Course: Databricks Certified Data Engineer Associate - Preparation
Chapter Title: Introduction
Welcome to the "Introduction" chapter, which serves as the foundational entry point for this course and orients learners to purpose, structure, and expectations. This chapter explains why the course exists and what learners should aim to achieve by the end. Its primary goal is to equip you with conceptual frameworks and practical skills that will underpin later modules. Whether you are a novice or refreshing prior knowledge, the material here prepares you to proceed with confidence. The chapter outlines clear course objectives that scaffold learning from basic principles to applied tasks. Learning outcomes are described so you know what competencies you will have acquired upon completion. The course structure is presented in a modular format to support incremental mastery and flexible pacing. We also summarize essential resources, including readings, videos, interactive notebooks, and community forums. Active engagement is emphasized because discussion and collaboration substantially improve retention and insight. Assessments and feedback mechanisms are introduced to help you measure progress and iterate on your work. Time management and reflection are recommended as strategies to deepen understanding. Practical tips for using the learning platform and tools are provided to minimize technical friction. Finally, this introduction functions as a roadmap that invites curiosity, persistence, and constructive risk-taking as you begin your learning journey.
Lesson
Subtitle: Course Introduction & Databricks Ecosystem Overview
Learning Outcomes
- Identify the course objectives and expected learning outcomes.
- Describe the course structure, resources, and modes of participation.
- Explain core components of the Databricks platform (Notebooks, Delta Lake, Repos).
- Demonstrate initial setup steps for Community Edition and Azure Free Trial.
- Apply good study habits: active learning, reflection, and incremental practice.
Conceptual Explanation
This lesson introduces learners to the course framework and the Databricks ecosystem that the curriculum uses as a practical platform. You will learn how the course is organized into modules, what resources are available, and how engagement and assessment will be managed. The Databricks overview covers collaborative notebooks, managed Spark, Delta Lake for reliable data storage, and DevOps-friendly features like Repos and Delta Live Tables. Practical setup topics include Community Edition, Azure free trial, and workspace organization to ensure you can follow hands-on labs. The conceptual emphasis is on combining theoretical understanding with immediate practical application so that each module builds competence and confidence incrementally.
Bilingual (EN → বাংলা)
EN: This chapter outlines course goals, structure, resources, and how to engage.
বাংলা: এই অধ্যায়টি কোর্সের লক্ষ্য, কাঠামো, সম্পদ এবং কীভাবে অংশগ্রহণ করতে হবে তা তুলে ধরে।
EN: Databricks provides collaborative notebooks, managed Spark, and Delta Lake.
বাংলা: Databricks ইন্টারঅ্যাকটিভ নোটবুক, পরিচালিত Spark এবং Delta Lake সরবরাহ করে।
Important Notes
- Follow the module sequence to respect prerequisite knowledge.
- Use the discussion forum for questions — peer answers accelerate learning.
- Back up notebooks and use version control (Databricks Repos) for reproducibility.
- Embrace incremental learning: iterate on exercises, read feedback, and revisit concepts.
| Module | Focus | Resources |
|---|---|---|
| Introduction | Course overview & setup | Readings, orientation video, checklist |
| Notebooks & Tools | Jupyter/Databricks notebooks, Git | Interactive notebooks, labs, Repos |
| Delta Lake & DLT | Reliable storage, pipelines | Hands-on labs, sample datasets |
Exercises
True / False
- Databricks Repos integrate Git version control inside the Databricks workspace.
Answer
True. Databricks Repos support Git providers (GitHub, GitLab, Bitbucket) allowing clone, commit, push/pull workflows inside the workspace. - Delta Lake does not support ACID transactions.
Answer
False. Delta Lake provides ACID transactions to ensure data reliability for both batch and streaming workloads.
Fill in the Blanks
- The feature that enables querying historical versions of data in Delta Lake is called ________.
Answer
Time Travel. - Auto Loader uses the format option "cloudFiles" to incrementally ingest files from ________ storage.
Answer
Cloud (e.g., S3, Azure Blob Storage, Google Cloud Storage).
Multiple Choice (MCQ)
- Which output mode writes only new rows appended since the last trigger in Structured Streaming?
- A. Complete
- B. Append
- C. Update
Answer
B. Append — only new rows are written to the sink for each trigger. - Which Databricks feature provides a declarative framework for building ETL pipelines with built-in monitoring?
- A. Repos
- B. Delta Live Tables
- C. Auto Loader
Answer
B. Delta Live Tables — it offers declarative ETL logic, automatic management, and monitoring.
Matching
Match the term with its description.
- K-Means
- CDC (Change Data Capture)
- Z-Ordering
Answer
1 → A simple centroid-based clustering algorithm (K-Means).2 → Technique to capture and process changes from the source system incrementally (CDC).
3 → Storage optimization technique that clusters related data to improve read performance (Z-Ordering).
Descriptive
Explain briefly why schema evolution is important for production Delta tables.
Answer
Schema evolution allows adding or modifying columns without rewriting existing data, enabling flexible ingestion of changing source structures. In production, this reduces downtime and simplifies onboarding new data sources while maintaining data integrity via controlled merge/append mechanisms.Creative
Propose a short plan (3 steps) to implement a simple CDC pipeline that updates a customer dimension table.
Answer
Step 1: Enable CDC on the source database or capture changes via transaction logs/timestamps and write change events to a staging topic or table.Step 2: Build a Delta Live Table or streaming job that consumes the CDC feed, normalizes event types (insert/update/delete), and applies transformations and validation rules.
Step 3: Merge changes into the target Delta customer dimension using DeltaTable.merge() with proper key matching and audit columns, and enable time travel for recovery.
Activities, Vocabulary, Homework, Practice Task
Activities
- Set up Databricks Community Edition account and run a "Hello, world" notebook.
- Clone a sample Git repo into Databricks Repos and commit a small change.
- Load a small CSV with Auto Loader and write it to a Delta table.
Vocabulary
| Term | Definition |
|---|---|
| Delta Lake | Storage layer that brings ACID transactions and time travel to data lakes. |
| Auto Loader | Incremental file ingestion mechanism for cloud storage. |
| DLT (Delta Live Tables) | Declarative framework for building & managing data pipelines. |
Homework
- Create a short notebook that reads a sample dataset and writes a Delta table; include one update and one time-travel query.
Answer
Submit your notebook link and indicate the Delta version number used for the time-travel query. - Write a 200-word reflection on how you plan to use discussion forums to enhance learning.
Answer
Describe specific questions you might ask and how you will summarize answers for your peers.
Practice Task
Implement a simple word-count structured streaming job reading from a socket and outputting to the console. Document each step.
Answer
1. Start a socket server (e.g., nc -lk 9999) and send text lines.2. In Databricks/PySpark: readStream.format("socket").option("host","localhost").option("port",9999).load().
3. Split lines into words, groupBy("word").count(), writeStream.format("console").outputMode("complete"/"update").start().
Instructor note: revisit this introduction before each module to align expectations and verify your environment is working. Good luck and engage actively!