ETL design and data integration
01
The "ETL" (Extract, Transform, Load) process refers to a succession of actions related to data processing: extracting data from many sources, transforming raw data into usable data, and then loading into a data warehouse that can be queried for analysis or modeling.
In a more concrete and detailed way, CIOs manipulate a multitude of data, whether structured or not. These come from many sources: internal and external databases, operating systems, activity or connection logs, etc.
It is important to be able to centralize the data and to do this, the Data Warehouse or the Data Lake are the most important tools. The extraction and load of Data through the ETL Pipeline mechanism is accompanied by a transformation used to standardize, differentiate, sort and any other necessary actions.
02
- Talend Data Integration, an open-source tool offering a wide range of components and connectors for data integration. It also has the advantage of offering a designer (graphic module), Talend Studio, facilitating the visualization and reuse of data flows
- SQL Server Integration Services (SSIS), developed by Microsoft; provides advanced features for data integration: data transformation, workflow task management, and error handling
- Pentaho Data Integration, also features for data integration through a graphical model to plan and visualize job execution
- DataStage, a data integration tool developed by IBM; contains a hybrid and multi-cloud option
- Oracle Data Integrator (ODI), developed by Oracle; comprehensive integration platform
03
The advantages of structuring your data via ETL for your business
Improved data quality
- ETL allows for transforming and cleaning data before it is loaded into a data management system.
Increased performance
- The good management of data provided by the ETL process, and its update, have a direct impact on the company's performance
Time saving
- ETL automates the data transfer process, allowing you to gain efficiency and focus on high value-added tasks
Cost and operational risk control
- Automatingthe data transfer process via ETL reducesmanual tasks and eliminates data entry errors
04
Data integration
- Connect and synchronize data sources from different databases and systems
Process automation
- Automate ETL processes to reduce manual processing, human error, and control operational risks
Data migration
- Reliably migrate data from one source to another
ETL Cloud platform
- Consulting and migration of your ETL solutions to the Cloud
Data preparation
- Cleaning, structuring, and organizing data for optimal exploitation and visualization
