What are the most common data transformations customized with ETL tools?

1 Data cleansing

Data cleansing is the process of identifying and correcting errors, inconsistencies, and anomalies in the data. Data cleansing is essential for ensuring the accuracy and reliability of the data warehouse, as well as for facilitating analysis and reporting. ETL tools can help data engineers perform data cleansing by providing features such as data validation, data quality rules, data profiling, and data matching. However, sometimes data cleansing requires more advanced or custom logic, such as applying regular expressions, parsing complex strings, or handling missing values. In these cases, data engineers can use scripting languages, such as Python or SQL, to write custom code within the ETL tool or as a separate component.

2 Data aggregation

Data aggregation is the process of combining data from multiple sources or records into a single value or summary. Data aggregation is useful for reducing the size and complexity of the data, as well as for enabling high-level analysis and reporting. ETL tools can help data engineers perform data aggregation by providing functions such as grouping, sorting, filtering, and calculating. However, sometimes data aggregation requires more sophisticated or custom calculations, such as weighted averages, percentiles, or custom metrics. In these cases, data engineers can use expressions, formulas, or functions within the ETL tool or as a separate component.

4 Data partitioning

Data partitioning is the process of dividing the data into smaller and more manageable subsets based on certain criteria, such as date, time, region, or category. Data partitioning is beneficial for improving the performance and scalability of the data warehouse, as well as for facilitating analysis and reporting. ETL tools can help data engineers perform data partitioning by providing features such as data splitting, data distribution, and data indexing. However, sometimes data partitioning requires more granular or custom criteria, such as data sampling, data clustering, or data hashing. In these cases, data engineers can use algorithms, models, or techniques within the ETL tool or as a separate component.

5 Data auditing

Data auditing is the process of tracking and documenting the changes and activities that occur in the data warehouse, such as data loading, data updating, data deleting, or data accessing. Data auditing is important for ensuring the integrity and security of the data warehouse, as well as for complying with regulations and standards. ETL tools can help data engineers perform data auditing by providing features such as data logging, data tracing, and data alerting. However, sometimes data auditing requires more detailed or custom information, such as data lineage, data provenance, or data governance. In these cases, data engineers can use metadata, tags, or frameworks within the ETL tool or as a separate component.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?