The main difference between data integration and ETL is that the data integration is the process of combining data in different sources to provide a unified view to users whereas ETL is the process of extracting, transforming and loading data into a data storage environment.
Data integration refers to combining data from disparate sources into valuable and meaningful information. Therefore, a complete data integration solution delivers reliable data from different sources. It is an important process when multiple systems are merged and applications are consolidated to provide a unified view of data. On the other hand, ETL is a process that is followed before data is stored in a data warehouse. It involves extracting, transforming and loading data.
Key Areas Covered
1. What is data integration?
– Definition, Functionality
2. What is ETL?
– Definition, Functionality
3. What is the difference between data integration and ETL?
– Comparison of key differences
What is data integration?
Data integration is the process of combining data located in different sources to provide a unified view to users. However, data integration varies from application to application. In a commercial application, two organizations may combine their databases. In a scientific application such as a bioinformatics project, research results from multiple repositories can be combined into a single unit.
Figure 1: Data Integration
Also, a common use of data integration is to analyze big data that requires sharing large data sets across data warehouses. In general, data integration is a difficult process. Furthermore, it requires enough generality to fit various integration systems, such as relational databases, XML databases, etc.
What is ETL?
A data warehouse is a system that helps analyze data, create reports, and visualize it. Managers, data analysts, business analysts can analyze this data to make business decisions. There are three steps to take before storing data in a data warehouse. It’s called ETL. It involves extracting, transforming, and loading data into the data warehouse.
There are multiple data sources in an organization. The first step is to extract data from these different sources. However, the data extraction should not affect the performance or response time of the original data source. Full extraction and partial extraction are two methods of extracting data.
The second step is transformation. Here, the extracted data is usefully cleaned, mapped and converted. Data selection, mapping, and data cleaning are some basic transformation techniques. Additionally, there are some advanced data transformation techniques as well. They are standardization, character set conversion and encoding handling, field splitting and merging, summarizing, and deduplication.
The last step is to retrieve the prepared data and store it in the data store. It’s called charging. Here, the load can be an initial load, incremental load, or a full update. The initial load is loading the database for the first time. Incremental loading consists of applying the changes that are required periodically, while the full update consists of removing data from one or more tables and reloading with new data.
Difference Between Data Integration and ETL
Data integration is the process of combining data that resides in different sources and giving users a unified view of it. ETL is a three-step extract, transform, and load function that occurs before data is stored in the data warehouse. Hence, this is the main difference between data integration and ETL.
Scientific and business applications use data integration, while data warehousing is an application that uses ETL. This is another difference between data integration and ETL.
The difference between data integration and ETL is that data integration is the process of combining data from different sources to provide a unified view to users, while ETL is the process of extracting, transforming, and loading data in one environment. data storage..
1. “Data Integration”. Wikipedia, Wikimedia Foundation, October 4, 2018, Available here.
2. “Data integration”. Data integration | Data integration information, available here.
3. vtakkar. 3 – ETL Walkthrough | Extract Transformation and Charge, Vikram Takkar, September 8, 2015, Available here.
1. “Data integration (KAFKA) (Case 3)” By Carlos.Franco2018 – Own work (CC BY-SA 4.0) via Commons Wikimedia
2. “Datawarehouse reference architecture” By DataZoomers – (CC BY-SA 4.0) via Commons Wikimedia