"One page, the whole vocabulary. Once these feel familiar, data engineering job posts and docs read like plain English."
Level: Reference · Time: ~browse · Prerequisites: none
Learning Objectives
By the end of this module, you will be able to:
- Recognize the key terms used across this track
- Explain each in simple language
- Use this page as a quick reference
Use this the way you'd use a phrasebook in a new country: skim it once now so the words feel familiar, then come back whenever a term shows up in a job post, a colleague's Slack message, or a tool's documentation. You don't need to memorize it — you need to recognize the terms and know roughly where each one sits in the bigger picture. The grouping below mirrors the order of the track, from raw data basics up through MLOps.
A quick note on how these terms relate: the vocabulary is layered, not flat. Data-basics words (schema, batch, streaming) describe the raw material; storage words (warehouse, lake, index) describe where it rests; processing words (ETL, Spark, Kafka) describe how it moves; orchestration and ops words (DAG, Docker, cloud) describe how it's coordinated and run; and MLOps words (deployment, drift, registry) describe operating models on top of all that. If a term ever feels disconnected, ask which of these layers it belongs to — that placement usually makes its meaning click.
Data Basics
| Term | Plain meaning |
|---|---|
| Structured data | Rows and columns (tables) |
| Semi-structured data | Flexible formats like JSON or XML with some shape |
| Unstructured data | Images, text, audio — no fixed shape |
| Schema | The defined structure/types of data |
| Schema drift | When data's shape changes unexpectedly and breaks pipelines |
| Batch | Processing data in scheduled chunks |
| Streaming | Processing each event as it arrives |
| Pipeline | Automated flow that moves/transforms data |
Storage & Databases
| Term | Plain meaning |
|---|---|
| Relational database | Data in linked tables, queried with SQL |
| SQL | The language for querying databases |
| NoSQL | Flexible non-tabular databases |
| Join | Combining rows from tables on a shared key |
| Index | Structure that speeds up queries |
| Data warehouse | System built for large-scale analytics (OLAP) |
| OLTP vs OLAP | Running the app vs analyzing the data |
| Data lake | Cheap storage for raw data of any format |
| Lakehouse | Lake + warehouse features combined |
| Data swamp | A disorganized, untrustworthy lake |
| Columnar storage | Storing by column for fast analytics |
| Object storage | Cheap, vast cloud file storage |
Processing & Movement
| Term | Plain meaning |
|---|---|
| ETL / ELT | Extract-Transform-Load / Load-then-Transform |
| Idempotent | Safe to run repeatedly, same result |
| Incremental load | Processing only new data, not everything |
| Spark | Engine for distributed big-data processing |
| Distributed processing | Splitting work across many machines |
| Lazy evaluation | Building a plan, running only on an action |
| Kafka | Durable backbone for streaming events |
| Producer / topic / consumer | Sends / stream of / reads events |
Orchestration & Ops
| Term | Plain meaning |
|---|---|
| Orchestration | Coordinating tasks in order, on schedule |
| DAG | Directed Acyclic Graph of tasks (no loops) |
| Backfill | Re-running a workflow over past dates to fix history |
| Airflow | Popular Python orchestration tool |
| Container | Code packaged with its whole environment |
| Image / container | Blueprint / running instance |
| Docker | The common container tool |
| Kubernetes | Orchestrates many containers at scale |
| Cloud (AWS/Azure/GCP) | Rented, on-demand infrastructure |
| Elasticity | Scaling resources up/down with demand |
MLOps
| Term | Plain meaning |
|---|---|
| MLOps | Deploying and maintaining models in production |
| Deployment / serving | Making a model usable via batch or API |
| Training/serving skew | Data prepped differently in training vs serving |
| CI/CD/CT | Automated test / deploy / continuous training |
| Experiment tracking | Recording each training run's data & results |
| Model registry | Versioned catalog of models (staging/production) |
| Drift | Data/accuracy diverging from training over time |
| Data governance | Rules for ownership, access, privacy, retention |
| Lineage | Where data came from and how it changed |
| Observability | Being able to see a system's health from its signals |
Try this: Pick any five terms above that felt fuzzy the first time you met them. Cover the right-hand column and explain each in your own words. The ones you can't explain plainly are exactly the concepts worth revisiting in their module — recognition is good, but being able to teach a term back is how you know it's yours.
Key Takeaway: These terms span the whole track — data basics, storage and databases, processing and movement, orchestration and operations, and MLOps. Bookmark this page as your quick reference while you build pipelines and ship models.
Further Learning
Part of "MLOps & Data Engineering." Original content for this learning platform.