Track 02

Data Engineering

18 courses · each graded Basic 50 · Intermediate 50 · Advanced 30 (scenario) · Practical 20–25.

Core processing

Ready

SQL-DE

SQL

Shared with Analytics — through to tuning.

Soon

PYSP

Python / PySpark

DataFrames, UDFs, tuning — hands-on.

Soon

SPARK

Apache Spark

Internals, shuffle, skew, AQE.

Soon

DBX

Databricks

Platform, Delta Lake, jobs, Unity Catalog.

Soon

KAFKA

Apache Kafka

Topics, partitions, offsets, delivery.

Soon

Airflow

DAGs, sensors, orchestration patterns.

Warehouse, modelling & transform

Soon

Data Warehousing

OLAP, schemas, SCD, grain, partitioning.

Soon

SNOW

Snowflake

Micro-partitions, warehouses, streams, cost.

Soon

Data Modelling

Dimensional, Kimball/Inmon, keys, grain.

Soon

ETL

ETL / ELT Design

Pipelines, idempotency, incremental loads.

Soon

DBT

dbt

Models, tests, sources, macros, deployment.

Soon

Data Quality & Production

Incidents, monitoring, on-call scenarios.

Databases

Soon

MONGO

NoSQL / MongoDB

Documents, aggregation, indexing, sharding.

Cloud, tooling & design

Soon

AWS-DE

AWS Data Engineering

Glue, S3, Redshift, EMR, Kinesis, Lambda.

Soon

AZ-DE

Azure Data Engineering

ADF, Synapse, ADLS, Databricks on Azure.

Soon

GIT

Git & GitHub

Branching, PRs, CI/CD, conflict handling.

Soon

LINUX

Linux & Shell

Commands, scripting, cron, text tooling.

Soon

SYSD

System Design for Data

Design a pipeline / platform end to end.

Professor of Data · Data Engineering track