This tutorial will explain the differences between Data warehouse vs Data lake. Let’s start with the basics discuss “What is Data Warehouse?”
What is Data Warehouse?
Data Warehouse combines a variety of technologies and components to enable strategic data to use. It gathers and manages data across multiple sources to deliver business insights. It’s the electronic storage of large amounts of information that can be used for analysis and query purposes, rather than transaction processing. It’s the process of turning data into information.
What is Data Lake?
A Data Lake can be used to store large amounts of structured, semi-structured, and unstructured data. You can store any type of data in the native format without any fixed file size limits. It allows for greater analytical performance and native integration.
Data Lake is a large container that is similar to real lakes and rivers. Like a lake, there are many tributaries that come in; similarly, data lakes have structured and unstructured data flowing in real-time.
Data Warehouse Concept
Data Warehouse stores data as files or folders. This allows you to organize the data and make strategic decisions. The storage system provides a multi-dimensional view that includes summary and atomic data. These are the most important functions that you need to perform:
- Data extraction
- Data Cleaning
- Data Transformation
- Data Loading & Refreshing
Next, we’ll discuss the differences between Azure data lake and Azure data warehouse.
- Data Lake stores all data, regardless of source or structure. Data Warehouse stores quantitative metrics.
- Data Lake is a repository for large, structured, semi-structured, and unstructured data. Data Warehouse is a blending of technologies that allow the strategic use and preservation of data.
- Data Lake is the schema that is created after the data is stored, while Data Warehouse is the schema that is created before the data is stored.
- Data Lake uses ELT (Extract Load Transform), while Data Warehouse uses ETL (Extract Transform Load).
- Data lake vs Warehouse: Data lake is for people who need in-depth analysis, whereas Data Warehouse can be used for operational purposes.
Data Lake Concept
A Data Lake can be described as a large storage repository that stores large amounts of raw data in their original formats. Each element of a Data Lake has a unique identifier. It also has extended metadata tags. It provides a wide range of analytical capabilities.
Key Difference between the Data Lake VS Data Warehouse
Here is the difference between data lake and data warehouse.