MugUp logoMugUp
HomeBrowseAboutBlog
LoginSign Up
MugUp

Plan your day, study, read, write and track your habits in one app.

Follow MugUp

Product

  • Features
  • Compare
  • Free Tools
  • Deck Library
  • MugUp for Discord

Community

  • Blog
  • Changelog
  • Feature Requests
  • Deck Requests

Company

  • Home
  • About
  • Contact

© 2026 MugUp. All rights reserved.

Terms of ServicePrivacy PolicyCookie Policy
  1. Browse decks
  2. Data Engineering Fundamentals
Public deck

Data Engineering Fundamentals

How data engineers build and run the pipelines that move and shape data for analytics. Covers dimensional modelling, warehouses and lakes, batch and streaming pipelines, Spark, Kafka, Airflow, dbt and analytical SQL.

Engineering
AShared by Alex Chen
0PlaySign in to clone

You’re reading a free preview. Sign up to see every topic and question — and to play this deck as games.

See the whole deck

Suggested order

Arrows point from what to learn first. Topics side by side can be studied in any order — and cloning the deck brings this order with it.

Topics

Open a topic to read through its content, then play just that topic.

Data Modelling for Analytics: Facts, Dimensions and Star Schemas
How analytical data is organised into fact and dimension tables, and how dimensions keep or discard history when source data changes.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
40 total questions
Warehouses, Lakes and Lakehouses
Where analytical data is stored: how warehouses, data lakes and lakehouses differ, and why columnar storage and separate compute matter.
MCQ: 10
Fill-in: 8
Flashcards: 11
Pair match: 10
39 total questions
Batch Pipelines: ETL, ELT and Idempotent Loads
How batch pipelines extract, load and transform data, load only what changed, and stay correct when a run is repeated.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
Locked · 40 questions inside
Distributed Processing with Spark
How Apache Spark splits work across a cluster, when it moves data between machines, and how to keep jobs fast and stable.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
Locked · 40 questions inside
Streaming Data: Kafka and Stream Processing
How Apache Kafka stores and delivers streams of events, and how stream processors group events by time even when they arrive late.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
Locked · 40 questions inside
File Formats, Partitioning and Compression
How data is laid out in files such as CSV, Avro, Parquet and ORC, and how partitioning and compression decide how much a query has to read.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
Locked · 40 questions inside
Orchestration with Airflow
How Apache Airflow defines pipelines as DAGs of tasks, schedules them over time intervals, and retries and reruns them safely.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
Locked · 40 questions inside
Data Quality, Testing and Governance
How teams test data with dbt and other checks, catch breaking changes and stale data, and handle personal data and ownership responsibly.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
Locked · 40 questions inside
Analytical SQL: Window Functions and CTEs
How window functions and common table expressions answer analytical questions in SQL, such as rankings, running totals, period comparisons and deduplication.
MCQ: 10
Fill-in: 8
Flashcards: 12
Pair match: 10
Locked · 40 questions inside
7 more topics in this deck

Create a free account to open every topic — and play the whole deck without copying it.

Sign up freeLog in