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Curriculum/Data and Databases/Data Roles and the Database Landscape
10 minBeginner

Data Roles and the Database Landscape

After this lesson, you will be able to: Tell apart the data engineer, data scientist, data analyst, and database administrator roles, and map the SQL, NoSQL, and API skills in this track to each one.

Data and Databases is a parent track for the skills backend and data engineers lean on every day. This free intro maps the data-career landscape so you know which role you are aiming at, then points you at the three professional sub-tracks (SQL, MongoDB, and REST API design) that build those skills.

This is a free introductory lesson. No purchase required.

Four roles people confuse constantly

Data analyst: answers business questions from existing data. Lives in SQL, spreadsheets, and a BI tool (Tableau, Power BI, Looker), and communicates findings to non-engineers. Strong SQL is the core skill. Data scientist: builds statistical and machine-learning models to predict or classify. Uses Python (pandas, scikit-learn, PyTorch), statistics, and experiment design. Still needs SQL to get the data. Data engineer: builds and runs the pipelines and storage that everyone else depends on. Moves data from sources into warehouses, models it, and keeps it reliable and fresh. Lives in SQL, Python, orchestration tools, and cloud data services. Database administrator (DBA): keeps the databases themselves healthy: performance tuning, indexing, backups, replication, security, and capacity. Deep on one or more database engines.

💡 The skill that every one of these roles shares

SQL. The analyst queries with it, the scientist pulls training data with it, the engineer models warehouses with it, and the DBA tunes it. If you learn one thing from this track first, make it SQL. Everything else builds on top.

SQL vs NoSQL vs APIs: what this track covers

Relational (SQL) databases (Postgres, MySQL) store structured, related data with strong guarantees and are the default for most applications. The SQL sub-track takes you to professional level: joins, indexing, transactions, window functions, and query tuning. Document (NoSQL) databases (MongoDB) store flexible, schema-light documents and fit certain access patterns better. The MongoDB sub-track covers data modeling, aggregation, and indexing, plus the honest tradeoffs vs relational. REST APIs are how services and data move between systems. The REST API sub-track covers designing, versioning, securing, and documenting APIs, which is core to both backend and data-engineering work (it is why this sub-track is shared with Web Development too).

How to use this track

Start with SQL regardless of your target role. If you are heading toward application or data engineering, add REST API design next. If your work involves flexible documents or you want to understand the relational tradeoffs first-hand, take MongoDB. These three sub-tracks are shared with the Programming Languages track (same lessons, same progress), and completing all three earns the Data and Databases certification.

Tools & Resources

Postgres official tutorial
MongoDB University (free courses)
What is a data engineer? (dbt explainer)
Back to Data and Databases