What is ELT (Extract, Load, Transform)? A Beginner’s Guide

ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. This transformation could involve cleaning, aggregating, or summarizing the data. ELT is commonly used in big data projects and real-time processing where speed and scalability are critical.

In the past, data was often stored in a single location, such as a database or a data warehouse. However, with the rise of the internet and cloud computing, data is now generated and stored across multiple sources and platforms. This dispersed data environment creates a challenge for businesses that need to access and analyze their data. ELT offers a solution to this challenge by allowing companies to extract data from various sources, load it into a central location, and then transform it for analysis.

The ELT process relies heavily on the power and scalability of modern data storage systems. By loading the data before transforming it, ELT takes full advantage of the computational power of these systems. This approach allows for faster data processing and more flexible data management compared to traditional methods.

This is part of a series of articles about ETL

In this article:

How the ELT Process Works

The Extract Phase

The extract phase is the first step in the ELT process. In this phase, data is collected from various sources. These sources could be databases, CRM systems, social media platforms, or any other place where data is stored. The extracted data is often raw and unstructured and may come in various formats such as text, images, audio, or video.

The extraction process requires careful planning to ensure data integrity. It’s crucial to understand the source systems and their structure, as well as the type and quality of data they produce. Often, the extraction process includes checks and balances to verify the accuracy and completeness of the extracted data.

The Load Phase

After the data is extracted, it’s loaded into a data storage system in the load phase. This system can be a database, a data warehouse, or a data lake, depending on the organization’s needs. The data is loaded as-is, without any transformation. This means that the data maintains its original format and structure, which can be beneficial for preserving the data’s original context and meaning.

Loading the data before transforming it allows for better scalability, as the data storage system can handle large volumes of data more efficiently. It also provides flexibility as the data can be transformed in different ways depending on the specific requirements of the analysis.

The Transform Phase

During this phase, the data is prepared for analysis. This preparation can involve various operations such as cleaning, filtering, aggregating, and summarizing the data. The goal of the transformation is to convert the raw data into a format that’s easy to analyze and interpret.

Transformation can be a complex process, especially when dealing with large volumes of data or complicated data structures. However, modern data storage systems have robust transformation capabilities that can handle these complexities at scale. Once the data is transformed, it’s ready for use in analysis, reporting, machine learning, or any other data-driven process.

ELT vs. ETL: What Is the Difference?

While ELT and ETL might seem similar at first glance, they differ significantly in their approach to data processing. The key difference lies in the order of the loading and transforming steps. In ETL, the data is transformed before loading it into the data storage system. This means that the transformation happens outside the data storage system, typically in a separate staging area.

On the other hand, in ELT, the data is loaded into the data storage system before transforming it. This means that the transformation happens within the data storage system. This approach has several advantages: 

  • It allows for better scalability as the data storage system can handle large volumes of data more efficiently. 
  • It provides more flexibility as the data can be transformed in different ways depending on the specific requirements of the analysis.

In terms of performance, ELT often has the upper hand as it leverages the power of modern data storage systems. However, ETL can be a better choice in scenarios where data quality and consistency are paramount, as the transformation process can include rigorous data cleaning and validation steps.

Learn more in our detailed guide to ETL tools (coming soon)

Challenges and Limitations of ELT

Managing Data Privacy and Security

One of the significant challenges of implementing ELT is managing data privacy and security. Given that ELT involves transferring vast amounts of data from one location to another, it poses significant privacy and security risks. The data is vulnerable to breaches during transit and even when stored in the data warehouse.

Moreover, since ELT allows for the storage of raw data, sensitive information may be exposed if adequate security measures are not in place. Therefore, it is critical to implement robust security protocols, such as encrypting data both at rest and during transit, to protect the integrity and confidentiality of the data.

Handling Large Volumes of Data

Another significant challenge of ELT is handling large volumes of data. ELT processes require substantial computational resources, which can be costly and time-consuming, especially when dealing with petabytes of data. Additionally, transferring such large volumes of data can put a strain on network resources, leading to bottlenecks and slowdowns in data transfer rates.

Furthermore, while ELT allows for real-time data processing, the sheer volume of data can make it challenging to process and analyze data in a timely manner. This can result in delays in gaining insights and making data-driven decisions, thereby affecting the organization’s competitiveness.

Complex Transformations and Querying

ELT involves complex transformations and querying, which can be challenging to manage and implement. Unlike the traditional Extract, Transform, Load (ETL) process, where transformations are performed before the data is loaded into the data warehouse, in ELT, transformations are performed after the data is loaded. This means that the data warehouse must be capable of handling more complex transformations and querying, often on unstructured data.

Moreover, the onus is on the data analysts and data scientists to write these queries and transformations, which requires a strong understanding of both the data and the business requirements. This can be a steep learning curve for many, sometimes leading to errors and inefficiencies in the ELT process.

4 Best Practices for Implementing ELT Pipelines

1. Proper Planning and Designing of the Data Pipeline

The first step towards successful ELT implementation is proper planning and design of the data pipeline. This involves understanding the business requirements, the source and type of data, the desired output, and the resources required for the ELT process.

The data pipeline should be designed to handle the volume, variety, and velocity of the data. This includes choosing the right data storage and processing technologies, designing the data schema, and planning the data transformations.

Furthermore, it is important to consider the scalability and flexibility of the data pipeline. It should be able to handle increases in data volume and changes in data structure without affecting the performance of the ELT process.

2. Implementing Strong Data Governance Measures

Implementing strong data governance measures is crucial in ELT. This involves establishing clear policies and procedures for data access, data quality, data privacy, and data security. These measures ensure that the data is reliable, accurate, and secure, thereby enhancing the value of the data and the trust in the ELT process.

Data governance also involves implementing data lineage and data cataloging. Data lineage helps track the data from its source to its destination, providing visibility into the data transformation process. On the other hand, data cataloging helps manage and discover data, making it easier for data analysts and data scientists to find and use the data.

3. Ensuring Data Quality and Consistency

Data quality and consistency are paramount in ELT. Since ELT involves storing raw data, it is essential to ensure that the data is of high quality and consistent. This can be achieved through data cleansing and data validation.

Data cleansing involves removing duplicates, correcting errors, and standardizing data. On the other hand, data validation ensures that the data conforms to the defined business rules and data standards.

Furthermore, it is important to continuously monitor data quality and consistency. This can be achieved through data profiling, which involves analyzing the data to identify anomalies, discrepancies, and inconsistencies in the data.

4. Continuous Monitoring and Optimization of the ELT Process

Lastly, continuous monitoring and optimization of the ELT process are essential for its successful implementation. This involves monitoring the performance of the ELT process, identifying bottlenecks, and optimizing the process to improve its efficiency and effectiveness.

Monitoring tools can be used to track the performance of the ELT process, such as data load times, data transformation times, and data query times. These metrics can provide insights into the performance of the ELT process and help identify areas for improvement.

Furthermore, the ELT process should be continuously optimized to handle the increasing volume of data and the changing business requirements. This can involve tuning the data transformations, optimizing the data queries, and scaling the data storage and processing resources.

Better data observability equals better data quality.

Implement end-to-end observability for your entire solutions stack so your team can ensure better data quality by managing, maintaining, and improving the quality of their data.