashboards and visualization software, offering precise data for decision-making. Executives can quickly spot trends or issues, such as a sales drop and take appropriate action.
Schema of data lakes vs data warehouses
Data lakes employ a "schema-on-read" approach, which means they store a vast array of data in its raw form, and they only apply structure when it's time to read or access this data. This method is flexible and allows businesses to continuously evolve their data models without needing to modify the stored data.
In contrast, data warehouses operate using the "schema-on-write" methodology. With this, data is transformed and structured before it's stored, adhering to a predefined schema. This ensures that the data researchers are working with remains consistent and reliable from the outset. While this method might seem overly rigid, it does offer the benefit of streamlined access and simplified queries, making it suitable for businesses that prioritize data reliability and immediate usability.
Data lakes scope vs data warehouses scope
Data lakes can get very large, up to the petabyte level, and they are capable of accommodating a spectrum of collected data, from granular social media interactions to streams of information from IoT devices. This size allows them to cater to diverse needs, and it makes them a preferred choice for organizations that anticipate data variety and volume growth. This adaptability means data lakes are ideal for dynamic and evolving enterprise applications.
Data warehouses have a more narrow scope. They are designed with specific structures in mind, primarily catering to well-defined operational tasks or departmental requirements. Because of this focus, they ensure a higher degree of consistency and integrity in the data, making them indispensable for functions where accuracy and stability are paramount, such as financial reporting or inventory management.
Who should use data lakes or data warehouses?
Data lakes are particularly well-suited for roles that engage deeply with varied and voluminous data sets. Machine-learning enthusiasts and data scientists benefit from data lakes as they often require a rich mix of structured and unstructured data for their experiments and models.
In contrast, data warehouses have a more structured and consistent environment. This makes them the go-to for business analysts, who need standardized data formats for their analyses. Similarly, decision-makers and executives favor data warehouses because they provide data that’s been cleaned and sorted to ensure that strategic insights can draw on reliable and consistent data, making data warehouses a linchpin in sectors where data consistency and reliability can't be compromised, such as in finance and health care.
Data sources for data lakes and data warehouses
Data sources for a data lake:
Structured databases: Structured databases such as SQL databases can house well-organized data with clear patterns and relations, providing a foundational layer to the data lake.
Web logs: These capture traffic patterns and user behavior on websites, offering insights into user preferences and potential areas of improvement for web platforms.
Social media streams: Data originating from platforms such as Twitter or Facebook, which provide a rich tapestry of user sentiment, trends and public opinion.
IoT data: This encompasses information from interconnected devices, from home thermostats to industrial sensors, reflecting a real-time pulse of machine and user interactions.
Data sources for a data warehouse:
CRM systems: These databases store detailed records of customer interactions, sales histories and customer preferences, enabling businesses to tailor their strategies according to customer needs.
ERPs: Enterprise resource planning systems that integrate various business processes and functions into one complete system, helping to streamline data and enhance decision-making.
Transactional databases: These databases contain detailed records of business transactions, from sales to supply chain movements, ensuring that every business activity is logged and available for analysis.
Processing data: Data lakes and data warehouses
Data lakes adopt the ELT (Extract, Load, Transform) approach. Here, raw data is extracted from sources, directly loaded into the lake and then transformed when it's queried. This flexibility allows businesses to shape data as needed but might introduce latency for on-the-fly transformations.
Conversely, data warehouses use the ETL (Extract, Transform, Load) process. After extraction, data is immediately transformed into a defined structure before storage, ensuring consistency and immediate readiness for analysis. This process guarantees quick query responses but necessitates a clear initial data structure.
Designing a data lake vs data warehouse
Data lakes adopt a bottom-up approach in design. This means they begin as vast repositories accommodating all sorts of raw data. As specific needs and analytics queries arise, structures and processing methods are determined. This approach provides flexibility, allowing organizations to adapt to changing data landscapes and unforeseen requirements.
On the other hand, data warehouses are crafted using a top-down approach. The design starts with an end-goal in mind, often derived from specific business objectives and reporting requirements. As a result, the structure, schema and data intake methods are predetermined, ensuring data consistency, faster query performance and alignment with business goals.
Size comparison of data lake and data warehouse
Data lakes, by their very nature, are intended to be vast reservoirs of data. Accordingly, they can scale horizontally, accommodating large data quantities. This makes them particularly suitable for organizations that generate or consume massive amounts of unstructured or semi-structured data daily. In contrast, data warehouses are structured entities. While they are undeniably robust and can handle substantial data volumes, their size is often influenced by server capacity, architectural considerations and costs. Additionally, because data is cleansed, transformed and indexed before being ingested into a warehouse, there's a built-in limit to how much data can be stored and processed efficiently.
What’s the cost of a data lake vs data warehouse?
Data lakes primarily build on scalable cloud infrastructure and typically have costs tethered to storage volume, data processing and management. As data scales, so does the cost, but modern cloud platforms offer competitive pricing structures, which can make this option generally more affordable. Additionally, due to the flexibility of data lakes, there's potential for unforeseen expenses related to data cleaning and preparation.
Data warehouses come with an upfront cost associated with their infrastructure, especially if deployed on-premises. Licensing fees, especially for proprietary solutions, can rapidly escalate these costs. There are other costs tied to server requirements, which vary based on the scale and complexity of operations. It's also worth noting that maintaining the reliability and performance of a data warehouse may necessitate periodic upgrades or expansions, which incur ongoing costs.
Data lakes vs data warehouses: What are the benefits and challenges?
Benefits and challenges of data lakes
Navigating the landscape of data lakes reveals a unique set of strengths and weaknesses.
Versatility: Data lakes can house myriad data types, including structured and unstructured. This makes them ideal for integrating diverse sources without the constraints of a fixed schema.
Economical scalability: Given the foundation on cloud platforms, it's often more cost-effective to scale data lakes. Storage expansion can be done seamlessly, adjusting to fluctuating data inflows.
Complex processing: With their broad dataset variety, data lakes are perfect for intricate analytical tasks, such as real-time analytics and machine learning processes.
Ensuring data quality: With the influx of heterogeneous data, maintaining consistent quality becomes a challenge. Disparate sources can lead to inconsistent or redundant data.
Implementing robust data governance mechanisms: Managing permissions, ensuring security and maintaining an organized data lake structure requires meticulous planning.
Managing potential retrieval latency: Large-scale datasets, especially when not optimally organized, can result in slow data retrieval times, impacting real-time analytics.
Benefits and challenges of data warehouses
The different structure of data warehouses also shows distinct advantages and shortcomings.
Uniform data: Thanks to their schema-on-write approach, data warehouses ensure a high level of consistency across datasets. This means departments can rely on uniform, standardized data for their insights.
Quick retrievals: Designed with query optimization in mind, data warehouses facilitate swift data access, making them crucial for timely business intelligence tasks.
Business aligned: With a structure that inherently supports business objectives, data warehouses ensure that stored data directly complements operational and strategic goals.
Handling potential data silos: Given their structured nature, data warehouses can inadvertently lead to the creation of data silos, limiting cross-functional insights.
Adapting to evolving data needs: The rigid structure, although beneficial for consistency, can pose challenges when adapting to dynamic business needs or integrating new data sources.
High costs: The initial setup, licensing and maintenance of data warehouses, especially premium solutions, can be capital-intensive.
Choosing a data lake vs data warehouse
Choosing between a data lake and a data warehouse requires you to think about your organization's specific data needs and goals.
A data lake is often preferable for firms engaging with varied data streams, such as IoT or social media feeds. Its flexibility accommodates high-volume, diverse data, making it ideal for cutting-edge analytics and real-time insights. On the other side, businesses that need consistent, structured data for decision-making typically prefer data warehouses. These platforms ensure data uniformity, benefiting enterprises where multiple departments rely on dependable data, such as financial institutions or large retailers.
As the data landscape grows, many organizations are considering hybrid models, merging the benefits of both systems. But choosing the right approach demands a clear understanding of each solution's strengths and constraints.
Cloud alternatives: Data marts, data lakehouses and databases
You're not limited to data lakes and warehouses in the evolving realm of storage and analytics. Several other options, some new and some familiar, are also available:
Data marts: These specialized versions of data warehouses offer a comprehensive, organization-wide solution and are curated to cater specifically to the data needs of individual departments. This tailored approach streamlines data access and improves query speeds for specific teams or projects.
Data lakehouses: These combine the best of both worlds — the vast storage and flexibility of data lakes with the structured, query-optimized environment of data warehouses. Such a hybrid solution ensures efficient analytics while maintaining the rigor and consistency of structured data storage.
Databases: Traditional databases primarily focus on managing structured data. They are optimized for handling well-defined datasets, such as customer details or inventory records, and might not be suitable for complex analytics or handling vast amounts of unstructured data.
As data needs grow and diversify, organizations must understand the nuances of each solution to determine which best aligns with their goals and infrastructure.
Using data lakes at Capital One
As one of the world's largest financial institutions, we’re focused on harnessing the power and versatility of data to refine our operational and analytical strategies. By making use of different data storage solutions, we’re able to manage the vast amount of data that flows through the enterprise. Learn more about structuring data lakes by reading our blog on data lake architecture, a practical and insightful deep-dive into the company's methodologies.