The realm of data management is vast. Concepts can intertwine, and one can easily confuse primary functions. Data integration and ETL (Extract, Transform, Load) are some of the most crucial data-management methods.
Each involves distinct properties that help bring data from multiple sources together. Let’s dig deeper into ETL vs. data integration and explore the essence of the two concepts.
What Is Data Integration?
In essence, data integration stands for the operation of collecting and merging multiple-source data into one place. The process translates to accumulating data from different outlets, cleaning it, and transforming it into readable, usable, and manageable data stored in a main repository. Business requirements like making decisions, data analysis, creating reports, and more, can benefit from data integration.
Example Use Cases for Data Integration
In general, data integration is utilized when the need to merge data from different sources arises. For instance, you can use this method to integrate data and create reports that offer insight into an organization or a business.
Data integration is perfect for tracking performance, pinpointing trends, etc. Here are some common use cases of data integration:
- Analysis: Organizations and businesses can use data integration to analyze differently-structured data from many outlets. This approach is perfect for performing market research, predictive analytics, etc.
- Data warehousing: Data integration is also commonly used to build and maintain a data warehouse. In other words, data integration means compiling data into a central repository with the goal of analyzing and reporting.
- Decision-making: Having the right information at hand can help make the right decision. Data integration helps businesses and organizations with allocation, strategic planning, and product development, among other instances. In essence, those who use data integration can reach the best business decisions based on facts.
Types of Data Integration
Data integration encompasses a wide range of activities. It can be divided into several types, which we’ve detailed below.
Consolidating data means combining and storing different data in a single place. Data consolidation allows the user to navigate different amounts of data from a single access point and transform raw data into readable information.
Data federation is a method used to query data from distinct outlets for digital rendition. It enhances the process of data integration.
Generally, data propagation means allocating data from one or multiple source data warehouses to a preferred database with local access.
Extract, Transform, Load (ETL)
ETL is a specific type of data integration. This process involves extracting, transforming, and loading data from numerous outlets into a designated data warehouse or another data repository.
What Is ETL?
As mentioned, ETL is a tool that extracts, transforms, and loads data into data repositories. ETL is vital to the success of reporting, analytics, and as of recently, machine learning processes. Today, AI also relies on ETL tools.
Example Use Cases for ETL
Common ETL uses include the following:
- Data warehousing: ETL is frequently used to maintain and populate repositories. An ETL tool extracts data from sources, transforms it into a suitable format, and loads it into the data warehouse.
- Data migration: ETL tools are perfect for mitigating data between data systems. The best uses are for cloud and on-site data systems.
- Business intelligence: Commonly, ETL is used as an inseparable part of business intelligence to extract data from multiple data outlets, transform data into a format that works for data analysis, and load new data into a dashboard or other reporting asset.
Types of ETL
The ETL process can be distinguished based on several types.
In general, these are ETL tools created by commercial organizations. Enterprise ETL tools are the most advanced and powerful data extraction tools today.
Most open-source ETL tools are free to use and are excellent add-ons for sharing data and staying on top of the flow of information.
Cloud-based ETL tools come with great efficiency and offer flexibility in storing readily available data for users from anywhere.
Custom ETL tools are mainly used by major businesses with massive development budgets. The main perk of such ETL tools is the uniqueness and flexibility of extracted, transformed, and loaded data.
Data Integration vs. ETL: 3 Key Differences
By placing data integration vs. ETL side by side, you can see they have some similarities. However, they are not the same. Below, we’ve made a table comparing data integration tools vs. ETL, showing the 3 points in which these methods differ.
|Order of Processing||Data is transformed at the initial stage before being loaded into the designated data system.||Data preparation, data franchising, metadata management, and data management.|
|Key function||To transform, mask, standardize, and join data across tables.||To create compact sets of data that are filtered and in line with the businesses’ requirements and end users’ goals in the system.|
|Maintenance||Some manual work might be required to change the schemas.||Data needs to be regularly updated.|
Choosing The Right Method for Your Customer Data
Gathering data for analytics and segmentation is no easy process. To ensure you’ve found the right data collection method, you need to bear in mind the sort of information you’re looking to collect. After that, see how fast you need it and other aspects.
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ETL is a distinct subcategory of data integration. It extracts, transforms, and loads data into data systems.
ETL is data integration because it consolidates multiple-source data into a targeted data system. Hence, data integration tools can be called ETL tools.
The main difference is that data integration is part of a data warehouse. This shouldn’t be confused with data warehousing, a type of data integration.
The main purpose of data integration is to link data from different sources to understand better the information you need.
Scope, connect, and sync are the three main data integration steps. You scope, connect, and sync the data to get a refined view of the data you need for analysis.