Suitable for cases when a small amount of
data is extracted
Enables program connections to almost any
Database Management System (DBMS)
Possible data sources:
1. Production data (OLTP systems)
2. Archive data (data backups, suitable for initial data warehouse loading)
3. Internal data sources (private data in different format, e.g. xls, doc, xml)
4. External data sources (data obtained from outside the organizational system)
Types of data extraction:
From the perspective of retrieve data:
o Full: Always retrieves the complete source of data.
o Incremental: Retrieves only the parts (newly added data) depending on the time of
the previous extraction.
From the perspective of accessing the data source:
o Online: Accesses the database server in an online mode.
o Offline: Accesses data sources outside of operational use (logs, archives, dump files).
Data Transformation
Usually involves (Cleaning) & Transformation of the data
Involves:
Validation, cleansing, integration, and timestamping of data.
Often the most demanding and complex part of the ETL process.
Key influence on ensuring data quality within the DW system.
Types of errors or inconsistencies:
1. The absence or non-compliance with key constraints (inconsistent identification of tuples in
relations). Missing Key Identifiers: key elements that should uniquely identify data entries are
missing or don’t work correctly.
2. The absence or disregard of other constraints (violation of attribute values, referential
integrity constraints, etc.) Broken Rules: Rules about what data can be (like allowed values or
links between tables) aren’t followed.
3. The occurrence of attribute synonyms and homonyms (inconsistency in names and
semantics): one actual attribute decomposed into multiple different attributes or multiple
different attributes consolidated into one. Confusing Names: The same attribute has different
names or meaning, or different attributes are grouped under the same name.
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