The Data Warehouse system (data warehouse) is a familiar technology used by businesses, helping them take advantage of the vast potential of Big Data. However, a new data storage technology – Data lake – is creating a change in the way businesses access and use data.
Viewing: What is a data warehouse?
To avoid confusion and distinguish the two concepts above, we need to define both technologies first.
What is a data lake
A data lake is a central repository that holds large amounts of raw data kept for use as needed. Because the data is kept original, businesses do not need to invest in transforming, classifying and storing data until it needs to be used.
A data warehouse is also a data store for businesses, whose primary purpose is to provide reporting and data analysis. Stored data sometimes has to go through extraction, transformation and processing through the ETL (Extract – Transform – Load) process before being imported into the archive.
Difference between Data lake and Data warehouse
Simply put, a data warehouse transforms and categorizes data from different sources within the enterprise. This data will be available for other purposes, especially reporting and analysis.
Data lake stores unanalyzed data and keeps it in its raw state. These data need to be processed further as needed.
Each technology has its own method of processing data and providing different results.
1. Data Types
As mentioned, a data warehouse consists of data extracted from transaction systems and quantitative metrics to support business performance and health analysis. A data warehouse needs a well-structured data model that identifies the data to be stored and eliminates unnecessary data.
In Data lake, all types of data from system sources are stored. Includes data sources that may be denied storage in the Data warehouse, such as web server logs, sensor data, social media activity, text and images, and more.
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A data lake can even store data that is not currently in use but may be needed in the future. This is made possible by low-cost storage solutions like Hadoop.
2. Schema form
The data warehouse applies the “Schema on Write” method, which means that the model is designed for the main purpose of providing reports. This process requires a significant time investment to analyze data sources, understand business processes, categorize data, and form a defined system for storing data.
Data lake keeps data in its original state; When there is a need to use data to solve business problems, only relevant data is selected and analyzed to provide answers. This approach is called “Schema on Read”, which saves businesses time and money.
Since a Data warehouse is a highly structured data warehouse, it is very expensive to change the structure according to the needs of the company. The change process requires many complex, time consuming and expensive processes.
Data lake, on the other hand, takes advantage of the flexibility of data, because the data is stored in its raw form and is always easily accessible, allowing for unhindered refactoring.
Data warehouse is familiar to businesses and users, easily meeting needs such as making performance reports, metrics, as well as data statistics. With a tight structure, easy to use and mainly used to answer user queries, Data warehouse meets the needs of the business.
Data lake is more suitable for users who perform in-depth analysis like data scientists. With so many different types of data in the data lake, they have the ability to combine different types of data and raise entirely new questions that need to be answered.
Who is the data lake for
?Based on the nature and capabilities of each, a Data warehouse seems to be a better choice for businesses that want to leverage data. Meanwhile, Data lake allows users to fully exploit the possibilities that data can bring, however, this can be a difficult task for ordinary users with insufficient advanced skills. .
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To be sure, both of these data storage technologies will continue to evolve. As well as the ability for vendors to develop a hybrid solution aimed at making data use faster, more flexible, and more reliable.
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