Data Transformation

Unlocking the Power of Data Transformations with dbt (Part Ⅰ)

In the first part of our three part series on data transformation with dbt, we give a brief overview of dbt, the recent shift from ETL to ELT, and lastly we talk about data transformation & its importance.
Arti Gupta
5 min to read

Effective data transformation is critical for businesses seeking meaningful insights in today's data-driven environment. In our latest three-part blog series on data transformation we will delve deeper into the data transformation power of dbt that enables businesses to streamline their data transformation processes. In the first part of our series we will start with a quick sneak peek into dbt,  journey of transition from ETL to ELT, and last but definitely not the least about data transformation and its importance. When businesses start leveraging dbt they get the most value from their data and achieve quick decision-making. 

What is dbt?

The open-source analytics engineering tool dbt (Data Build Tool) streamlines and improves the data transformation process within a data warehouse. It allows users to generate analysis-ready datasets by defining SQL-based transformations known as models. dbt promotes collaboration between stakeholders by leveraging software engineering best practices such as version control, testing, and documentation. It also readily connects with common data warehouses and provides dynamic transformations via macros and Jinja templating. As dbt can be efficiently used to develop tests that guarantee the accuracy and reliability of data. These tests can further be used to verify the accuracy and dependability of the outputs at various points in the transformation pipeline. Although dbt doesn't deal with data management activities like governance, lineage, or cataloging directly, it can interact with specialized tools for thorough data management. Additionally, by utilizing its transformation capabilities and producing curated data sets, dbt is helpful for reverse ETL, enabling data synchronization from the data warehouse to operational systems.

From ETL to ELT

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two different approaches to data integration and transformation. Here's a brief differentiation between the two:

ETL vs ELT_DataChannel_dbt_data build tool


  • Extract: In this step, data is extracted from numerous sources, such as databases, APIs, or files, and moves it to a staging location.
  • Transform: To prepare it for the target data model, the retrieved data is transformed by cleaning, filtering, aggregating, and joining.
  • Load: The transformed data is then loaded into the destination, typically a data warehouse or a data mart.


  • Extract: ELT, like ETL, includes extracting data from various sources, but instead of transferring it to a staging area, the raw data is put directly into the target system.
  • Load: The extracted data is placed into a data warehouse, data lake, or other comparable storage environment during ELT.
  • Transform: The transformation occurs within the target system itself, utilizing the storage platform's processing power. SQL or other data manipulation languages are used directly on the raw data to execute transformations.

Compared to conventional ETL (Extract, Transform, Load) operations, ELT (Extract, Load, Transform) has advantages. Real-time analytics, scalability, cost effectiveness, and adaptability to various data sources are all features of ELT. It makes use of the capabilities of modern data warehouses, enabling direct loading of raw data and on-demand modifications. Advances in data warehousing, rising data volume and variety, and cost optimization are driving the transition from ETL to ELT. Organizations can now handle massive amounts of data, gain quicker insights, and save infrastructure costs while still meeting changing data needs thanks to this transformation.

What is Data Transformation and why is it important?

The process of turning raw data into a format appropriate for analysis and decision-making is known as data transformation. Data transformation is critical in various ways in ELT workflows. For starters, by standardizing and normalizing the data, it enables the integration of diverse data sources. Second, it enhances data quality by addressing dupes & inconsistencies through cleansing procedures and data validation. Third, data transformation enables data aggregation and summarization, which reduces data size and improves query performance. It also employs business rules and calculations, preparing the data for analysis and aligning it with specific requirements. 

ELT maximizes the value and usefulness of data by conducting transformations within the data storage system, allowing organizations to gain relevant insights and make educated decisions based on high-quality, well-structured data.

Stay tuned for our upcoming blog, in which we will go over data transformation with dbt and the myriad of benefits it provides, as well as how you can use dbt to your advantage.

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