I use the solution for transformation. When we perform the ELT process, we need to transform the data according to the business requirements. We can also use the tool for testing.

dbt Platform
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Elevating Our Data Stack
DBT at my point of view
Data can be transformed in batch
Ease to use, it has lot of good features similar in Django web application
Persistent cluster is required for running the sql statements.
Like Presto/Hive it can’t be connected to BI Directly
Converts SQL select statements into Tables or Views
Supports DW process such as incremental, SCD etc.,
Graphical representation of pipelines
We majorly use DBT cloud for ETL in the organization
DBT(Data Build Tool) to build Excellent data models for Quickly and collaboratively
i can use and configure the transformation as per the object like table,view or incrementalalong with features like auto-generated lineage graphs and can perform native testing with few lines of codes in a YAML file and can able to re use them.
and Performing CI/CD pipeline tests along with data migration to targets like Bigquery.
A game changer for dashboarding
It was good
Best data modeling tool
easy to use and implement
dbt learning courses provided by dbt are super usefull
data sharing and orchestration is super easy
development in cloud ide is very good
custoer support is extreme fast and efficient
integration with snowflake and GitHub is easy
Using daily this tool for building data models
dbt also solves and give beautifull lineages, from where your source data is traversrsing to final mart layer.
table level lineage is provided by dbt and is super usefull
reporting on a single layer is solved by dbt, meaning developer need not to login to data warehouse and dot the development.
dbt separates out the data warehouse from modelling layer
Overwhelming when it comes to optimize and centralize your big data.
2. The incremental model runs greatly helped me in optimizing large data models as I was dealing with billions of rows of data.
When I started using DBT, I was able to quickly determine and find the staging and intermediate layers for the purpose of creating a final layer and the documentation it creates was awesome.
I am talking about dbt docs generate and dbt docs serve.
Great experience with dbt
It is easy to integrate with other tools like integration.
Developer-friendly and easy to use, but doesn't have many optimization options
What is our primary use case?
What is most valuable?
The product is developer-friendly. A person who understands SQL can develop the transformation. We do not have to learn a lot of things like we do for new tools. The tool has good testing and data quality features. Implementing Slowly Changing Dimensions through dbt has been easy. It is very easy for a beginner to use the product.
The tool provides multiple technical advantages if we use Snowflake. It is a good transformation tool because it is SQL-oriented. It has data lineage, data quality, and workflow scheduler.
What needs improvement?
The solution must add more Python-based implementations. Transformation tools require Python-based implementations. It would give developers more freedom to use SQL or Python. We can use Python, but it is not that user-friendly. The product doesn't have a lot of optimization options.
For how long have I used the solution?
I have been using the solution for almost three years.
What do I think about the stability of the solution?
There are no problems with the product’s stability.
What do I think about the scalability of the solution?
We have at least 25 to 50 users in our organization.
How was the initial setup?
The solution is deployed on the cloud. It can be deployed on AWS, Azure, or GCP. The initial setup is easy.
What's my experience with pricing, setup cost, and licensing?
The solution’s pricing is affordable.
What other advice do I have?
We also use stored procedures and Talend. They are not replaced by dbt completely. We use dbt only for the transformation process. My recommendations would depend on an organization’s requirements and problems. I will recommend the tool to others. The product is developer-friendly. However, it is still dependent on the data warehouse for big data and optimization.
It's just a SQL transformation tool. It doesn't have a lot of optimization options like Spark. It's a good tool for Snowflake. If it were only for Snowflake, I would have rated it an eight out of ten. However, there are other data platforms.
Overall, I rate the tool a six and a half out of ten.