ETL vs ELT: advantages, challenges, and how to navigate the choice of the right data architecture.
In today's data management landscape, companies often have to decide between two main approaches to data processing: ETL and ELT.
But what is the difference between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) and how to choose the right solution for data warehouse?
In this article, we will explore the meaning of ETL and ELT, analyze the main differences between these two processes, and understand which of the two approaches is best suited for the needs of modern businesses.
ETL
ETL stands for Extract, Transform, Load. This data treatment process is traditionally used to extract data from various sources (such as databases, CSV files, APIs), transform them into a standardized format, and then load them into a data warehouse for analysis.
Data extraction: The process begins with the extraction of data from various sources. These can be relational databases, log files, APIs, or any other system that contains relevant information.
Data transformation: After extraction, the data is transformed into a format that can be easily analyzed. This step can include operations such as data cleaning, format standardization, handling null values, and integration of different data sources.
Loading data: Once the data have been transformed, they are loaded into the data warehouse for use in analytical activities and reporting.
When to use ETL?
ETL is suitable when working with structured data and when there is a need to ensure complete cleansing and transformation of the data before it is loaded into the data warehouse. It is a popular choice for traditional data warehouses and when data quality is crucial. However, it has some limitations:
ETL
ELT, which stands for Extract, Load, Transform, is a more modern and faster approach to handling data in which the order of operations changes from ETL. In this case, data are extracted from sources and loaded directly into the data warehouse before being transformed.
Data extraction.: As in ETL, data are extracted from different sources.
Loading into the data warehouse: The main difference with ETL is that the data is loaded into the database prior to transformation. In this way, the resources of the data warehouse itself are used to perform the transformation operations.
Data transformation: Once loaded, data are transformed directly into the database, using internal computing power. Transformations can include aggregation, join, or other complex operations, but all are performed after loading.
When to use ELT?
ELT is particularly advantageous when working with structured, semi-structured and unstructured data. The main features that make it preferable are:
The main differences between ETL and ELT, in comparison:
| FEATURE | ETL | ELT |
|---|---|---|
| Speed | Slower, as data must be transformed before loading. | Faster, as data are loaded before being transformed. |
| Cost | More expensive, as it requires external tools for transformation. | More economical, as it takes advantage of internal database resources. |
| Managed data | Prefers structured data. | It handles structured, semi-structured and unstructured data. |
| Security | Security is customized during transformation. | Security is handled directly in the target database. |
| Scalability | Less scalable in big data environments. | Ideal for cloud and big data environments due to its scalability. |
Rivery: a modern ELT-first approach to data migration
In the landscape of data management platforms, Rivery stands out for its fully cloud-native architecture and its low-code approach, which simplifies the process of extracting, uploading and transforming information.
Due to its 100% SaaS nature, Rivery eliminates the need for complex installations or maintenance, offering companies a scalable and flexible tool to manage their data. The platform is particularly effective in migrating from on-premise systems to cloud data warehouses, supporting both one-time migrations and phased data transfer processes. In addition, its ability to process large volumes of data quickly and without infrastructure limitations makes it an ideal solution for companies with advanced analytical needs and frequent synchronization.
Are you looking for a more efficient solution for your ETL process?
Rivery is the breakthrough we've been waiting for: a modern data integration platform, based on ELT and Change Data Capture (CDC), that provides real-time updates and frictionless data management.
By partnering with Boomi, we can now offer you the best of both worlds: the power of Boomi integration combined with the flexibility and cloud-native approach of Rivery.
