Data Lake vs Data Warehouse: What's the Difference?
In this tutorial on the difference between Data lake vs. Data warehouse, we will discuss the key differences between Data warehouse vs data lake. But before discussing the difference, let us first learn “What is Data Warehouse?”.
Data Warehouse is a blend of technologies and components for the strategic use of data. It collects and manages data from varied sources to provide meaningful business insights. It is the electronic storage of a large amount of information designed for query and analysis instead of transaction processing. It is a process of transforming data into information.
A Data Lake is a storage repository that can store a large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers a large amount of data quantity for increased analytical performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake, you have multiple tributaries coming in; similarly, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Data Warehouse stores data in files or folders which helps to organize and use the data to take strategic decisions. This storage system also gives a multi-dimensional view of atomic and summary data. The important functions which are needed to perform are:
- Data Extraction
- Data Cleaning
- Data Transformation
- Data Loading and Refreshing
Next, we will learn the key difference between Azure data lake vs data warehouse.
- Data Lake stores all data irrespective of the source and its structure whereas Data Warehouse stores data in quantitative metrics with their attributes.
- Data Lake is a storage repository that stores huge structured, semi-structured and unstructured data while Data Warehouse is blending of technologies and component which allows the strategic use of data.
- Data Lake defines the schema after data is stored whereas Data Warehouse defines the schema before data is stored.
- Data Lake uses the ELT(Extract Load Transform) process while the Data Warehouse uses ETL(Extract Transform Load) process.
- Comparing Data lake vs Warehouse, Data Lake is ideal for those who want in-depth analysis whereas Data Warehouse is ideal for operational users.
A Data Lake is a large size storage repository that holds a large amount of raw data in its original format until the time it is needed. Every data element in a Data lake is given a unique identifier and tagged with a set of extended metadata tags. It offers wide varieties of analytic capabilities.
Here are key differences between data lakes vs data warehouse:
|Parameters||Data Lake||Data Warehouse|
|Storage||In the data lake, all data is kept irrespective of the source and its structure. Data is kept in its raw form. It is only transformed when it is ready to be used.||A data warehouse will consist of data that is extracted from transactional systems or data which consists of quantitative metrics with their attributes. The data is cleaned and transformed|
|History||Big data technologies used in data lakes is relatively new.||Data warehouse concept, unlike big data, had been used for decades.|
|Data Capturing||Captures all kinds of data and structures, semi-structured and unstructured in their original form from source systems.||Captures structured information and organizes them in schemas as defined for data warehouse purposes|
|Data Timeline||Data lakes can retain all data. This includes not only the data that is in use but also data that it might use in the future. Also, data is kept for all time, to go back in time and do an analysis.||In the data warehouse development process, significant time is spent on analyzing various data sources.|
|Users||Data lake is ideal for the users who indulge in deep analysis. Such users include data scientists who need advanced analytical tools with capabilities such as predictive modeling and statistical analysis.||The data warehouse is ideal for operational users because of being well structured, easy to use and understand.|
|Storage Costs||Data storing in big data technologies are relatively inexpensive then storing data in a data warehouse.||Storing data in Data warehouse is costlier and time-consuming.|
|Task||Data lakes can contain all data and data types; it empowers users to access data prior the process of transformed, cleansed and structured.||Data warehouses can provide insights into pre-defined questions for pre-defined data types.|
|Processing time||Data lakes empower users to access data before it has been transformed, cleansed and structured. Thus, it allows users to get to their result more quickly compares to the traditional data warehouse.||Data warehouses offer insights into pre-defined questions for pre-defined data types. So, any changes to the data warehouse needed more time.|
|Position of Schema||Typically, the schema is defined after data is stored. This offers high agility and ease of data capture but requires work at the end of the process||Typically schema is defined before data is stored. Requires work at the start of the process, but offers performance, security, and integration.|
|Data processing||Data Lakes use of the ELT (Extract Load Transform) process.||Data warehouse uses a traditional ETL (Extract Transform Load) process.|
|Complain||Data is kept in its raw form. It is only transformed when it is ready to be used.||The chief complaint against data warehouses is the inability, or the problem faced when trying to make change in in them.|
|Key Benefits||They integrate different types of data to come up with entirely new questions as these users not likely to use data warehouses because they may need to go beyond its capabilities.||Most users in an organization are operational. These type of users only care about reports and key performance metrics.|