What is ETL: Full Form, Process, Benefits, Examples, Use Cases

If data is the new oil, then definitely the engine it fuels is business intelligence. And every engine requires high-quality fuel to perform at its best. This article explains ETL as a critical process in the growing fields of data mining, data warehousing and business analytics. Stay with us to learn everything about what, when and how of ETL. 

What is ETL?

The ETL full form is Extract, Transform and Load. It’s a process followed in data mining, extraction and warehousing, especially when data is being collected from multiple sources of varying data structures. 

The rules and principles followed in designing an ETL process depend on business use cases. For example, if the aim is predictive analysis, then ETL should ideally follow a distinct set of rules than when it is being used for increasing data storage efficiency. 

As the name implies, ETL comprises 3 distinct stages: 

• Extract - This stage involves the extraction of data from different sources, like data from IoT-enabled sensors, APIs, point-of-sales data, ERP, etc. These sources may offer data in fully structured, semi-structured and unstructured formats. So, extraction techniques vary depending on source characteristics. 

Depending on the source database, you may need to conduct full-extraction or partial extraction. Full-extraction involves getting the entire database extracted every time the data warehouse manager is conducting ETL. Partial-extraction involves getting only the part of the data that has been updated or changed. 

• Transform - ETL involves warehousing data from multiple dissimilar sources. So, once extraction is complete, the extracted raw data requires cleaning, validation, data type conversion, and mapping into the desired formats. 

This is the stage which ensures uniformity, integrity, and quality of raw data extracted from different sources. So, if the benchmark criteria of the designated ETL process are not met, this stage can start rolling back the extracted data. It means the transform stage has compliance and diagnostics implications in data warehouse management.   

• Load - This is the last stage of ETL where cleansed data gets loaded to storage in a data warehouse for further applications. Loading may involve creating a new database or updating an existing target database with freshly extracted and cleansed data.    

Why is ETL Important? 

The ETL process in data warehousing and data analytics is important for several reasons. It helps: 

• Extract and integrate data from dissimilar sources - Business data can reside in various sources in structured and unstructured formats. But for analytics and other use cases, we need to extract and integrate them in a coherent format before storing them in data warehouses.  
• Remove inconsistencies, errors and other data anomalies during extraction - At the transformation stage of ETL, database managers can identify inconsistencies, duplicate data and other anomalies in raw data and initiate rollback. It allows us to maintain the accuracy, integrity and quality of raw data being processed.  
• Ensure legal compliance and reduce cost of data warehousing – Digital data processing and retrieving actionable business intelligence from the extracted data has become an issue of legal compliance. Failure to comply with prevailing regulations may cause significant monetary loss and penalties. So, the design of the ETL process has both compliance and financial implications. 
• Transform raw data from different sources into analytics-worthy information - Not all raw data is worthy of analytics. ETL extracts and transforms structured, unstructured raw data into actionable information.  
• Enables application of standardised data warehousing techniques - ETL enables database management techniques, like sharding, partitioning and indexing, as well as in optimising storage and the usability of stored information. 

How does ETL work?

Source: Educba

ETL or Extract-Transform-Load function involves a 5-step workflow. 

1. Extract - Collection of raw data from different sources.  
2. Clean - Sanitisation of raw data collected from unstructured sources, flat files; removing anomalies.  
3. Transform - Integration of extracted and cleansed data from different sources, and transforming it into the target data types and formats. 
4. Load - Upload cases of integrated, transformed and standardised data to an existing or a new database in a data warehouse for specific use cases. 
5. Analytics - Store data goes through analytics or machine learning models for gaining critical business intelligence or other purposes. 

What is ETL Testing?

ETL testing is a part of database management activity that tests and verifies that a specific ETL process matches its targeted quality and functional benchmarks. 

ETL testing requires: 

• Clarity regarding requirements for business data analytics 
• Devising a test plan 
• Identifying test cases 
• Collecting test data 
• Implementing the test 
• Documentation and reporting test results  

Some of the critical test cases for ETL testing are: 

• Metadata testing - compares metadata, like length, data types, schema and constraints between sources and the target databases.   
• Data accuracy testing - checks the existence of inaccurate, duplicate data and other anomalies in extracted and transformed data. 
• Data completeness testing - compares the sources of raw data with data loaded to a target database.  
• Data integration testing - identifies anomalies in interactions between database components. 

What is the ETL Pipeline?

ETL pipeline is a data pipeline that accommodates sequential processes of extraction, cleansing, transformation and loading of data to a target database for further use in applications. Such pipelines automate the ETL process for a continuous update of existing databases with any addition or change in raw data sources.  

Examples of ETL

A prominent example of ETL is central database management and data warehousing of organisation-wide data for a large company.

Source: Semantic Scholar

In this example, we can observe that raw data resides in different sources, like sales, inventory, CRM, logistics and finance departments. These sources can also be geographically dispersed. Raw data from these sources are extracted, cleansed, transformed into a target format. Finally, the transformed data is stored in the central database.

Benefits of ETL 

• Data standardisation - ETL helps transform raw data into standardised formats and makes it accessible for easier analytics.  
• Movement of bulk data - Companies can move large-sized raw data from dissimilar sources in minimum time and costs.  
• Transformation of complex data - ETL tools allow the implementation of complex data type transformation and integration tasks.  
• Data cleansing - ETL helps maintain data accuracy and integrity, and meets the quality benchmark necessary for analytics and machine learning models. 
• Data warehousing automation - ETL pipeline allows you to automate the entire process of data extraction, transformation and loading to a target warehouse.  
• Storage and management cost reduction - Easier movement of bulk data, automation of data pipelines and availability of highly accurate data reduces data storage and management costs. 

Challenges of ETL 

• Quality of raw data - Inconsistent, incorrect, duplicate entries and other data anomalies have always been a major challenge for implementing a reliable ETL pipeline. 
• Increasing complexity in raw data - Engineers, sensor technologists and scientists are making available new raw data from unconventional sources. Cleaning and maintaining data harmony with similar raw data can be a major challenge. 
• Scalability concerns - Even when ETL rules are simpler, scaling data pipelines or replication of ETL pipelines can require business case specific adjustments. It increases costs. 
• Transformation complexity - With an increase in unconventional raw data, integration and transformation processes become more complex and challenging.  
• Legal regulation and data governance - Data privacy acts and digital data processing related regulations are increasing challenges for data management and warehousing. 

When to Choose ETL? 

ETL is suitable for business cases where it is necessary to extract and integrate raw data from dissimilar sources and transform it into a specific structured format before loading to a target database. 

ETL Cases by Industry

ETL use cases may vary depending on industries and, within an industry, on the end objectives of data utilisation. Some of the common examples of industries using ETL pipelines are: 

• Retail supply chain management data 
• Patient data in large hospital chains 
• Banking risk management data 
• Situational awareness data for logistics companies 
• Grid performance data collected by power utilities 

Best Practices for ETL Implementation

One of the key solutions to ensuring ETL performance and overcoming common challenges is following only the best practices in ETL implementation. We have mentioned these practices in sequential order. 

1. Define the objectives and requirements of the target database 
2. Review the regulatory and data governance implications 
3. List the tools essential to building the ETL pipeline 
4. Design the ETL implementation workflow 
5. Design the ETL test criteria 
6. Implement the ETL processes 
7. Run ETL testing and validate the performance 
8. Monitor ETL performance based on pre-established benchmarks
9. Maintain performance log and compliance documentation 
10. Conduct database performance audit   

ETL vs ELT, Which is Better? 

Both ETL and ELT are data extraction, integration, and transformation processes in database management. And their suitability varies with use cases. 

• ETL (Extract-Transform-Load) is more suitable for databases extracting data from sources with structured and semi-structured raw data. 
• ELT (Extract-Load-Transform) is suitable for processing extensive databases, handling bulk raw data with a significant portion of unstructured data, like images and documents.