What is Informatica ETL tool
Informatica is a widely used ETL tool for extracting the source data and loading it into the target after applying the required transformation. ‘E’ stands for the extraction function. Extraction of the data from the outside sources in data warehousing is what the extraction of the data is meant for. The ‘T’ stands for the transformation of the data. The data in most of the cases is needed to be transformed well for its best fit in to the operational working of the software. The transformation function can even include the quality levels in it. Finally, the ‘L’ stands for the loading process. The extracted and the transformed data is finally loaded to the concerned and the relevant position. The data is loaded on to the data warehouse or the data base, which ever is the end target assigned. Hence, this is what ETL tools are meant for.
The main uses of ETL tools are :
- One of major reason for Informatica etl tool success is its capability of enabling Lean Integration, Lean manufacturing is common concept in manufacturing industry to avoid waste. Informatica also leverage the same integration model.
- High “GO Live” success rate .Informatica claims the highest ration of successful deployment which it says is near 100%. Product rate of renewal and customer loyalty is significant from industry average.
- Easy training and tool availability has made easy resource availability for software industry, where else other etl tools are way behind in this aspect. This definitely helps organization in reducing training costs. Moreover forming a new team for this tool is not as challenging as others.
- Informatica etl is moderate expansive tool, where else tools like ab initio is very expensive which has many added advantages in technical aspect , Same time others etl tools are having there own challenges like ease of use, re-usability , debugging, connectivity which makes informatica as an ideal etl tool.
The execution strategies or the steps involved in a practical ETL cycle are as follows:
- Cycle initiation
- Reference data build up
- Source extraction of the data
- Validation of the data
- Transformation of the data
- Data integrity and the data clearances
- Loading the data into stages, if needed
- Audit reports
- Publishing the data into concerned tables
- Archive the data
- Cleaning up the data.