I am currently working with dbt and Snowflake together. We use dbt for data transformation purposes. We obtain the data and store the raw data directly into Snowflake, then perform all transformations using dbt to prepare the data for reporting purposes. We use dbt's modular SQL models. In dbt, we do not use full refresh or full data refresh. We have incremental strategies in place that only compute or transform incremental data, which operates in a CDC architecture. This approach is very fast, and we use it on a daily basis. We have scheduled all our dbt models using Airflow to run according to the scheduled time. We use dbt's testing framework and the inbuilt functionality of dbt testing. For example, we use dbt's built-in tests to identify not null values and determine how many not null columns and values exist in each column. Beyond the built-in functionality, we have written custom SQL scripts to create external test cases on our models. We ensure that incorrect or incomplete data does not go into the reporting layer because it can impact the business. We always perform dbt tests on our landing or raw data to ensure the correctness and completeness of the data before loading it into the final reporting layer. These reports are used by higher management, so we ensure that incorrect data is not published into the reporting layer for the Power BI reports. We use dbt's documentation site generator. In dbt, we have YML file functionality, which can be used for creating documentation for each model. Whenever we make modifications to a model, we always update the YML file so we can track the history of how frequently we change the model. When new team members join, they can refer to this documentation to understand the data lineage and the data transformation strategy of the project.
My main use case for dbt is for data transformation and data engineering.A specific example of how I use dbt for data transformation and engineering is that we use it to connect and ingest data from our Azure blob and S3 buckets, then transform through our glorified serving layers into our data platform. We use dbt to orchestrate our data engineering pipelines.
dbt is a transformational tool that empowers data teams to quickly build trusted data models, providing a shared language for analysts and engineering teams. Its flexibility and robust feature set make it a popular choice for modern data teams seeking efficiency.
Designed to integrate seamlessly with the data warehouse, dbt enables analytics engineers to transform raw data into reliable datasets for analysis. Its SQL-centric approach reduces the learning curve for users familiar with it,...
I am currently working with dbt and Snowflake together. We use dbt for data transformation purposes. We obtain the data and store the raw data directly into Snowflake, then perform all transformations using dbt to prepare the data for reporting purposes. We use dbt's modular SQL models. In dbt, we do not use full refresh or full data refresh. We have incremental strategies in place that only compute or transform incremental data, which operates in a CDC architecture. This approach is very fast, and we use it on a daily basis. We have scheduled all our dbt models using Airflow to run according to the scheduled time. We use dbt's testing framework and the inbuilt functionality of dbt testing. For example, we use dbt's built-in tests to identify not null values and determine how many not null columns and values exist in each column. Beyond the built-in functionality, we have written custom SQL scripts to create external test cases on our models. We ensure that incorrect or incomplete data does not go into the reporting layer because it can impact the business. We always perform dbt tests on our landing or raw data to ensure the correctness and completeness of the data before loading it into the final reporting layer. These reports are used by higher management, so we ensure that incorrect data is not published into the reporting layer for the Power BI reports. We use dbt's documentation site generator. In dbt, we have YML file functionality, which can be used for creating documentation for each model. Whenever we make modifications to a model, we always update the YML file so we can track the history of how frequently we change the model. When new team members join, they can refer to this documentation to understand the data lineage and the data transformation strategy of the project.
My main use case for dbt is for data transformation and data engineering.A specific example of how I use dbt for data transformation and engineering is that we use it to connect and ingest data from our Azure blob and S3 buckets, then transform through our glorified serving layers into our data platform. We use dbt to orchestrate our data engineering pipelines.
We use the solution to deal with data transformations inside different organizations.