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In this video, we will discuss a common question among data engineers: Should we use Apache Spark or dbt (data build tool) for data transformation? We'll compare these two powerful tools side-by-side to help you decide which is right for your project. We'll explore what Spark and dbt are, how they work, their key features, and when to use each one. We'll also discuss how they differ from traditional data processing tools and address the debate around ETL vs ELT. This video covers: What is Apache Spark? (Big data processing engine) What is DBT ? (SQL-based transformation tool) Spark vs DBT: Key differences and similarities When to use Apache Spark for data transformation When to use dbt for data transformation Spark's distributed computing and multi-language support DBT's SQL-based transformations and automated testing Spark for big data processing, real-time analytics, and machine learning DBT for data modeling and transformation within a data warehouse Rule of thumb: Big data = Spark, Simple transformations within a DataWarehouse = DBT Whether you're a seasoned data engineer or just starting out, this video will provide valuable insights into the world of data transformation. Want to learn Apache Spark using Free Databricks cluster set up? That too with practical assignments and use cases? Check out our Udemy course below https://www.udemy.com/course/apache-s... Leave your comments and questions below! We'd love to hear your thoughts on Spark vs dbt. Don't forget to LIKE and SUBSCRIBE for more data engineering content! Continue Your Learning Journey here: Full Playlist: • Apache Spark for Beginners: Full Course Us... Previous Video: • Apache Spark vs Databricks: Key Difference... Connect with Us: Newsletter: http://notifyme.itversity.com LinkedIn: / itversity Facebook: / itversity Twitter: / itversity Instagram: / itversity Join this channel to get access to perks: / @itversity #cloudcomputing #ApacheSpark #DBT #DataTransformation #dataengineering #BigData #DataAnalysis