Optimise the value of your IS with ETL
ETL- three letters covering a fairly simple concept: the integration of data from multiple sources.
In your quest to improve your information system, you can't have missed 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 why you must have felt the need to better understand the concept of ETL.
What is ETL?
Definition
This acronym is a combination of the English words Extract - Transform - Load. It characterises 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 providing the interface between the sources and the target.
ETL works in 3 stages
1. E of Extract
This stage consists of extracting data from their storage systems: whether they come from an ERP, a DBMS or flat files.
2. T for Transform
This involves transforming the data: it is
- verified
- reformatted
- cleaned of duplicates,
- anonymised
- enriched.
3. Load phase
Finally, the data is loaded into the data warehouse, where it is made available to various tools such as data mining or OLAP (OnLine Analytical Processing).
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.
Example of ETL
The most obvious example is the implementation of a data warehouse. In other words, a repository for data from the entire company in a form that can be delivered 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. 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,
- or an archiving, backup or redundancy system.
Finally, ETL solutions are continually being enhanced to incorporate functions that are sometimes considered secondary. These include the monitoring of information exchanges, quality management and information tracing, and even the provision of information via ESB.
The benefits of ETL for your company
- upgrading your IT architecture to a higher level of integration, control and data exchange;
- better meet the different needs of your employees:
- internal needs: to bring the company to life by enabling consistent information to be communicated between departments so that they can respond as effectively as possible to business activity,
- external needs: optimising exchanges with partners (suppliers, customers, competitive groups), meeting administrative requirements (administrative declarations, audits, RGPD),
- emerging needs: making hidden information speak for itself, detecting weak signals,
- cleaning up and formatting the quantity of information generated by the processes, software and machines used by employees;
- the optimised architecture, generally on-premise, then loading the information into the database 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,
- maximise the use of storage infrastructures,
- meet security requirements, such as data anonymisation.
ETL or ELT?
Of course, it will not have escaped you that Big Data and the Cloud have revolutionised the way we approach information, its usefulness and its monetisation. Here again, ETLs are trying to sharpen their weapons to interface with these hyper-scalable systems.
But the task is not an easy one, and if you have experienced exponential growth in your data, attempts at ETL processing could 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 can take advantage of the power of the platform hosting the data, rather than intermediate servers.
The ELT, coupled with a highly scalable datalake, therefore has the virtue of keeping the raw information, representing considerable volumes, warm. It is exchanged in near-real time, untouched by any transformation specifications, ready to be explored by data scientists. They will derive 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 hybridisation or the advent of concepts such as data virtualisation.
Which ETL tool should I choose? List of ETL tools
There is a particularly wide choice 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 software to the most expensive solutions, they all address at least these basic issues:
- Integrating information from different systems
- Cleaning up the information to make it consistent
- Making this information available to users
But all of them also highlight their own specific features, so as to match your uses as closely as possible. From the most comprehensive legacy 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