HIGAET Data Engineering
Learn Python, SQL, and pipeline tools to build warehouses, orchestrate workflows, and deliver reliable datasets through HIGAET Practical Training projects.
Duration
12 weeks · 5-7 hours/week
Level
Intermediate
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
Data engineering is the discipline of building the pipelines, warehouses, and workflows that deliver clean, reliable datasets to analysts and models. It matters now because analytics and AI are only as good as the data supply behind them.
Engineers use Python, SQL, and pipeline tools to build batch ingestion into warehouses, design dimensional models for reporting, and orchestrate scheduled workflows with retries and dependencies. It does not solve upstream problems by itself: pipelines do not fix unclear definitions, poor source quality, or schemas designed without the questions they must serve.
By the end you will be able to build batch ingestion pipelines into a warehouse, dimensional models and schemas for analytics workloads, and orchestrated workflows with validation tests, freshness checks, and anomaly detection.
Why this course exists
The gap is between a script that loads a file once and a production dataset that stays fresh, tested, and documented every day. This course teaches the arc from Sources to Pipelines to Models to Decisions: ingesting structured and semi-structured data, modeling it for analytics, orchestrating it reliably, and guarding it with quality checks.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and SQL basics
- Familiarity with databases and file formats
- Basic command-line comfort
Technologies & tools
Skills you'll gain
A 12 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Data Engineering Lifecycle, Warehouses, Lakes, and Lakehouse Concepts
- Module 02
Module 02 — Core: Advanced SQL and Data Modeling for Analytics Workloads
- Module 03
Module 03 — Core: Python for Ingestion, Transformation, and File Format Handling
- Module 04
Module 04 — Engineering: Batch Pipelines, Incremental Loads, and Idempotent Design
- Module 05
Module 05 — Engineering: Workflow Orchestration, Scheduling, and Failure Recovery
- Module 06
Module 06 — Engineering: Warehousing, Transformation Layers, and Data Contracts
- Module 07
Module 07 — Advanced: Streaming Ingestion and Near-Real-Time Processing Patterns
- Module 08
Module 08 — Production: Data Quality, Observability, Security, and Access Control
- Module 09
Module 09 — Capstone: Production-Style Data Pipeline with Warehouse and Monitoring
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build batch ingestion pipelines that load structured and semi-structured data into warehouses
- Design dimensional models and schemas that support analytics and reporting workloads
- Develop orchestrated workflows with retries, scheduling, and dependency management
- Evaluate data quality with validation tests, freshness checks, and anomaly detection
- Automate pipeline testing and deployment with version control and CI practices
- Optimize query and pipeline performance through partitioning, indexing, and incremental loads
- Integrate streaming sources with batch systems for unified data delivery
- Architect a production-style data platform project with documentation and monitoring
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Batch ingestion pipeline
- Project 02
Dimensional warehouse model
- Project 03
Orchestrated workflow with retries
- Project 04
Data quality validation suite
- Capstone
Analytics-ready warehouse with orchestrated pipelines
Speak the language first.
- Batch Ingestion Pipelines
- Scheduled jobs that extract data from sources and load it into a warehouse for analysis.
- Data Warehousing
- Central storage organized for fast analytical queries over cleaned, modeled tables.
- Dimensional Modeling
- Designing fact and dimension tables so reports and dashboards query efficiently.
- Schema Design
- Defining table structures, types, and constraints that keep stored data consistent.
- Workflow Orchestration
- Scheduling pipeline tasks with dependencies and retries so they run in the right order.
- Dependency Management
- Declaring task order and upstream requirements so a job waits for the data it needs.
- Data Validation Tests
- Automated checks for nulls, ranges, and row counts that catch bad data early.
- Freshness and Anomaly Checks
- Monitors that alert when data arrives late or volumes and values look unusual.
- Semi-Structured Data Loading
- Techniques for ingesting JSON and similar flexible formats into structured warehouse tables.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Pipeline fails when upstream data arrives late or out of order
Add dependency sensors, scheduling windows, and retries with backoff, then alert on missed SLAs.
Duplicate rows appear after pipeline reruns
Make loads idempotent with merge keys or delete-then-insert windows, and add row-count validation tests.
Schema change in source breaks warehouse loads
Add schema checks at ingestion, quarantine unexpected columns, and version the target schema before backfilling.
Stale dashboards caused by silent pipeline failure
Add freshness checks and row-count monitors with alerts, then backfill and verify the affected partitions.
Data quality issues reach reporting tables
Add validation tests for nulls, ranges, and referential integrity at staging, and block promotion on test failure.
Before you move on, you should be able to
- Build batch ingestion pipelines into warehouse tables
- Design dimensional models and schemas for analytics workloads
- Develop orchestrated workflows with scheduling, retries, and dependencies
- Evaluate data quality with validation tests and freshness checks
- Detect anomalies in pipeline outputs before they reach reports
- Explain pipeline lineage and failure handling to stakeholders
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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Ready to start HIGAET Data Engineering?
A 12 weeks course — Data & Machine Learning.