You can execute data integration and ETL procedures for data management and analysis. However, you may wonder what the difference between data integration and ETL is if you’re considering employing a data integration platform to construct your ETL process. In this article, we will discuss the differences between the two methods.
Data integration is the method by which information from various data sources is combined into one. It allows you to gather data from many sources, organize it, and save it in a central location, where you can easily analyse it for insights.
There is no general approach to data integration, and it often consists of a few components that are shared among them. These components include a primary server, a network of data sources, and clients that retrieve data from the primary server.
In a typical data integration process, the client initiates communication with the primary server by submitting a request for data.
After this, the primary server will collect the data from many internal and external sources.
Finally, the data will be retrieved from the sources and combined into a single coherent dataset.
Several companies use data integration to improve the accuracy of their data reporting and analysis for better business decisions. It is useful in businesses in the following ways
- Improves collaboration and unification of systems
- Saves time and boosts efficiency
- Delivers more valuable data
The term “data migration” refers to transferring data between various data formats and applications and between different data storage systems.
There is also the work of preparing, extracting, and transforming data during the migration process. It’s common practice when implementing new procedures and technology in a business.
Common scenarios of when it’s necessary to migrate data are as follows:
- Storage system and apparatus maintenance, upgrade, and expansion
- Improving and replacing outdated software
- Switching from on-premises to cloud-based storage to maximize efficiency.
- Merging of websites
- Integration of fresh software to work alongside and improve upon pre-existing programs that use the same data source
- Infrastructure upkeep
- Merging of computer networks
- Relocation of a Data Center
ETL & Reverse ETL
What Makes Up an ETL Data Pipeline?
In this process, data is extracted from the target systems using queries, change data capture (CDC), API calls, or another method, and then migrated to a staging area.
In the Transform stage, the data is prepared for analysis by being cleaned, standardized, and organized. This phase ensures the data is complete, correct, and consistent in format and unit.
Data is loaded into the target system, which is usually a data warehouse, at this phase. This could involve overwriting existing data as part of a job, writing to a file, or building relevant schemas and tables.
As business requirements and available technologies evolve, the ELT methodology can be rapidly refined to meet those evolving demands. It consolidates information from multiple sources into a unified database that can be viewed in various formats to serve its users’ needs better.
What do you do if you need more information than what the dashboard provides? It is helpful to use reports and BI dashboards for double-checking assumptions and generating broad assessments. However, there are occasions when the latest information could be useful to front-line business users after being consolidated and converted in the data warehouse.
This issue can be fixed by employing reverse ETL. A reverse ETL tool takes live data from a data warehouse, applies transformations, and then puts it into an operational system or application.
This procedure is necessary for several scenarios when business customers wish to implement remodeled data or the outcomes of data modeling in their preferred software.
For instance, critical metrics like Customer Lifetime Value (CLV) were calculated in the data warehouse. The sales team could benefit from having access to this information in a Hubspot-like CRM, even though it has already been made available via a Looker dashboard. That data can be extracted from the data warehouse and re-inserted into the CRM using a reverse ETL tool.
With this modern tech hack, you can automate data extraction from your warehouse system and analyze it in CRM software to gain more insights about the data, enabling you to earn more data-driven decisions.
Data Integration vs ETL
The ideas of data integration and ETL are similar and often used interchangeably. In fact, ETL can be viewed as a subset of data integration, and this is because both procedures compile information from several resources into one central hub.
While ETL tools and principles are commonly used in data integration solutions, it is crucial to remember that this is only the case for some solutions.
Replication, virtualization, APIs, and web services are just some of the alternate approaches that can be used to bring together information from many locations.
However, when deciding if ETL is the best data integration method, it is important to consider the business’s unique requirements.
In contrast to ETL, data integration encompasses a broader range of activities. It’s not limited to transferring data between computers. It often includes
- Making sure the data is precise, current, and accessible.
- Building a centralized database that contains all relevant information, such as product names, codes, and client identifiers.
A number of large corporations use ETL, data migration, and integration to manage and analyze their data. But the question of what to do at any given moment depends entirely on the task.
If you need to merge data from many sources, data integration is the method to use. However, you can carry out data migration or ETL if you wish to move your data to a different storage system or extract, convert, and load it into your warehouse.