Three Data Extraction methods:

1. Full Extraction
2. Partial Extraction- without update notification.
3. Partial Extraction- with update notification

Irrespective of the method used, extraction should not affect performance and response time of the source systems. These source systems are live production databases. Any slowdown or locking could affect company’s bottom line.

Some validations are done during Extraction:

• Reconcile records with the source data
• Make sure that no spam/unwanted data loaded
• Data type check
• Remove all types of duplicate/fragmented data
• Check whether all the keys are in place or not

Step 2) Transformation

Data extracted from source server is raw and not usable in its original form. Therefore, it needs to be cleansed, mapped and transformed. In fact, this is the key step where ETL process adds value and changes data such that insightful BI reports can be generated.

In this step, you apply a set of functions on extracted data. Data that does not require any transformation is called as direct move or pass through data.

Validations are done during this stage

• Filtering – Select only certain columns to load
• Using rules and lookup tables for Data standardization
• Character Set Conversion and encoding handling
• Conversion of Units of Measurements like Date Time Conversion, currency conversions, numerical conversions, etc.
• Data threshold validation check. For example, age cannot be more than two digits.
• Data flow validation from the staging area to the intermediate tables.
• Required fields should not be left blank.
• Cleaning ( for example, mapping NULL to 0 or Gender Male to “M” and Female to “F” etc.)
• Split a column into multiples and merging multiple columns into a single column.
• Transposing rows and columns,
• Use lookups to merge data
• Using any complex data validation (e.g., if the first two columns in a row are empty then it automatically rejects the row from processing)

Step 3) Loading

Loading data into the target datawarehouse database is the last step of the ETL process. In a typical Data warehouse, huge volume of data needs to be loaded in a relatively short period (nights). Hence, load process should be optimized for performance.

Types of Loading:

• Initial Load — populating all the Data Warehouse tables
• Incremental Load — applying ongoing changes as when needed periodically.
• Full Refresh —erasing the contents of one or more tables and reloading with fresh data.