Data Engineering Terms You Need to Know

Data Pipeline Data pipeline: Mapping data flow from collection to analysis for organizational insights. Source, process, deliver for analytics efficiency. Database vs Schema vs Table Database: Organized collection of related data, like a file cabinet for storing information. Schema: Blueprint defining structure and organization within a database. Table: Grid-like structure within a schema, where data … Read more

Slowly Changing Dimensions in Databricks

Introduction Slowly Changing Dimensions, or SCD is a data management concept that determines how tables handle data which change over time. For example, whether you want to overwrite values in the table or maybe retain their history. This can be determined by the SCD type to implement. There are three types of SCD which are … Read more

Streaming from Multiplex Bronze in Databricks

In this notebook, we are going to parse raw data from a single topic in our multiplexer bronze table. As you can see, in our architecture diagram, we will create the orders silver table. Let us start by copying our dataset files. Before starting, we need to cast our Kafka binary fields as strings. Here … Read more

Bronze Ingestion Patterns in Databricks

Learning Objectives You will understand the available ingestion models in the bronze layers and the difference between them, and you will learn how the ingested data will be promoted to the silver layer. When setting up ingestion into the bronze layer, we need to decide how input datasets should be mapped to the bronze tables. … Read more

Databricks Certified Data Engineer Professional

6 broad categories related to the For Databricks Certified Data Engineer Professional exam topics, which are: Having the Data Engineer Associate certification from Databricks is not a prerequisite to appear for the Professional-level certification exam. However, this exam assumes that you have the skills of a data engineer associate on Databricks platform. The source code … Read more

What is SQL and Why It Matters in Data Engineering

Introduction If you are starting a career in technology—especially in Data Engineering—there is one skill you will repeatedly hear about: SQL. Some beginners ignore it because they think modern tools like Python, AI, or cloud platforms are more important. Others underestimate it because it looks “too simple.” But here is the truth: SQL is the … Read more

Delta Lake Check Constraints Explained (Ensure Data Quality in Data Pipelines)

Introduction In modern data engineering, ensuring data quality is just as important as building scalable pipelines. No matter how advanced your architecture is, poor data quality can lead to incorrect analytics, broken dashboards, and unreliable machine learning models. This is where Delta Lake check constraints come into play. Check constraints allow you to enforce rules … Read more

Configuring Auto Loader for Reliable Data Ingestion (Complete Guide 2026)

Introduction In modern data engineering, ingesting data reliably and efficiently is one of the most critical challenges. With the rise of cloud-based data platforms, tools like Auto Loader (commonly used in Databricks) have become essential for building scalable and fault-tolerant data pipelines. Auto Loader is designed to automatically detect and process new files as they … Read more

SQL Indexing Explained for Beginners (Improve Query Performance in 2026)

Introduction As your database grows, you may start to notice that your SQL queries become slower. Simple queries that once took milliseconds may begin to take seconds—or even longer. This can impact application performance, user experience, and overall system efficiency. One of the most effective ways to solve this problem is SQL indexing. Indexing is … Read more