Better Data, Better Analytics: Transforming Data Through ETL & ELT for Consistent Outcomes 

Accurate data is crucial for effective analytics, as it can impact government and enterprise organizations in terms of time, money, reputation, customers, and even safety. Just like a fuel filter removes impurities before reaching a car engine, transformation functions are necessary for analytics to ensure high-quality data for decision-making. The adage ‘Garbage In, Garbage Out’ holds true. 

In our previous blog, From Raw Data to Informed Decisions, we discussed various transformations such as ETL/ELT ((Extract, Transform, Load/Extract, Load, Transform), Federated Search, Entity Resolution, and Metadata Calculations, each with unique methods to enhance consistency and add value to the data. This article focuses on the distinction between ETL and ELT and their respective transformation stages. 

ETL (Extract, Transform, Load) 

ETL involves extracting data from external sources and applying transformations before loading it into an analytical platform. This approach is commonly used in business intelligence applications with established ETL offerings. The necessary data is extracted from corporate data warehouses or other sources and transformed to align the content/structure and make required changes. Once completed, the data is loaded into a predefined structure in the analytical platform for further manipulation and analysis. 

ETL Benefits: 

• ETL allows advanced transformations and handles complex extractions. 
• Data restrictions, such as GDPR or HIPAA, can be enforced by removing sensitive content before loading. 
• Numerous commercial and open-source systems are available for ETL, including tools like NiFi and programming languages like Python. 

ETL Considerations: 

• Data is staged outside the source/database and analytical platform, requiring additional storage and resources. 
• Lineage tracking across multiple systems becomes complex. 
• It can’t re-process previously loaded data without reloading the entire set. 

ELT (Extract, Load, Transform) 

ELT reverses the processing order, placing a greater burden on the analytical platform. In ELT, transformations are applied after data loading, and the platform must control and manage the data directly extracted from its source. Regardless of the loading process, transformations are essential for aligning data structures and values. ELT platforms rely on robust model-definition frameworks to map data into a dynamic model and manage source, fields, and value alignment. This flexibility enables the discovery of patterns, trends, anomalies, and relationships within the data. 

ELT Benefits: 

• Data is maintained as a single authoritative source within the analytical platform. 
• Administration is simplified with a single platform. 
• Lineage tracking and auditing are streamlined. 

ELT Considerations:  

• Requires a stronger security model to control what content is used to generate results (permissions). 
• Transformation dependencies might impact system performance for updating values. 
• Access to a transformation framework for programming, scripting, or embedded functions. 

Combining ETL & ELT 

A hybrid approach combining ETL and ELT can offer the best of both worlds. Standard transformation activities can be optimized within the loaders, reducing the overhead of repeatedly managing these transformations. This allows the ELT approach to focus on dynamic discovery processes across diverse data sources. 

Want to learn more about how federated queries are used in analytical platforms and how they add value to existing sources? Look for the next blog article in this series, Federated Search: Incorporating the Right Data at the Right Time, which will introduce the fundamentals of federated search, subscription services, and third-party libraries – and “where and how” they should be used. Using the right combination of techniques should improve the quality of the results generated by intelligence systems. 

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