Data Lake vs. Data Warehouse: 6 Key Differences
Data storage is a big deal as around 60% of corporate data is stored in the Cloud. Considering big data collection is significant in determining a business’s success, companies must invest in data storage. Data Warehouse Data lakes and warehouses are pivotal options for ample data storage, but they vary in architecture, processing, user groups, and objectives. Data Warehouse
Before delving into comparisons, let’s discuss them one by one.
What is a Data Lake?
A data lake is a storage repository designed to gather and store vast amounts of raw data. This can be semi-structured, unstructured, and structured. Once in the data lake, the data can be used in artificial intelligence, machine learning models, and algorithms for business objectives. It can be moved to a Data Warehouse post-processing.
Data Lake Examples
Data professionals use data lakes in multiple sectors to overcome and resolve business concerns. For example,
- Education: Data lakes are now being used in the education sector to monitor data about grades, attendance, and various performance indicators. This empowers universities and schools to enhance their fundraising and policy goals. Data lake offers flexible solutions to handle these types of data.
- Transportation: Data lakes help make predictions once data is processed for AI, machine learning, and predictive analytics. It increases efficiency and provides cost-cutting benefits to support lean supply chain management.
- Marketing: Data lakes empower marketing experts to gather data about their targeted customer demographic from diverse sources. Platforms like HubSpot store this data within data lakes and then offer it to marketers in a shiny interface. Data lakes allow marketers to analyze information, make strategic decisions, and design data-driven campaigns.
What is a Data Warehouse?
A Data Warehouse is a central repository and information platform used to derive insights and inform decisions with business intelligence. Like a physical warehouse, data undergoes processing and categorization before being placed on its “shelves,” known as data marts.
Data Warehouses store organized data from relational databases and employ OLAP to analyze data. It performs functions on the data like transformation, cleaning, extraction, and others.
Data Warehouse Examples
Data Warehouses offer structured technology and systems to support business operations. For example,
- Finance and banking: Financial companies can use Data Warehouses to provide data access across the company. Rather than using Excel spreadsheets to generate reports, a Data Warehouse can generate secure and accurate reports, saving businesses time and costs.
- Food and beverage: Major corporations (such as Nestlé and PepsiCo) use advanced enterprise Data Warehouse systems to manage operations efficiently, consolidating sales, marketing, inventory, and supply chain data all on one platform.
Top 6 Differences Between a Data Lake and Data Warehouse
- Data structure: Raw data represents data that has not been processed and tends to be unstructured or semi-structured (like images with attached metadata). The primary difference between data lakes and Data Warehouses lies in raw versus processed data structure. Data lakes store raw, unprocessed data, including multimedia and log files, while Data Warehouses store refined, processed, and structured data, typically texts and numbers.
Due to this, data lakes need a much larger storage capacity than Data Warehouses. Raw data’s flexibility allows quick analysis, making it ideal for machine learning. However, inadequate data quality and governance measures can transform data lakes into data swamps, posing a potential risk for businesses.
- Users: Data from a data lake with enormous unstructured data is used by engineers and data scientists who wish to explore data in its raw state to uncover new and unique business insights.
Whereas data from a Data Warehouse is used by business-end users and managers who wish to extract insights from business KPIs. This is because it has structured data to address pre-determined questions for analysis.
- Schema approach: The schema is often developed after the data has been saved for a data lake. This offers simple data acquisition and high agility; however, the process should be finished with effort.
For a Data Warehouse, schema is defined before storing the data. It demands work at the start of the process but provides integration, security, and performance.
- Accessibility: Accessibility and user-friendliness refer to the entire data repository rather than its components. Data lake architecture is notably less rigid and, hence, has fewer constraints.
In contrast, Data Warehouses are inherently more organized by design. A significant advantage of Data Warehouse architecture lies in the streamlined processing and structure of data, which enhances its readability. However, the rigid structure also makes it difficult and costly to manipulate.
- Data processing methods: Data lakes support conventional ETL (extract, transform, and load) processes but most commonly use ELT (extract, load, and transform), in which data is loaded as is and transformed for specific uses.
In Data Warehouses, ETL processes are common for data integration and preparation. The data structure is finalized before loading datasets to support the intended BI and analytics applications.
- Business benefits: Data lakes allows data science teams to analyze varying sets of structured and unstructured data and design analytical models which offer insights for business decisioning and strategic planning.
Data Warehouses offer a central repository of integrated and curated data sets that can be easily accessed and used to analyze business activities and support operational decisions.
Conclusion
Choosing a data lake or warehouse is paramount in today’s data landscape. Based on your company’s data needs and analytical requirements, embracing flexibility for diverse data types with a data lake or harnessing structured processing power through a Data Warehouse will be instrumental in growth.
