Data lakes and data warehouses have several key differences. Can you tell them apart? Read this guide to learn what they are and more.
Data lakes and data warehouses are storage systems for big data used by data scientists, data engineers, and business analysts. Despite their similarities, though, they're more different than they are similar, and understanding these key differences is important for any aspiring data professional.
In this article, you'll learn all about data lakes and data warehouses, including what they are, how they differ from one another, and what they're used for. At the end, you'll even explore recommended courses to deepen your understanding of these two important data storage systems.
Data lakes, much like real lakes, have multiple sources ("rivers") of structured and unstructured data that flow into one combined site. Data warehouses are designed to be repositories for already structured data to be queried and analyzed for very specific purposes.
For some companies, a data lake works best, especially those that benefit from raw data for machine learning. For others, a data warehouse is a much better fit because their business analysts need to decipher analytics in a structured system.
The key differences between a data lake and a data warehouse are as follows [1, 2]:
Parameters | Data Lake | Data Warehouse |
---|---|---|
Data type | Raw (all types, no matter source of structure) | Processed (data stored according to metrics and attributes) |
Data purpose | To be determined | Currently being used |
Process | Extract Load Transform (ELT) | Extract Transform Load (ETL) |
Schema position | After data storage, to offer agility and easy data capture | Before data storage, to offer security and high performance |
Users | Data scientists, those who need in-depth analysis and tools (such as predictive modeling) to understand it | Business professionals, those who need it for operations |
Accessibility | Accessible and easy to update | Complicated to make changes |
History | Relatively new for big data | The concept has been around for decades |
To gain a deeper introduction to data lakes, check out this video from Google:
A data lake is a storage repository designed to capture and store a large amount of all types of raw data. The data can be structured, semi-structured, and unstructured. Once it’s in the data lake, the data can be used in machine learning or artificial intelligence (AI) algorithms and models for business purposes. It can also be transferred to a data warehouse after processing.
Data lakes can be used in a variety of sectors by data professionals to tackle and solve business problems. Some examples include:
Marketing: Marketing professionals can collect data on their target customer demographic’s preferences from many different sources in a data lake. Platforms such as Hubspot store data in data lakes and then present it to marketers in a shiny interface. Data lakes enable marketers to analyze data, make strategic decisions, and build data-driven campaigns [2].
Education: The education sector has begun using data lakes to track data on grades, attendance, and other performance metrics so that universities and schools can improve their fundraising and policy goals. A data lake provides the right amount of flexibility to handle these types of data.
Transportation: A data lake is used when data scientists of airline and freight companies cut costs and increase efficiency to support lean supply chain management.
A data warehouse is a centralized repository and information system used to develop insights and inform decisions with business intelligence. Like an actual warehouse, data gets processed and organized into categories to be placed on its "shelves" that are called data marts.
Data warehouses store organized data from multiple sources, such as relational databases, and employ online analytical processing (OLAP) to analyze data. The warehouses perform functions on the data such as extraction, cleaning, transformation, and more.
professional certificate
Launch your career in Human Resources. In this program, you’ll learn in-demand skills for a career as an Human Resource Associate. No degree or prior experience needed. Coursera's 2024 Learners First Award Winner.
4.8
(2,005 ratings)
84,997 already enrolled
Beginner level
Average time: 5 month(s)
Learn at your own pace
Skills you'll build:
Employee Relations, Training development, Performance Management, Recruitment, Compliance strategy, Benefit types, Compensation strategy, Pay systems, Total rewards, Business Continuity, Employee Engagement, Learning Delivery Methods, Effective Training, Training Needs, Learning Models, Legal Compliance, Risk Management, Safety Compliance, Compliance Implementation, Employee Onboarding, Job Analysis, interviewing
Data warehouses provide structured systems and technology to support business operations. Some examples include:
Finance and banking: Financial companies can use data warehouses to provide company-wide access to the data. Rather than using Excel spreadsheets to create reports, a data warehouse can generate reports that are secure and accurate, saving companies time and money.
Food and beverage: Big conglomerates (think Nestlé and PepsiCo) turn to high-performance enterprise data warehouse systems that enable them to run operations, consolidating sales, marketing, inventory, and supply chain data all in one place.
Start your career as a data warehouse engineer today.
In IBM’s Data Warehouse Engineering professional certificate, you'll learn all about SQL statements and queries, how to design and populate data warehouses, and more. Earn your professional certificate in three months or less.
Amazon's Introduction to Designing Data Lakes on AWS course, meanwhile, will help you understand how to create and operate a data lake in a secure and scalable way, without previous knowledge of data science.
professional certificate
Get on the fast track to a career in cybersecurity. In this certificate program, you'll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.
4.8
(39,136 ratings)
840,197 already enrolled
Beginner level
Average time: 6 month(s)
Learn at your own pace
Skills you'll build:
Python Programming, Security Information and Event Management (SIEM) tools, SQL, Linux, Intrusion Detection Systems (IDS), Packet Analyzer, Security Hardening, Network Security, Transmission Control Protocol / Internet Protocol (TCP/IP), Network Architecture, Cloud Networks, escalation, resume and portfolio preparation, stakeholder communication, Job preparedness, integrity and discretion, Cybersecurity, Information Security (INFOSEC), Ethics in cybersecurity, NIST Cybersecurity Framework (CSF), Historical Attacks, Computer Programming, Coding, PEP 8 style guide, NIST Risk Management Framework (RMF), Security Audits, Incident Response Playbooks, Authentication, vulnerability assessment, Cryptography, asset classification, threat analysis, Command line interface (CLI), Bash
Guru99. “Data Lake vs Data Warehouse: What’s the Difference?, https://www.guru99.com/data-lake-vs-data-warehouse.html.” Accessed December 20, 2023.
Talend. “Data Lake vs Data Warehouse, https://www.talend.com/resources/data-lake-vs-data-warehouse/.” Accessed December 20, 2023.
Editorial Team
Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work.
These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising.
These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance.
These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly.