Integration of DBT With Modern Data Stack Technologies

Author(s)	Rameshbabu Lakshmanasamy, Girish Ganachari
Country	USA
Abstract	In the current world where information is critical, the timely processing and analysis of data are essential in the decision-making process. Modern data stack is based on the cloud-native, modular, and scalable technology stack that makes the workflows more efficient and flexible. The dbt (Data Build Tool) is one of the critical components in the ecosystem working on SQL level transformations for the cloud data warehouses such as Snowflake and BigQuery. When compared to the other traditional ETL tools, dbt focuses more on the transformation layer, making the tasks easier for data analysts and engineers. This paper discusses how dbt fits into the modern data stack, how it differs from ETL, what tools it complements or interfaces with (like Airflow, Spark, and Kafka), and its implications for data governance and democratization
Keywords	Dbt, Snowflake, Bigquery, Spark, Kafka, ETL
Field	Computer Applications
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-25
Cite This	Integration of DBT With Modern Data Stack Technologies - Rameshbabu Lakshmanasamy, Girish Ganachari - IJFMR Volume 5, Issue 5, September-October 2023.

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research