Data Engineering with Python: Building Scalable Data Pipelines

Program Description

This two-day technical program is designed for technical executives (CTOs, IT Directors, and Data Architects) to master the architectural backbone of the modern AI enterprise: The Data Pipeline. Beyond simple data movement, this course focuses on building robust, scalable, and automated data infrastructures using Python as the primary orchestrator.
Designed for the Malaysian corporate landscape, participants will learn to manage the transition from "Data Silos" to "Data Lakes," implementing Traditional ETL/ELT alongside Generative AI for automated schema mapping and metadata management.
The program emphasizes technical resilience, high-concurrency processing, and strict PDPA-compliant data engineering for high-stakes industries.

While this outline serves as a foundational framework with use cases from multiple industries and functions, the final program is fully customized to your industry and internal workflows.

Participants work on real-world problems, not generic examples. We engage in a pre-workshop alignment to inject your specific organizational datasets, pain points, and proprietary use cases directly into the curriculum.

Learning Objectives

Architect Scalable ETL/ELT Pipelines: Design Python-led frameworks for high-volume data ingestion and transformation.
Master Stream & Batch Processing: Technically differentiate between real-time data streaming and batch workloads using Pandas, PySpark, and Dask.
Implement AI-Augmented Data Engineering: Leverage Generative AI for automated code generation, SQL optimization, and pipeline troubleshooting.

Program Details

Content

Day 1: Architectural Foundations & High-Volume Ingestion

Module 1: The Modern Data Engineering Stack

Moving from “Legacy Middleware” to “Code-Native Pipelines.” Understanding the CAP theorem, ACID properties, and the architectural shift to Data Lakehouses.
Scenario (General): Transitioning a banking system from nightly batch updates to a real-time event-driven architecture to support instant fraud detection triggers.
Hands-on: “The Pipeline Blueprint” – Architecting a multi-source ingestion layer that pulls from SQL, NoSQL, and REST APIs using Python’s multiprocessing library.
Expected Impact: Technical clarity on selecting the right architecture for high-concurrency workloads.

Module 2: Advanced Data Transformation with Pandas & Dask

Scaling Python beyond memory limits. Using Pandas for structured logic and Dask or Polars for parallelized processing of multi-gigabyte datasets.
Demo (Retail/E-commerce): Processing 10+ million SKU records across regional Malaysian outlets to perform real-time inventory reconciliation and price parity checks.
Hands-on: “The Scaling Sprint” – Writing a parallelized data cleaning script that handles imbalanced data and missing values in a manufacturing sensor dataset.
Expected Impact: Capability to lead teams in building low-latency transformation layers without expensive hardware overhead.

Module 3: Schema Management & AI-Augmented Mapping

Managing “Schema Evolution.” Using Generative AI to automate the mapping of disparate data sources and generate DDL (Data Definition Language) scripts.
Scenario (Manufacturing): Using an AI-agent within an n8n or Python workflow to automatically map inconsistent CSV headers from various factory floor machines into a unified SQL schema.
Hands-on: Building an “Auto-Mapper” – Using an LLM API to generate Python transformation code for “dirty” legacy data structures.
Expected Impact: 60% reduction in manual data mapping time; increased agility in onboarding new data sources.

Module 4: Technical PDPA & Pipeline Security

Engineering “Security-by-Design.” Implementing end-to-end encryption (TLS), secret management (HashiCorp Vault), and PII masking within the code pipeline.
Scenario (HR/Finance): Building a “Privacy Filter” that automatically hashes NRIC and bank details at the ingestion point before data enters the analytical sandbox.
Hands-on: Implementing an automated “Audit Logger” in Python to track every data touchpoint for PDPA 2.0 compliance.
Expected Impact: Structural protection against data leaks and 100% compliance with Malaysian data residency laws.

Day 2: Orchestration, Monitoring & Deployment

Module 5: Workflow Orchestration with Apache Airflow

Managing task dependencies and “DAGs” (Directed Acyclic Graphs). Understanding retries, backfilling, and error handling in mission-critical pipelines.
Scenario (Logistics): Orchestrating a 10-step pipeline that fetches port congestion data, updates internal delivery ETAs, and triggers customer notifications via WhatsApp/SMS.
Hands-on: “The Master Orchestrator” – Designing a DAG that automates a daily financial reconciliation loop, including failure alerts and data quality checks.
Expected Impact: Transition from “Fragile Scripts” to “Resilient Systems” with 99.9% uptime.

Module 6: Data Quality & Observability

Implementing “Unit Tests for Data.” Using Great Expectations or custom Python validators to identify data drift and schema breaks before they reach the dashboard.
Demo (Supply Chain): Setting up an automated “Quality Gate” that halts a pipeline if a shipment’s arrival date is logically impossible (e.g., date is in the past).
Hands-on: Coding a “Data Drift Monitor” using Traditional ML statistical checks to alert the engineering team when input data distribution shifts significantly.
Expected Impact: Higher executive confidence in data accuracy; reduced “Data Debt” across the organization.

Module 7: CI/CD for Data Engineering & MLOps Integration

Version control for data (DVC) and code (Git). Automating deployments using Docker and Kubernetes to ensure environment parity.
Scenario (Banking): Implementing a “Blue-Green” deployment for a loan-scoring pipeline to ensure zero downtime during model or schema updates.
Hands-on: Containerizing a Python pipeline using Docker and pushing it to a secure registry for cloud-based deployment.
Expected Impact: Professional-grade software engineering standards applied to data infrastructure.

Module 8: The 90-Day Data Engineering Roadmap

Consolidating the course into a practical technical roadmap. Moving from “MVP” to “Enterprise Scale.”
The Framework: Prioritizing the “Technical Backlog” based on Data Velocity, Processing Cost, and Business Criticality.
Hands-on: Co-creating a “Data Engineering Playbook” for your team, covering documentation, naming conventions, and AI-governance checkpoints.
Expected Impact: A clear, sustainable path toward an AI-ready, highly-automated data organization.

List of Deliverables

Prerequisites

Who Should Attend

Training Methodology

Code-First Architecture: 70% of the program is hands-on coding, debugging, and whiteboarding.
Deep-Dive Technical Case Studies: Analyzing real-world pipeline failures and successes in the Malaysian market.
Technical Co-Design: Group sessions to solve actual departmental data bottlenecks using advanced engineering patterns.

100% HRDC-Claimable

This program is fully registered and compliant with HRDC (Human Resource Development Corporation) requirements under the SBL-Khas scheme, allowing Malaysian employers to offset the training costs against their levy.

Certification of Completion

Participants who successfully complete the program will be awarded a “Professional Certificate in Data Engineering & Pipeline Architecture.“

Post-Workshop Consulting (Optional)

For organizations looking to bridge the gap between training and execution, we offer optional, paid consulting services. These engagements provide expertise and technical support for specific pilot development or full-scale operational integration of the data- and AI-driven use cases established during the program.