• Snowflake DBT Tools

  • Dec 5 2023
  • Length: 25 mins
  • Podcast

Snowflake DBT Tools

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  • Summary

  • The podcast today is the latest episode of Beyond Transformation, where we talk about Snowflake DBT or data build tools. To help with the topic, Prabhash Thakur from Beyond Key joined.    Beyond Transformation is brought to you by Beyond Key. Beyond Key is a software development solutions organization. One of the services that we offer is Snowflake. DBT interacts with Snowflake.   Snowflake DBT Overview Dbt is short for a data build tool. It's a development framework combining modular SQL and software engineering best practices to make your data transformation reliable, fast, and fun.  Dbt is familiar to those who know how to write SQL select statements. They have the power to build their models, write test scenarios, and schedule their jobs to produce reliable, actionable data sets.   When you have data, you need to transform the data to give you business insight so that you can build the report on top of this. DBT acts as an orchestration for your transformation.   What is Dbt?   First, dbt is not a data warehousing solution. For example, in data warehousing, you follow the ETL (extract, transform, and load) approach,    In ETL, you first extract and load full raw data into your data warehouse that will act as a data link, and then you do the transformation on top of that raw data.  You then always have the true data, so you can perform as many transformations as you want. DBT helps you with the “T” or the transformation part of ELT.   Analytical Engineering: Data Engineering and Data Analyst   DBT uses another concept called analytical engineering. Analytical engineering acts as a bridge between data engineering and data analysis.   When we talk about data engineering, we build custom data integrations to manage the overall pipeline orchestration. Then, we deploy machine learning endpoints to build and maintain the data platforms.   Data engineering folks will then do some performance optimization. Analytical engineering provides clean, transformed data ready for analysis using software engineering best practices.   For example, delivering your models into small sprints. That's agile methodology. It's very difficult to perform agile methodology in data engineering because it is not enabled everywhere.   DBT comes with that. The tool documents the transformation in case anyone needs to review how the data transformed, what logic was used for the process, and the data sources.   On the other hand, a data analyst will do deep inside work like why did this point spike last month? What are the best acquisition channels? Then, they will work with the business users to understand data requirements to build forecasting dashboards.   DBT is key to analytical engineering because it maintains the documentation and trains business users how to use the data visualization created from the transformation of insights. As we said earlier, DBT is the “T” or the transformation of data.   How can you use DBT to transform your data into usable insights?   DBT is not connected to your extraction tool like how data is coming inside Snowflake or other data warehousing solutions like:   BigQuery   Databricks   Postgres   Redshift   Spark   Azure Synapse    Teradata   The number of platforms connected to DBT keeps changing. You need to take the data and load it in raw format into your data platform; DBT will connect your data platform.   It will use your data platform's compute and storage resources. You can access the DBT through your account credentials or use service account credentials to perform the transformation.   How can you use DBT to transform your data into usable insights with the right BI tool?  You can start building your DBT models by creating SQL files with select statements. DBT will do the rest for you.   For executives, an SQL file is like a CSV file. You create a .SQL file that works with any code editor like Notepad++ or VS code to store relational ...
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