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The Ultimate Guide to ELT (Extract, Load, Transform) in 2024

The Ultimate Guide to ELT (Extract, Load, Transform) in 2024The Ultimate Guide to ELT (Extract, Load, Transform) in 2024

ELT (Extract, Load, Transform), and ETL (Extract, Transform, Load) are data integration approaches that facilitate the transfer and processing of data from multiple sources into a destination storage system. These methods ensure the efficient handling and organization of data, enabling seamless management and analysis for various business applications.

In this article, we will explain what ELT is, discussing its capabilities, use cases, and advantages. Furthermore, we will highlight the key differences between ELT and its counterpart, ETL, to comprehensively understand these data integration methodologies.

What is ELT?

ELT stands for Extract, Load, and Transform. It is a type of data integration process used to transfer and manipulate raw data from a source system to a target system, such as a data lake or data warehouse. ELT is the inverse of the ETL (Extract, Transform, Load) method, which transforms data before loading it into the target system. This allows for faster loading times.

How ELT works

ELT consists of three main steps: Extract, Load, and Transform.

1. Extract

Data is extracted from multiple source systems, including NoSQL databases, CRM and ERP systems, and websites. To extract data from source locations, you need to establish connections to them using a data extraction tool or library.

The variety of data sources and types introduces several challenges during the data integration process. For instance, relational databases (such as MySQL) contain structured data, while text files (like logs) contain unstructured data. When working with such a diverse range of data sets, it is crucial to approach data extraction to  address the specific requirements of each data source and type.

Bright Data’s Web Scraper IDE enables businesses to extract mass data from any data source. They provide pre-made web scraper templates, making it easy for individuals with limited technical skills to extract the desired data.

Bright Data's Web Scraper IDE enables users to extract desired data from target data sources automatically.

2. Load

The second stage of the ELT process is the “Load” step. Extracted data is loaded into the target data store, typically a cloud data warehouse, in its original raw format. This step includes choosing a target storage system, mapping source data to the target schema, and selecting the loading method. The data loading process varies based on variables such as the capabilities of the target system. Some of the most common data-loading methods include:

  • Bulk loading: Bulk loading loads large amounts of data into a target data storage system in a single operation. It can reduce the overhead of multiple smaller operations.
  • Batch loading: In some cases, it may be necessary to load data in smaller, predefined chunks. Batch loading makes identifying and resolving the issue easy,  as the entire operation is divided into manageable batches.
  • Streaming loading: Streaming loading is often used for time-sensitive or streaming data sources. It loads data into the target store in near-real-time as the data becomes available.

3. Transform

Once the data is loaded into the target system, the data transformation process begins. In this stage, raw data is cleaned, aggregated, and transformed for business intelligence (BI) and big data analytics.

ETL vs ELT – what is the difference?

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration methods that can  extract, process, and store data from multiple data sources into target storage. Although they share similarities, there are notable differences between ETL and ELT. The main difference between ELT and ETL lies in the sequence and location of the data transformation step.

Figure 1: Key distinctions between ETL and ELT

This table summarizes the key differences between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).

The order of steps:

ETL: The transformation step is performed in the ETL process before loading the data into the target system. The target system receives data in a format that has already been transformed.

ELT: In the ELT process, the transformation step occurs after the data has been loaded into the target store, such as a data warehouse or data lake. ELT does not transform raw data in transit. It loads data into the target database before any transformation occurs. ELT transforms data within the target data store as required.

The target data store:

ETL and ELT tools can both be adapted to work with data lakes. However, it is important to note that the target data store is usually a data lake in the ELT process. Unlike traditional data warehouses, data lakes can process and store structured and unstructured data at a massive scale.

Transformation process

ETL: In ETL, data scientists may have limited flexibility in manipulating raw data, making tailoring to specific requirements difficult.

ELT: It enables data scientists to tailor data transformation to the specific needs of each project.

Top 5 benefits of ELT

ELT offers several benefits over the traditional ETL (Extract, Transform, Load) method. Some of the main advantages of ELT include:

  1. Faster data availability: ELT provides faster data availability by loading raw data directly into the target storage system before performing any transformations. This is particularly beneficial for real-time or near-real-time data analysis requirements.
  2. Simplified data pipeline: ELT eliminates the need for a separate intermediary system or dedicated ETL tool by performing transformations directly within the target data store. This simplifies the overall data pipeline and reduces the complexity associated with ETL tools.
  3. Real-time or near-real-time processing: ELT enables real-time or near-real-time data processing, which can benefit organizations that require timely insights, such as responsive customer service. It continuously analyzes incoming data to provide actionable insights in real-time.
  4. Data transformation flexibility: ELT allows data engineers to perform custom data transformations within the target data store based on their specific requirements. Data scientists can load raw data from multiple sources directly into a data lake or a cloud-based data warehouse. The collected raw data is available for data analysis since it is not transformed before loading.
  5. Reducing server scaling issues: ELT (Extract, Load, Transform) enables organizations to mitigate server scaling issues associated with traditional ETL (Extract, Transform, Load) processes. It utilizes the power and scalability of modern data storage systems, such as cloud-based data warehouses and data lakes. For instance, modern cloud data warehouses allow organizations to handle large volumes of raw data and scale resources on demand.

Challenges of ELT

ELT and ETL approaches employ distinct techniques for managing data integration. Organizations implementing  ETL processes may encounter difficulties when transitioning to an ELT framework. When you switch from an ETL to an ELT architecture, the transformation step is moved from an intermediary system or ETL tool to the target data storage system. There is a risk of data interception or leakage during data transfer from source systems to target storage. Utilizing  encryption and securing communication protocols is crucial to minimize such risks.

Use cases of ELT

ELT is mostly used in scenarios involving large amounts of data or real-time data processing, such as financial market analysis and social media monitoring. Here are some typical applications of ELT:

  • Real-time or near-real-time analytics: The ELT approach allows businesses to process and analyze data as it is generated or received. Data is continuously ingested as it is generated into the target warehouse or data lake without any initial transformation.
  • Data warehousing: ELT can take advantage of the processing capabilities of modern data warehouses, such as Snowflake and Amazon Redshift. This enables organizations to transform and process massive data sets efficiently.
  • Large-scale data transformation: Many industries generate and process large amounts of data to gain insights and optimize operations. For example, online retailers and brick-and-mortar stores collect large amounts of data on customer behavior. This category may also include IoT devices and connected systems that produce large amounts of sensor data that can be used to optimize processes. ELT processes are designed to handle massive amounts of structured and unstructured, making them ideal for big data analytics.

Further reading

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.

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