What is the Difference Between Data Lake vs Data Warehouse?

Data Lake vs Data Warehouse

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.

Key difference

  • 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.

Parameters Data Lake Data Warehouse
Storage All data, regardless of source or structure, is stored in the data lake. All data is kept in its raw form. Only when data is ready for use can it be transformed. Data warehouses will contain data from transactional systems, as well as data that includes quantitative metrics and their attributes. Data is cleaned up and transformed
History Data lakes are a relatively new use of Big Data Technologies The data warehouse concept is not like big data and has been around for many decades.
Data Capturing All data and structures are captured, both semi-structured as well as unstructured, from the source system. Captures structured data and organizes it in schemas that are defined for data warehouse purposes
Data Timeline All data can be stored in data lakes. All data can be stored in data lakes. This includes data currently in use and data that might be used in the future. Data is also kept forever to allow for analysis and backup. Analyzing data sources is a significant part of the process of developing a data warehouse.
Users Deep analysis is possible with Data lake. These users include data scientists, who require advanced analysis tools that can perform predictive modeling and statistical analysis. Because it is well-structured, easy-to-use, and understandable, the data warehouse is ideal to support operational users.
Storage Prices Big data storage is cheaper than storing data in a warehouse. Storing data in a Data Warehouse is more expensive and takes longer.
Task Data lakes can house all data and types. It allows users to access data before it is transformed, cleansed, and structured. Data warehouses provide insight into pre-defined questions and pre-defined data types.
Processing Time Data lakes allow users to have access to data even before it is structured, transformed, or cleansed. Data lakes allow users to access their data faster than traditional data warehouses. Data warehouses provide insight into pre-defined questions and pre-defined data types. Any changes to the data warehouse would take more time.
Schema The schema is usually defined after the data has been stored. This allows for high flexibility and data capture, but it requires some work at the end. Schema is usually defined before data is stored. This requires work, but it offers integration, security, performance, and integration.
Data processing Data Lakes uses the ELT process (Extract Load Transform). Data warehouse employs an ETL (Extract Transform Load), process.
Complain Data is stored in its original form. Only when data is ready for use can it be transformed. Data warehouses are often criticized for their inability to change, or the difficulty in making changes in them.
Key Benefits They combine different types of data to create new questions. These users are unlikely to use data warehouses as they might need to go beyond their capabilities. The majority of users within an organization are operational. These users are focused on key performance metrics and reports.

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