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Data transformation has long been a critical aspect of data management, with ETL (Extract, Transform, Load) being the traditional approach. However, with the advent of modern data technologies and cloud data warehouses, ELT (Extract, Load, Transform) has emerged as a powerful alternative. Let’s delve into the nuances of these approaches and explore their strengths and weaknesses in today’s data landscape.
ETL is a traditional approach to data integration that involves extracting data from various sources, transforming it outside the data warehouse, and then loading it into the target database. The transformation phase typically occurs on dedicated ETL servers or platforms before loading the data into the destination.
Data transformation is handled in the data pipeline.
Imagine a retail company that collects sales data from multiple stores and wants to consolidate this information into a centralized data warehouse for analysis. In an ETL scenario:
ELT flips the traditional ETL process by loading raw data into the data lake or warehouse first, then performing transformations within the warehouse using SQL, Python, or other programming languages. This approach leverages the processing power and scalability of modern cloud data warehouses for transformation tasks.
Consider a healthcare organization that collects patient data from multiple sources, including electronic health records and medical devices. In an ELT scenario:
By understanding the differences between ETL and ELT, along with examples and key vendors in each category, organizations can make informed decisions when designing their data integration and analytics workflows.
Peliqan adopts primarily ELT principles, leveraging the capabilities of data warehouses for transformation tasks. However, it introduces a unique twist by offering basic yet powerful transformations for SaaS data sources using Singer pipelines. Unlike typical ELT tools that land source data in raw format files in a data lake, Peliqan lands data directly into a relational data warehouse with specific column transformations and incremental patterns included.
In the dynamic landscape of data management, the choice between ETL and ELT depends on various factors such as data volume, complexity of transformations, and infrastructure considerations. While traditional ETL remains relevant for certain use cases, ELT offers compelling advantages, especially with the emergence of innovative tools like Peliqan that blend the best of both worlds.
Piet-Michiel Rappelet is a founder of Peliqan. Before Peliqan, Piet-Michiel co-founded Blendr.io, a no-code iPaaS integration platform. Blendr.io was acquired by Qlik in 2020 and Piet-Michiel became Director of Product Management Foundational Services at Qlik. Piet-Michiel’s primary interest is in scaling SaaS software and rolling out customer-oriented service teams. Piet-Michiel holds a Masters degree in mathematics, he lives with his wife and two kids in Belgium.