ETL Data Extraction Explained in Just 5 Minutes
ETL stands for Extract, Transform, Load — three essential processes in data integration. Each phase plays a vital role in preparing data for analysis and reporting. We will focus on the extraction part, which involves pulling raw data from various source systems.
Understanding Data Extraction
Data extraction refers to the process of retrieving data from different sources, which may include:
- RDBMS (Relational Database Management Systems)
- NoSQL databases
- XML and JSON files
- APIs
- CRMs and SaaS applications
The extracted data is crucial as it serves as the foundation for the transformation phase.
The Importance of Staging Layer
Once data is extracted, it is not immediately transformed. Instead, it is stored in a staging layer. This practice is vital for several reasons:
- If any issues arise during the transformation phase, the extracted data remains intact.
- Storing data in a staging area allows for easier access and manipulation before transformation.
Methods of Data Extraction
There are multiple tools and methods for data extraction, especially within the AWS ecosystem. Here are some popular options:
AWS Glue
AWS Glue is a powerful tool for data extraction, capable of pulling data from various sources, including databases and APIs.
AWS DMS (Database Migration Service)
AWS DMS is another excellent option for extracting data from RDBMS, facilitating smooth data migration.
Apache NiFi
Apache NiFi is specifically designed for data extraction and loading, making it a great choice for managing data flows.
Types of Data Extraction
Understanding the types of data extraction is essential for effective ETL processes. Here are the main types:
Full Extraction
Full extraction involves pulling all available data from a source system. For example, if you have an employee database, a full extraction would retrieve all records from January 1, 2020, to January 1, 2024. This method is useful when:
- There is no timestamp column in the source.
- You need a complete dataset for analysis.
Partial Extraction
Partial or incremental extraction focuses on retrieving only the new or updated records. This method requires a timestamp column or an update notification indicator. For instance, if an employee changes their location, the system can notify you of the update, allowing for targeted extraction.
Update Notification
In partial extraction, an update notification can be represented by a column indicating whether a record has changed (e.g., updated = ‘Y’ or ‘N’).
Conclusion
In summary, mastering the extraction phase of ETL is crucial for any data engineer. By understanding the various methods and types of extraction, you can ensure that your data integration processes are efficient and reliable. Whether you choose AWS Glue, AWS DMS, or Apache NiFi, the key is to implement a robust data extraction strategy that meets your organization’s needs.
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