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

Modern business data flows are continuous and are of large volume. It also integrates raw data of different types and formats requiring significant time and cost efficiency to extract, load and transform such an enormous volume in real time. ELT is the most obvious answer. This article explains what ELT technology is and how it has changed data engineering, analytics and real-time business intelligence.

What is ELT?

ELT’s full form is Extract–Load–Transform 

ELT is a data integration and processing pipeline suitable for modern big and very big data sets. Cloud-integrated data warehouses, data lakes and data lake houses use the ELT pipeline to create analysis and machine-learning data repositories.

It is similar to the conventional ETL pipelines, but allows more flexibility in terms of the availability of processing and data transformation tools. ELT pipeline helps data warehouses and data lakes to contain raw data with all their freshness. This may make the job more challenging for data scientists and also improve data modelling opportunities.

Why is ELT important?

The purposes of ELT in managing and processing data are: 

• Real-time data processing: ELT pipelines prioritise data extraction and loading to cloud-based data warehouses and data lakes. It integrates data from multiple dissimilar sources into a single storage by cleaning and transforming it. So, analysts and scientists can access data in real-time. 
• Wider flexibility: ELT can work with sources of all data types, like flat files, geospatial images, IoT sensor APIs and many more. So, data engineers can enjoy better flexibility in storing data in central data lakes using ELT pipelines. Unlike ETL, they need not decide the final use case before warehousing raw data. 
• Scalable data warehousing capacity: Because of its flexibility and real-time data warehousing capacity, ELT pipelines can parallelly process multiple sources and load to cloud storage. As data transformation happens after data warehousing, engineers can reuse a portion of the ELT data pipeline without requiring significant changes or intensive coding. 
• Cost-effective: In any data pipeline, transformation is the major resource-hungry process. ELT delegates responsibility for cleaning and transformation of raw data to the end users. This type of data extraction and integration requires less processing power and other resources. This makes ELT more cost-effective. 
• Compliance-friendly - Due to warehousing of raw data in real-time, ELT pipelines allow fast tracking of anomalies in raw data. It helps to take countermeasures for better compliance with data processing related to the regulatory framework.

How does ELT work?

ELT process is a combination of 3 data pipeline handling sub-processes, namely - 

• Extract 
• Load 
• Transform 

Extraction - In this sub-process, data is extracted from multiple sources, like IoT sensors, Point-of-Sales terminals, geospatial APIs, and legacy applications. This can be automated using simple-to-use tools and SaaS data extraction applications. The ELT extraction process allows a variety of structured, non-structured information available in flat files and other sources. 

Loading - The aim of this stage is to transfer the raw data to cloud-based data storages, like warehouses, data lakes, etc. The uniqueness of the ELT process is that loading of data happens without cleaning or transforming it to any specific target format. So, other than ensuring faster storage of the raw data, no other data processing happens before or during the Loading. So, the ELT pipeline allows quick or real time extraction and data storage. No decision regarding the target format is necessary for warehousing the data. 

Transformation - ELT allows storage of raw data in their original types and formats in large cloud databases. Transformation of data happens depending on the final use cases. So, data loaded using ELT pipelines may contain issues, like: 

• Lack of clarity in column attributes 
• Un-nested JSON fields
• Missing primary keys 
• Incorrect timestamps 
• Disjoined tables 
• Inconsistent data types

Based on the quality of raw data and the complexity of target formats, the data analyst can segregate the cleaning and transformation process into three categories: 

• Light transformation - It involves cleaning and removing data anomalies like incorrect entries, duplicate entries, missing timestamps, incorrect time-stamps, etc.  
• Heavy transformation - It is a combination of cleaning and basic transformation tasks like the application of business intelligence logic and the integration of multiple datasets. 
• QA transformation - This requires making the stored data compliant with benchmark standards of business use cases. 

Examples of ELT

Every modern business generating and handling big data uses ELT pipelines to load their data warehouses. Some of the common use cases are: 

• Search engines 
• Social media platforms 
• Earth observation platforms 
• E-commerce giants 
• Financial institutions

Example form financial domain: 

• One of the major applications of large real-time data handling in financial institutions is fraud awareness, fraud detection and fraud avoidance. 
• A bank or financial intermediary handling payment processing may receive millions of transaction intimations every second. It requires instant verification with multiple stakeholders, validation and approval or rejection. 
• ELT data processing helps to achieve faster data processing, decision-making and the highest level of fraud avoidance. 

Benefits of ELT 

• Real-time data storage, testing and control - ELT pipelines load data in real-time. It allows the data management team to run tests and control processing flow with no significant delay.  
• Faster versioning of analysis models - Faster warehousing helps analysts and scientists run multiple versions of business models without a major time delay. As a result, a data team can achieve continuous integration and delivery.  
• Unmatched flexibility and scalability - ELT can load a wide variety of raw data and ELT pipelines can be reused without major or intensive changes. 
• Compliance and transparency to data users—The absence of cleaning and transformation before loading the data allows users to be highly transparent regarding extracted raw data. 
• The highest level of cost-efficiency - Flexibility with data formats and less transformation-related processing load make ELT cost-effective for big data users.

Challenges of ELT 

• Data privacy issues - As raw data is stored in warehouses without cleaning or processing, unsolicited private data can get extracted and loaded. It can cause serious compliance issues.   
• Processing of big data - ELT involving big data can require significant cleaning and processing if sources are not reliable. 
• Complex transformation - Transformation of ELT data to a target format can require complex coding and scripting.  

When to choose ELT? 

ELT is the preferred choice for data extraction and integration in cases like: 

• Businesses generating big data  
• Time-sensitive data warehousing 
• Businesses handling structured and unstructured data types 
• Businesses requiring real-time analytics and intelligence 

ELT use cases

ELT is becoming popular among every industry handling large data. However, the early adopters of ELT are: 

• Video streaming sites 
• Online trading platforms
• Digital publishing platforms 
• Social media platforms 

Best practices for ELT implementation

1. Learn about the basic transformation layer requirements 
2. Maximise source quality as per the transformation layer requirements 
3. Finalise cloud storage for warehousing 
4. Design testing methodology 
5. Automate extraction and loading 
6. Implement testing and maintain log 
7. Hand over access to end users

ELT vs ETL which is better? 

The answer to ELT vs ETL depends on the business use cases. However, almost every company, using cloud-based data warehouses and working with big data, prefers ELT for flexibility, scalability and processing efficiency.