Optimize the value of your IS with ETL
ETL, three letters that cover a fairly simple concept: the integration of data from multiple sources.
In your quest to improve your information system, you can't have overlooked Extract-Transform-Load. A few questions follow. How does it work? Can I do it with the means at my disposal? Which ETL tool will give me the most satisfactory result in this integration process?
That's how the need to better understand the notion of ETL must have seemed inescapable.
What is an ETL?
Definition
This acronym is a combination of the English words Extract - Transform - Load. It characterizes the sequence of 3 main operations involved in integrating data into a Database Management System (DBMS).
An ETL solution is therefore a set of tools and machines interfacing between the sources and the target.
How ETL works in 3 steps
1. E of Extract
This stage consists of extracting data from their storage systems: be they ERP, DBMS or flat files.
2. T from Transform
This involves transforming the data: it is
- verified,
- reformatted,
- cleaned of duplicates,
- anonymized,
- enriched.
3. Load stage
Finally, we load the data into the datawarehouse, where it becomes available to various data mining or OLAP (OnLine Analytical Processing) tools.
Depending on the history and architecture of your information system, and the diversity of your businesses, this process can be relatively straightforward, or a real acrobat's act. This is often where a well-chosen ETL middleware can bring you significant added value.
ETL example
The most obvious example is the creation of a data warehouse. In other words, a repository for company-wide data, in a form suitable for delivery to different consumers, analysts, auditors or other systems.
Such a warehouse can then provide each business line with a datamart tailored to its specific analysis and reporting needs. It will be based largely on its own data, but not exclusively. Indeed, the greatest added value will come from the enrichment of this data by the company's other systems.
More broadly, ETL can be useful for any data exchange requirement, whether one-off or recurring. For example :
- migration of operational systems,
- exchanges with partners or regulatory bodies,
- archiving, backup or redundancy systems.
Last but not least, ETL solutions are continually being enriched to integrate functionalities sometimes considered secondary. These may include monitoring of information exchanges, quality management and information tracing, or even the provision of information via ESB.
The benefits of ETL for your company
- upgrade your IT architecture to a higher level of integration, control and data exchange;
- better respond to the different needs of your staff:
- internal needs: keep the company running by communicating consistent information between departments, so that you can react as effectively as possible to your business activity,
- external needs: optimizing exchanges with partners (suppliers, customers, competitive groups), meeting administrative requirements (administrative declarations, audits, RGPD),
- emerging needs: making hidden information talk, detecting weak signals,
- cleaning up and formatting the quantity of information generated by processes, software and machines used by employees;
- optimized architecture, generally on-premise, then loading information into the database only once it has been adapted to the requirements specified upstream, in order to :
- produce enriched, high-quality information that can be rapidly used for operational reporting or decision-making analysis,
- maximize the use of storage infrastructures,
- meet security requirements, such as data anonymization.
ETL or ELT?
Of course, it won't have escaped your notice that Big Data and the Cloud have revolutionized our approach to information, its usefulness and its monetization. Here again, ETLs are trying their best to interface with these hyper-scalable systems.
But the task is not an easy one, and if you've experienced exponential growth in your data, attempts at ETL processing may reveal the limits of this principle.
This state of affairs has given new impetus to ELT (Extract Load Transform), a cousin process in data integration:
- information is stored in its raw form,
- then transformed,
- and finally made available.
In this way, we leverage the power of the host platform rather than intermediate servers.
The ELT, coupled with a highly scalable datalake, has the virtue of keeping raw information, representing considerable volumes, warm. It is exchanged in near-real time, untouched by any transformation specification, ready to be explored by data scientists. They will extract new knowledge from it, giving rise to new needs and, ultimately, new profits.
Today, the worlds of ETL and ELT can only converge, either through the existence of tools capable of hybridization or the advent of concepts such as data virtualization.
Which ETL tool should you choose? ETL list
The choice is particularly wide in this field. To help you find your way around, here's a quick classification:
| Type of ETL tool | Examples of ETLs |
| The behemoths |
|
| The newcomers |
|
| Open source ETLs |
|
| Cloud-based and No-code |
|
Conclusion
From "free" open-source to the most expensive solutions, they all address at least these basic issues:
- Integrate information from different systems
- Cleanse and standardize information
- Making this information available to users
But all of them also highlight their specific features, so as to meet your needs as closely as possible. From the most complete historical solutions to "no-code", cloud or real-time specialists, the ETL world is full of nuggets. It's getting closer to ELT, and the concentration of this fast-moving market is far from over.
Article translated from French