📘Introduction In this hands-on dbt tutorial, you’ll learn how to configure your sources.yml file to handle multiple environments (DEV and PROD) dynamically using dbt’s powerful target variable. This will ensure that your models always connect to the correct database, based on the environment you are currently working...
📘Introduction When working with dbt (data build tool), YAML files are the backbone of your project’s configuration. They define how dbt behaves, how your models connect to data sources, and how metadata, documentation, and tests are managed. Understanding these YAML files and knowing where they are located within your...
📘 Introduction When processing massive datasets in PySpark, it’s often necessary to uniquely identify rows or efficiently detect changes across records. Using multiple columns as a composite key can quickly become cumbersome and inefficient — especially during joins or deduplication. A better solution is to generate a single hash value derived...
📘Introduction In this hands-on dbt tutorial, you’ll learn how to switch between development (DEV) and production (PROD) environments using target variables in dbt. You’ll see how to dynamically control where your models are built, and easily run commands against different data warehouses — all without changing a single line...
📘Introduction In this hands-on dbt tutorial, you’ll learn how to configure separate development (DEV) and production (PROD) environments to safely build, test, and deploy your data models. We’ll walk through why environment separation matters and how to configure your profiles.yml so you can switch between environments with...
📘 Introduction If you’re working with modern data stacks and want to transform raw data into clean, analytics-ready tables, then you’ve likely heard of dbt (Data Build Tool). dbt has quickly become the go-to framework for data transformation, modeling, and testing, empowering data teams to treat analytics like software...
📘 Introduction When running Spark jobs, you expect every task to share the workload evenly — but that’s not always the case. Sometimes, a few tasks take far longer than the rest, keeping the entire stage waiting. This imbalance, known as data skew, is one of the most common causes of...
