Implementing a data warehouse necessitates an understanding of the distinction between ETL (extract, transform, load) and ELT (extract, load, transform) processes. In today’s business world, vast amounts of data are generated. Surprisingly, a recent Seagate-commissioned IDC survey of 1500 global enterprise leaders found that 68% of this data remains unused. Many companies overlook the importance of data analytics. According to BARC research, businesses employing big data have seen an 8% increase in profits and a 10% reduction in overall costs. 

However, many organizations accumulate vast amounts of data without a clear strategy for storage and utilization. This is where data warehouses and data lakes play a crucial role. 

Data warehouses enhance the speed and efficiency of accessing diverse datasets from various sources, empowering decision-makers to derive valuable insights for improved business and marketing strategies. Both ETL and ELT enable companies to consolidate data from multiple databases into a single repository, although they differ in their data pipeline approaches. Let’s explore these differences. 

Understanding Data Pipeline 

A data pipeline serves a single function: extracting data from its source and transmitting it to its destination. In this context, the source comprises data gathered from various systems, while the destination is the location where the data is loaded. Constructing data pipelines entails data processing to uphold effective data governance. Data integration processes primarily come in two forms: ETL and ELT. 

What Is ETL In Data Integration? 

When we gather raw data from diverse sources, it’s essential to refine it into a structured and understandable format. After formatting, the data is transferred to a data warehouse for in-depth analysis. This entire procedure is referred to as ETL, involving the sequential steps of data Extraction, Transformation, and Loading. 

During ETL, extracted data is directed to a processing server where it undergoes transformation to align with SQL-based standards, ensuring compliance. There are several ETL tools available for this purpose, including: 

1. Talend Open Studio 

2. AWS Glue 

3. Azure Data Factory 

4. Google Cloud Dataflow 

5. Microsoft SSIS 

Benefits Of ETL 

1. Scalability

ETL enhances data scalability and accelerates analysis by structuring and transforming data before loading, optimizing it for specific use cases. 

2. Compliance

ETL simplifies compliance with regulations like HIPAA and GDPR by removing sensitive data before it reaches the target system. 

3. Accelerated Analysis

ETL enables quicker data queries compared to unstructured data, resulting in faster analysis. 

4. Versatility

ETL can be deployed in both on-premises and cloud environments, offering flexibility in implementation. 

What Is ELT? 

Following data extraction, it’s initially loaded into a data warehouse in its raw state and subsequently transformed within the storage for advanced analysis. This comprehensive process is known as ELT, encompassing data Extraction, Loading into a data repository, and Transformation into a more interpretable format. 

Within the ELT data integration process, tasks such as data cleansing, enrichment, and transformation take place directly within the data warehouse, employing a database engine rather than a dedicated ETL engine. Notable ELT tools for this purpose include: 

1. Amazon Snowflake 

2. Amazon Redshift 

3. Google BigQuery 

4. Microsoft Azure 

Benefits Of ELT

1. Real-time Analysis

ELT enables users to perform real-time data analysis without the need to wait for additional extraction and transformation steps. 

2. Cost-Efficient Maintenance

ELT’s cloud-based transformation process translates to lower infrastructure maintenance costs. 

3. Comprehensive Data Access

ELT centralizes data in the data lake, granting tools access to both structured and unstructured data in its loaded form. 

4. Expedited Loading

Data is promptly loaded into the data lake without prior transformation, accelerating the availability of data for analysis. 

ETL vs ELT – What is the difference 

The key distinctions between ETL and ELT are evident in two primary factors: 

1. Transformation Location

  • ETL carries out data transformation in a separate processing server. 
  • ELT performs data transformation directly within the data repository. 

2. Data State 

  • ETL transforms data before sending it to the warehouse. 
  • ELT sends raw data to the repository without prior transformation. 

Parameters 

ETL 

ELT 
Transform Raw data is transformed on the processing server. 
Raw data is transformed inside the target system. 
 

 

Data storage 

ETL is the traditional process for transforming and incorporating structured or relational data into a cloud-based or on-premises data warehouse. 

 

ELT supports data warehouses, data lakes, data marts, etc. 
 

 

Size and type of data 

ETL can be leveraged for small data sets which require complex transformation. 

 

ELT is suited for both structured and unstructured data of any size. 
 

 

Security 

Pre-load transformation can eliminate PII. 

 

As ELT loads the data directly, more privacy safeguards are required. 
 

 

Code-based transformation Transformation occurs on the secondary server. As a result, transforming large datasets can take longer. 
Transformation is performed in databases. The transformation step takes little time but can slow down the querying and analysis processes 
 

 

Compliance 

ETL is better suited for compliance with GDPR, HIPAA, and CCPA standards. 

 

There is more risk of a security breach in the case of ELT. Hence, it is difficult to comply with GDPR, HIPPA, etc. 
 

 

Data output 

The output only comprises of structured data. 

 

ELT process offers structured, semi-structured and unstructured output data. 
 

 

Re-queries 

As data is transformed before entering the destination, re-query is not possible. 

 

Raw data is directly loaded in ELT, making it possible to run re-queries multiple times. 
 

 

Cost 

As it requires an additional server, the cost is comparatively higher. 

 

With no extra server required, the cost is low. 
 

 

Maintenance  The extra server needs more maintenance. 
With fewer systems, the maintenance burden is reduced. 
 

 

Hardware 

The traditional, on-premises ETL process requires more hardware. 

 

As the ELT process is cloud-based, no additional hardware is required. 

 

ETL vs ELT: What To Choose? 

ETL, which stands for Extract, Transform, Load, has been in use since the 1970s, primarily due to the increasing volume of diverse data sources. As the demand for data warehouses grew, ETL became increasingly vital. However, with the advent of cloud computing and cloud storage in the 2000s, a new approach called ELT (Extract, Load, Transform) emerged. 

When it comes to achieving faster data processing, ELT is often the most suitable choice. On the other hand, ETL is preferred for its robust security measures and scalability in data analytics. Both ETL and ELT come with their own set of advantages and disadvantages. Therefore, the choice between these data pipeline processes should be based on your specific requirements. 

It is highly recommended to seek assistance from a service provider with expertise in the field of data analytics and data science. This will empower you to make informed decisions and ultimately lead to improved return on investment (ROI).