jnaapti

AI curriculum

Course outline for Data Engineering Foundations

Reliable analysis depends on repeatable data movement and transformation. Build a small pipeline with quality checks, scheduling rules, and traceable lineage.

Technology
Data Engineering and Databases
Duration
4 hours
Modules
2 · 2 hours each
Level
Prerequisites listed

About Data Engineering Foundations

Reliable analysis depends on repeatable data movement and transformation. Build a small pipeline with quality checks, scheduling rules, and traceable lineage.

Data Engineering Foundations Course Objectives

  • Compare ETL and ELT pipeline designs.
  • Implement a repeatable transformation with quality checks.
  • Record dependencies and data lineage.

Pre-requisites

  • Basic scripting and SQL skills.
  • Familiarity with tabular data and files.

Lab Setup

  • Computer with a scripting runtime and a local SQL database.
  • Use synthetic CSV files and simulated streaming records; hosted orchestration is not required.

Detailed Course Outline

Proposed modules

Module 1: Pipeline structure

  • Data pipelines
  • ETL
  • ELT
  • Batch processing

Practical outcome: Build a repeatable batch transformation.

Module 2: Operational data flow

  • Streaming
  • Data quality
  • Orchestration
  • Data lineage

Practical outcome: Specify quality checks and source-to-output lineage.

Practical exercise

  • Build a small pipeline, inject a malformed record, and demonstrate quality reporting and rerun behavior.

How we train

Contact us for full course details, including duration, delivery options and lab requirements.

Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.

Discuss your learning goals