As a Data Engineer, I collect, extract and transform raw data in order to provide clean, reliable and usable data.
📘 Introduction Real-time data ingestion is a critical part of modern data architectures. Organizations need to process and store continuous streams of information for analytics, monitoring, and machine learning. Databricks, with the combined power of PySpark and Delta Lake, provides an efficient way to build end-to-end streaming pipelines that handle data...
📘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...
📘 Introduction Every Spark application tells a story — a story of how your code travels from a high-level command in Python or Scala to a fully distributed computation running across dozens or even hundreds of executors. Behind the scenes, Spark organizes this work into jobs, stages, and tasks — the building blocks...
📘Introduction In this dbt tutorial, you’ll learn how to supercharge your dbt models with Jinja, the templating language that brings flexibility, automation, and scalability to your SQL transformations. Understanding Jinja in dbt will unlock a whole new level of productivity — letting you write dynamic, reusable, and DRY (Don’t...