Data Lake vs. Data Warehouse: What’s the Difference?

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Data lakes and data warehouses are more different than they are similar. Do you know what the key differences are? Find out here.

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Data lakes and data warehouses are both storage systems for big data used by data scientists, data engineers, and business analysts. But while a data warehouse is designed to be queried and analyzed, a data lake (much like a real lake filled with water) has multiple sources (tributaries, or rivers) of structured and unstructured data that flow into one combined site.

The two storage systems serve different purposes, so different job roles work with each of them. 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.

Read on to learn the key differences between a data lake and a data warehouse.

Data lake vs data warehouse: Key differences

The key differences between a data lake and a data warehouse are as follows [1, 2]:

ParametersData LakeData Warehouse
Data typeRaw (all types, no matter source of structure)Processed (data stored according to metrics and attributes)
Data purposeTo be determinedCurrently being used
ProcessExtract Load Transform (ELT)Extract Transform Load (ETL)
Schema positionAfter data storage, to offer agility and easy data captureBefore data storage, to offer security and high performance
UsersData scientists, those who need in-depth analysis and tools (such as predictive modeling) to understand itBusiness professionals, those who need it for operations
AccessibilityAccessible and easy to updateComplicated to make changes
HistoryRelatively new for big dataThe concept has been around for decades

To learn more, check out this video from Google’s Modernizing Data Lakes and Data Warehouses with Google Cloud:

What is a data lake?

A data lake is a storage repository designed to capture and store a large amount of structured, semi-structured, and unstructured raw data. Once it’s in the data lake, the data can be used for machine learning or artificial intelligence (AI) algorithms and models, or it can be transferred to a data warehouse after processing. 

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Data lake examples

Data lakes can be used in a variety of sectors by data professionals to tackle and solve business problems.

  • 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 actually 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: This 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.

What is a data warehouse?

A data warehouse is a centralized repository and information system used to develop insights and inform decisions with business intelligence. 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 such as data extraction, cleaning, transformation, and more.

Data warehouse examples

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 companies 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.

Get started with Coursera

Start your career as a data warehouse engineer today. Enroll in IBM’s Data Warehouse Engineering professional certificate to learn all about SQL statements and queries, how to design and populate data warehouses, and more. Earn your professional certificate in eight months or less.

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Relational Database (RDBMS), Business Intelligence (BI), Enterprise Data Warehouse (EDW), SQL, Extract Transform Load (ETL), Data Science, Database (DBMS), NoSQL, Database (DB) Design, Database Architecture, Postgresql, MySQL, Relational Database Management System (RDBMS), Cloud Databases, Python Programming, Jupyter notebooks, Shell Script, Bash (Unix Shell), Linux, Database Servers, Relational Database, Database Security, database administration, Extraction, Transformation And Loading (ETL), Apache Kafka, Apache Airflow, Data Pipelines, Data Warehousing, Cube and Rollup, Star and Snowflake Schema, cognos analytics

Article sources

1. Guru99. “Data Lake vs Data Warehouse: What’s the Difference?, https://www.guru99.com/data-lake-vs-data-warehouse.html.” Accessed August 4, 2022.

2. Talend. “Data Lake vs Data Warehouse, https://www.talend.com/resources/data-lake-vs-data-warehouse/.” Accessed August 4, 2022.

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