Data lake vs. data warehouse
Data lakes store raw data in their native format, regardless of how they arrive. Data warehouses store data that has been cleaned and structured in a predefined way.
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Introduction to data lakes and data warehouses
Data lakes and data warehouses are systems that store, manage, and retrieve large volumes of digital data. Businesses collect data to help them gain insights into their operations, customers, markets, and supply chains so they can respond more strategically.
Data warehouses emerged as a solution to break down data silos and address the challenge of business data scattered across multiple systems, formats, and departments.
The inconsistency made it difficult for users to access, integrate, and analyze this data to spot patterns, forecast demand, or evaluate business performance. Data warehouses were developed to consolidate this data into a centralized repository, where data could be integrated, cleaned, and structured for analysis. This approach established a “single source of truth” to support compliance, performance monitoring, and business intelligence processes.
Data lakes, in turn, emerged as a solution to the limitations of data warehouses, which could not adequately handle the explosion of unstructured and semi-structured data generated from new sources like social media, IoT devices, sensors, mobile apps, and more. Storing and processing immense amounts of diverse data, such as images, video, and text, proved too expensive and inefficient, as traditional data warehouses initially required data to be cleaned and processed upfront and before storage.
Businesses needed a more flexible, low-cost way to store data in its raw, original format, and data lakes were created as the solution.
Today, many modern enterprises adopt a hybrid approach involving both data warehouses and data lakes: the data lakehouse. This architecture provides both the fast, structured reporting capabilities of the former and the potential for AI and machine learning applications of the latter.
Data lakes vs. data warehouses: key differences
The key difference between data lakes and data warehouses is in the type of data they store and how they store that data, both of which play a key role in an organization’s data strategy.
Data warehouses store structured data that’s been cleaned and processed according to a predefined structure, or schema. Because the schema is applied before the data is stored, the approach is known as schema-on-write.
For example, a schema may mandate that customer ID data must be an integer, order date data must be in YYYY-MM-DD format, and that total sale amount data must be in decimal format. Because all data adheres to these rules, users can ask queries like “find the total sales per customer in April 2025” quickly and reliably. This speed and accuracy make data warehouses ideal for reporting, dashboards, and business intelligence use cases.
In contrast, data lakes can store raw data in their original format regardless of how they’re structured. No predefined schema is required upfront.
The schema is only defined when the data is queried, so the approach is known as schema-on-read. Only then is the raw data parsed, structured, and interpreted according to the query.
To summarize, data warehouses apply a schema before storing data to ensure all data is structured and cleaned for use. Data lakes apply schema when the data is queried and can store any data, structured or not, from the start.
Differences between data lakes and data warehouses
Choosing between data lakes vs. data warehouses
Since data lakes can store raw data in any format, they are ideal for businesses that need flexibility. Retailers, for example, collect massive amounts from multiple sources, such as websites, mobile apps, social media, point of sale systems, and others. Because the data they collect doesn’t need to be cleaned, transformed, or structured, they can use more cost-effective storage systems that scale easily. However, the cost of processing raw data at query time can be higher compared to a data warehouse’s optimized queries.
In comparison, costs will be higher with data warehouses. The cleaning, transforming, and structuring processes before loading—as well as indexing and partitioning after loading—require additional resources and storage to work. However, this optimization results in ready-to-use data for business intelligence, reporting, and operating analytics. With data warehouses, analysts and executives can generate reports, monitor KPIs, and make informed decisions quickly and easily.
It must be noted that data lakes do unlock new opportunities for AI and machine learning applications. The vast and varied datasets they store enable data scientists to find trends, build predictive models, and run machine learning applications. This results in, for example, recommendation systems that suggest products to users based on past interactions or natural language processing tools that run sentiment analysis on customer reviews or social media comments.
Today, many modern enterprises run data architectures that are essentially combinations of both. These data lakehouses aim to offer the flexibility of a data lake with the governance and performance of a data warehouse. While adoption is growing quickly, many businesses still rely on traditional warehouses for critical reporting.
Real-world examples and use cases
Here are examples of how different industries use data lakes, data warehouses, or a combination of elements from both to support their unique needs.
Healthcare: Hospitals often use a data lake architecture to store, manage, and analyze the vast amounts and varied types of data their operations generate. This includes unstructured wearable data and medical images, semi-structured HL7 patient data, and structured lab test results. By consolidating it all in a central repository, they can apply advanced analytics and AI to the raw data to, for example, identify patients at risk or analyze genomics to personalize treatment plans. With patients now equipped with “smart” wearable devices that stream data on vital signs, healthcare providers can even detect early warning signs and intervene faster.
Finance: Banks and other financial institutions must comply with anti-money laundering (AML) rules and strict financial reporting regulations (such as Sarbanes-Oxley in the U.S. or Basel III internationally). By using data warehouses to store structured financial data from multiple systems, including transaction records, account balances, and trading data, they can generate regulatory reports that meet governance and security requirements. In addition to compliance, financial institutions also use data warehouses to power their business intelligence, manage risk, and detect fraud by running complex queries across historical and current datasets.
Media: Video streaming services use a data lakehouse approach to collect, store, and analyze user data to deliver personalized experiences. They intake diverse types of data from multiple sources, like streaming logs and social media feedback, and store it in a central repository. This data can then be used to build machine learning models that recommend the most relevant content. The same data can also be curated and structured into subsets for analytics or reporting needs, powering dashboards on retention rates or informing decisions on content acquisitions.
Emerging trends in data platforms
Data lakehouses are fast becoming the preferred option for businesses looking to maximize the value of their data. They can support both business intelligence and AI and machine learning use cases on a single platform. However, it must be noted that they’re still evolving and that some enterprises continue to rely on traditional data warehouses for mission-critical reporting.
The potential of AI as a driver of productivity and efficiency has especially influenced data architectures, with some emerging data lake and data lakehouse platforms now integrated with LLMs. This enables non-technical users to explore and analyze data by asking queries in plain language. For example, a user can ask “show me sales trends in Q2,” and the LLM can generate SQL that the system can understand. This democratizes access to data-driven insights.
Serverless architectures are also emerging as a strategy, where businesses hire a cloud provider to manage their data infrastructure. In this arrangement, a company pays for access to a data platform instead of setting up and managing their own. The pros of this are easier scalability and cost-efficiency. The cloud provider provides flexibility in bandwidth in the event of spikes in data volume or query load, and the business only pays for what they use. This way, developers can deploy more quickly, as they do not have to contend with infrastructure considerations.
Some businesses even opt for a multi-cloud strategy, distributing their data lakes and warehouses across several cloud services. The main benefit is resilience in redundancy. If one cloud goes offline, the business can keep running on another. They can also optimize specific workflows on certain clouds, such as if one service specializes in machine learning. In some industries or countries, sensitive data must be stored in a region or cloud provider that meets local compliance requirements.
To connect, manage, and govern data across multiple cloud environments, businesses can implement data fabric architectures. They provide real-time access to data across separate but synchronized systems and applications, creating a unified view across the landscape.
To protect sensitive data like medical records, social security numbers, and source codes, organizations are also adopting policies like zero-trust access controls in their data platforms. These controls require all users to verify their identity to access the data they need.
FAQs
Data lakes are useful for data scientists who want to train machine learning models that power content recommendation systems.
A data warehouse is a storage system primarily designed to hold large volumes of structured data. Structured data is cleaned, organized, and formatted in a certain way. (Think of the defined rows and columns of a spreadsheet). More modern warehouses can also handle certain semi-structured formats like JSON or XML.
Businesses use data warehouses to answer questions quickly, generate reports, and track key performance metrics. These functions are categorized as business intelligence.
Schemas are rules for how data is organized, such as what kind of data can be stored (numbers, dates), how the data is arranged (tables and columns), and how the information relates to each other.
Schema-on-write means the data must fit into a predefined structure (schema) before being stored. This is how data warehouses work. They ensure the data is clean and ready for analysis upfront.
Schema-on-read means the structure is only applied when someone wants to use or analyze the data. This is how data lakes work. They allow more flexibility since the data can be stored in any form first, and you don’t have to organize it immediately. However, the trade-offs of this approach include slower query times and potential inconsistency, since different users might interpret the same raw data differently.
By contrast, schema-on-write enforces consistency upfront but reduces flexibility.
Structured data is highly organized, easy to search, and can usually be stored in tables, like customer names, sales numbers, and dates.
Unstructured data has no fixed format and is harder to organize, like videos, images, audio files and social media posts.
Semi-structured data is somewhere in between. It has some organization but not as strict as tables. Think JSON files, XML documents, and e-mails.
