Pull data from different sources
In this step, we connect to different data sources and extract the necessary data. This extracted data should be made available at the earliest for further processing before analytics.
Clean it to get accurate, consistent and good quality data
This process involves detecting any errors, redundancies, or inconsistencies in the data. It helps to get accurate and consistent data to maintain good quality in the data warehouse.
Prepare and transform the collected source data into a form that matches the target system requirement
This step helps in transforming the extracted and cleansed data into a form that can be used for analysis. Pre-aggregation might help boost the performance, but at the downside of increased cost.
Import the transformed data into target/warehouse
The transformed data is then imported into the target database or warehouse, either incrementally added at regular intervals or at one go, depending on the business requirement of the customer.
Design and manage a strong ETL architecture with recovery settings
To effectively implement an ETL architecture, it’s necessary to regularly streamline and audit the entire process of data collection and processing, to minimize errors and enhance efficiency.