In the past few years, organizations are migrating their on-prem data sources to cloud data sources. This has significantly reduced the cost and single point of failures and has increased performance and scalability.
ETL is currently used for on-prem where the data is collected from different vendors, gets transformed using some business logic and then it is finally loaded in the data warehouse. Whereas ELT is used on cloud platforms where we collect the data from various vendors and store it into a cloud data warehouse and then transform the data on the cloud itself.
The main advantage of ELT over ETL is we can have unlimited access to all our data at any time. ELT also delivers faster data ingestion unlike ETL where data can be loaded and transformed simultaneously.
In the ELT (Extract Load Transform) of data, the T is a crucial step as the transformed data is used by the business analysts and decision makers to get insights.
DBT is a data transformation tool that enables data/business analysts or developers to transform the data in the cloud data warehouse.
As we keep having lots of data now more than ever before and ELT being a new trend in town, there is a shortage of data engineers who can manage the data and make it accessible to use. Most of the data is not even analytics-ready and this is where DBT comes into play and makes the data transformation and analytics easier. DBT makes use of simple SQL select statements and effectively transforms the data using code. Anyone who has a basic understanding of databases and SQL can write such queries which would transform the data according to the business requirements.
DBT also allows one to test the data quality, documentation, and deployment along with the code. The ease of using this tool makes anyone with basic coding skills create data pipelines and therefore it reduces the entry barrier to this data world.
Some advantages of DBT:
Code Re-usability: With dbt one can write a model (SQL select statement which creates a table/view) once and then reference it within your other models. It also makes the code modular, we can re-use instead of re-code.
**Testing: ** With DBT, the testing of data quality becomes easier as alert our teams related to data quality issues as soon as possible. We can write tests like not null, unique, etc. to source and target tables/models.
**Speed: ** DBT makes the deployment and creation of models a lot faster. As our models are modular, we can create it once and reference it at other places, we are not executing the same code repeatedly and that saves lots of time.
Documentation: Dbt allows us to add descriptions of our created data models into the code.
DBT is the new hot data transformation tool that has been getting massive adoption by the organisations which deal with lots of data. It is contributing to the growth of such organisations in several ways:
Accessibility of ELT: With dbt, it is easy to make data transformation through code and with analysts having less knowledge of data engineering and basic knowledge of SQL it makes this ELT process very accessible and analysts can now also create such data pipelines as well.
Cloud Flexibility: DBT is designed in such a way that it can work with any cloud vendors like AWS, Google Cloud, Azure, etc. and make transformations on the cloud data warehouse.
Documentation and Testing: The testing in DBT is easy and can be easily automated and any failure would also send alerts to the respective teams. Documentation is also generated by DBT automatically about various models, snapshots, tests, and dependencies.
DBT is Fast: DBT has a simple and efficient development style which is in combination with SQL statements and good practices which makes fast and reliable data pipelines.
Saves cost: As the data analysts with basic SQL can build data pipelines, it saves costs for organizations.
From the above case study, it is quite clear that data will be more valuable than ever before and DBT is one of the most efficient tools present in the market that will help us in data transformation. Here at Gemini Solutions, we are making use of this new tool in many of our projects, and it is making data transformation easier than ever before.
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