Course (Part 373)

Course: Databricks Certified Data Engineer Professional -Preparation

Lesson

Topic: Course Introduction and Overview

Welcome to the Introduction chapter, which opens the pathway to a structured and practical learning journey designed for learners at different levels. This course aims to build a solid foundation in the subject by combining core principles with concrete examples and interactive practice. We will start by clarifying the course purpose and expected learning outcomes so that you can align your goals with the curriculum. Each module balances theory and hands-on activities, allowing you to move from understanding concepts to applying them in realistic scenarios. You will encounter case studies, scenario walkthroughs, and live exercises that illustrate how the material works in practice. Regular quizzes, discussion prompts, and reflective journals are included to reinforce learning and promote active engagement. The course materials—textbooks, lecture slides, and supplemental resources—are curated to support both novice and experienced learners. We emphasize data quality, architecture, and practical patterns like Bronze ingestion, CDC, and partitioning, while also covering operational topics such as monitoring, jobs, and security. Throughout the course you are encouraged to ask questions and collaborate with peers, because dialogue accelerates comprehension and uncovers new perspectives. Time management and proactive participation will greatly increase your success and enjoyment of the material. Feedback loops, both automated and instructor-led, help you iterate and improve your skills continuously. By the end of the course you should be able to apply learned techniques to real projects and make data-driven decisions with confidence.

Lesson

Topic: Introduction — Course Overview, Materials, and Key Concepts

Learning Outcomes

• Understand the course purpose, structure, and core objectives.
• Describe Bronze ingestion, CDC, Delta Lake CDF, and SCD patterns.
• Apply hands-on techniques for ingestion, deduplication, joins, and Type 2 SCDs.
• Implement best practices for partitioning, transaction logs, and materialized views.
• Use Databricks tooling, jobs, CLI, and automation to manage pipelines and clusters.
• Demonstrate monitoring, logging, security governance, and troubleshooting skills.

Conceptual Explanation

This module introduces foundational concepts required for designing and operating modern data systems. We start with Bronze ingestion—capturing raw, unprocessed data into a landing zone—then progress through transformations, deduplication, and the creation of materialized "Gold" tables for analytics. Change Data Capture (CDC) and Delta Lake Change Data Feed (CDF) enable incremental, low-latency propagation of changes. Slowly Changing Dimensions (SCD) patterns (Types 1–3) show methods to preserve or overwrite historical dimension data, with Type 2 focusing on full history retention via new rows. Partitioning and the Delta transaction log improve query performance, ACID guarantees, and enable time travel. Stream-stream and stream-static joins let you enrich or correlate real-time events, while job orchestration and cluster management support reliable production workloads. Security, governance, monitoring, and logging are cross-cutting responsibilities that protect data, enforce policies, and provide operational observability.

Bilingual (EN → বাংলা)

EN: This course introduces data ingestion, change capture, and best practices for building reliable pipelines.

বাংলা: এই কোর্সটি ডাটা ইনজেশন, চেঞ্জ ক্যাপচার এবং নির্ভরযোগ্য পাইপলাইন তৈরির সেরা অনুশীলনগুলো পরিচয় করিয়ে দেয়।

EN: Type 2 SCD preserves history by adding new rows for changed records.

বাংলা: টাইপ ২ SCD পরিবর্তিত রেকর্ডগুলোর জন্য নতুন সারি যোগ করে ইতিহাস সংরক্ষণ করে।

Important Notes

• Engage actively: participate in discussions and hands-on labs to solidify learning.
• Monitor resource usage when running clusters and streaming workloads.
• Design Bronze layers to preserve raw data for auditability and replayability.
• Prefer idempotent operations and unique identifiers to simplify deduplication.
• Document schema changes and SCD rules to support governance and audits.

Course Structure (Quick Reference)

Module	Focus	Format
Introduction	Overview, objectives, materials	Lecture, readings
Bronze & Ingestion	Raw data capture, batch/stream	Hands-on labs
CDC & CDF	Incremental change propagation	Practical demos
SCD & Modeling	Dimension history management	SQL exercises
Production Ops	Jobs, clusters, monitoring, security	Case studies, labs

Exercises

Types: True / False / Fill in the Blanks / MCQ / Matching / Descriptive / Creative

True / False

1. Bronze ingestion stores fully processed, analytics-ready datasets.
   Answer

   False — Bronze is raw/unprocessed data.
2. Type 2 SCD retains historical rows when attributes change.
   Answer

   True — Type 2 inserts new rows and preserves history.
3. CDC captures inserts, updates, and deletes incrementally.
   Answer

   True — CDC captures change events.

Fill in the Blanks

1. Bronze layer primarily stores __________ data.
   Answer

   raw / unprocessed
2. Delta Lake stores transaction metadata in the __________ directory.
   Answer

   _delta_log
3. Type 2 SCD commonly uses __________ keys to identify records.
   Answer

   surrogate

Multiple Choice Questions

1. Which method best suits low-latency event propagation?
   1. Batch ingestion
   2. Stream ingestion
   3. Manual export
   4. Ad-hoc download
   Answer

   b) Stream ingestion
2. Which partitioning key is usually ideal for time-based queries?
   1. Transaction date
   2. Random id
   3. Short string token
   4. Binary blob
   Answer

   a) Transaction date

Matching

Match the concept (Left) with its description (Right).

Concept	Description
A. CDC	1. Preserves raw landing data
B. Bronze Layer	2. Incremental change capture
C. Materialized View	3. Stores pre-computed query results

Answer

A→2, B→1, C→3

Descriptive

1. Explain why retaining raw data in the Bronze layer is valuable for auditability and reprocessing.
   Answer

   Retaining raw data preserves original signals and context, enabling reprocessing with updated transformations, meeting audit/compliance requirements, and facilitating debugging when downstream issues arise.
2. Describe the main trade-offs between Type 1 and Type 2 SCD implementations.
   Answer

   Type 1 is simpler and uses overwrite—no history retained, less storage, simpler queries. Type 2 retains history via new rows—enables historical analysis but increases storage and ETL complexity and may require careful key/version handling.

Creative

1. Design a short data pipeline (3–5 steps) that ingests IoT sensor data into a Bronze layer and makes it available for near-real-time analytics.
   Answer

   Example pipeline: (1) Stream ingestion via Kafka collects sensor events into Bronze storage; (2) Lightweight validation & schema registry apply; (3) Streaming deduplication and enrichment join with static device metadata; (4) Write clean events to Silver (curated) Delta tables and enable CDF for downstream consumers; (5) Materialize aggregated metrics into Gold tables for dashboards.

Activities

1. Run a small Kafka (or local) producer and consumer to demonstrate streaming ingestion of 100 events and observe deduplication logic.
   Answer

   Execute the provided producer script to send events with repeating ids; run the consumer that tracks seen ids (in-memory or Redis) and verify only unique events are processed.
2. Implement a Type 2 SCD update for a sample customer table and query historical versions.
   Answer

   Follow steps: set EndDate/IsCurrent on old record, insert new row with StartDate and IsCurrent=1, then query by CustomerID to review versions.

Vocabulary

• Bronze layer — raw ingested data.
• CDC — Change Data Capture; incremental changes.
• CDF — Change Data Feed in Delta Lake.
• SCD — Slowly Changing Dimension.
• Materialized View — pre-computed query result stored for performance.

Homework

1. Write a short 300–500 word reflection describing a use case in your organization where Delta Lake CDF or CDC would reduce processing cost and latency.
   Answer

   Submit a reflection describing the current batch process, identify how incremental change capture reduces data scanned and speeds downstream updates, and propose a migration plan with testing and rollback steps.
2. Prepare a three-slide deck: (1) Problem & data flow, (2) Proposed pipeline (Bronze→Silver→Gold, CDC/CDF usage), (3) Risks & mitigations.
   Answer

   Slides should outline sources, transformations, retention policy, access controls, monitoring, and a short timeline for implementation with responsible owners.

Practice Task

Implement a small Databricks Job that reads a Delta Bronze table, applies a simple transformation, writes to a partitioned Silver table, and schedules it to run nightly. Document commands and parameters used.

Answer

Sample steps: (1) Create a cluster with autoscaling; (2) Notebook reads Delta Bronze, filters invalid rows, writes df.write.format("delta").partitionBy("date").mode("append").save("/mnt/delta/silver"); (3) Create a Databricks Job pointing to the notebook; (4) Schedule nightly and configure alerts. Provide JSON for job creation via CLI if required.

Conclusion

Use this sheet as your quick reference. Revisit the exercises and hands-on tasks frequently, and leverage the bilingual notes when discussing concepts with diverse teams. If you need further clarification, open a discussion in the course forum or request targeted office hours.