Data is everywhere in the form of values, text, numbers, pictures and so on that can be stored and used anytime when required. The importance of data and data storage systems has gained recognition since businesses find the potential of big data and its use cases.
The data was with us always, but the possibilities to use it effectively were an arduous task. More of the time, data was in an unstructured form that hindered the effective use of data, and was not easy to filter out useful data from the raw.
Since technology has grown enough to utilize data in all aspects and store data at any level of volume, the possibilities to evaluate data become expanded. The developments of databases, data warehouses, and data lakes have upsurged the opportunity to store data in any volume and utilize it when needed effectively.
Though we have multiple landscapes to store and use data, all of that is not the same, in this blog, we will explore the key difference between database, data warehouse, and data lake.
Database is a storage location where data will be stored in a structured pattern. In a database, the data is easy to access and edit by the user. Data stored in a computer or a smartphone is an ideal example to understand how a database works. Plus, a database can be used in multiple ways such as for data storage, data management, data processing, and data evaluation.
The use cases of a database are diverse for different sectors such as storing and processing data regarding financials, small dataset analysis, data evaluation for business processing, data auditing, and much more. There are many database facilitators in the market to say, MongoDB, Apache Cassandra, Elasticsearch, Oracle
But, the volume of data that can be stored in a database has a limit. So, big business firms and enterprises level users are not a fan of databases but they have data warehouses and data lakes.
Data Warehouse is an upgraded version of a database with more storage and processing capacity which can serve the purpose for enterprises. It can accommodate larger volumes of data from multiple sources as well as make it accessible across an organization more making a foundation for business intelligence and analysis for informed decisions.
It is more like structured as well as oriented data in larger dimensions. Similar to sorting data in an excel sheet data will be stored in a data warehouse with named columns. Users can add new entries with varying difficulty levels based on the existing data volume and structure. The data stored in a data warehouse is easily visualized and measured as well.
Data lake is a huge data repository that can store any amount of data in its original format. It is a low-cost data storage option for enterprises that need to be refined when needed. Unlike, data warehouses, data lake stores, semi-structured, unstructured, and raw data.
The architecture in the data lake follows schema-to-read methodology while the data warehouse follows the schema-to-write method. This is because data lake follows the extract-load-transform approach for storing data. Apache Hadoop is a very example of a data lake that uses for storing huge data volumes of different classes.
However, the data stored in a data cannot be used directly by the business leaders or management, it needs an expert data scientist to transform it in a useful way. Plus, enterprises that use data lake have the freedom to have a flexible business strategy because data can be accessed and transformed based on the business needs at any time. Also, by storing data on data lakes companies can get up-to-date and accurate data analysis.
Hope you have understood the critical difference between database, data warehouse, and data lake. Based on your business model driven by data, you can use any one model or a mix of all these data storage methods. Reach ZiniosEdge to know more about data management for your business.