IBM Data Engineering: Introduction

Written By Kamil Pospiech

Data engineering is the invisible force behind today’s data-driven decision-making. While data scientists often get the spotlight, it is data engineers who lay the groundwork by designing, building, and maintaining the infrastructure and pipelines that move, process, and secure the data. IBM Introduction to Data Engineering course is the first one in Coursera’s Professional Certificate series that I decided to complete this year

 

Module 1: Foundations of Data Engineering

What is Data Engineering?

Data engineering refers to the discipline focused on collecting, transforming, storing, and making data available for analysis. Engineers are responsible for:

  • Building reliable data pipelines

  • Designing storage architectures

  • Ensuring security and data quality

  • Supporting downstream users like analysts and data scientists

Ecosystem & Roles

The modern data ecosystem includes data engineers, analysts, scientists, business analysts, and infrastructure. Roles can overlap, but the key difference is in how close to raw data a professional operates.

Key Concepts:

  • Structured / Semi-Structured / Unstructured Data

  • OLTP (Transactional) vs OLAP (Analytical) systems

  • Data repositories: RDBMS, data warehouses, lakes

  • ETL pipelines: Extract, Transform, Load workflows

Module 2: Data Repositories, Tools, and Platforms

Databases and Data Repositories

  • RDBMS: Table-based, SQL-driven, good for structured transactional data

  • NoSQL: Schema-flexible, built for scalability; includes key-value, document, column-family, graph models

  • Data Warehouse: Centralized, cleaned, structured store optimized for querying

  • Data Lake: Raw data in any format; schema-on-read

  • Lakehouse: Combines structure of warehouses with flexibility of lakes

Data Stores Design Criteria

  • Volume, Variety, Velocity

  • Query complexity

  • Backup & compliance needs

  • OLTP (fast writes) vs OLAP (fast reads)

ETL vs ELT

  • ETL: Transform before loading, common with structured sources

  • ELT: Load then transform; suitable for unstructured or cloud-based architectures

Integration Tools

  • Python scripts, Apache Airflow, dbt, Informatica, AWS Glue, etc.

  • Pipelines link ingestion, transformation, validation, and storage

Module 3: Data Engineering in Practice

Layered Platform Design

  • Ingestion Layer: API pulls, sensors, DB dumps

  • Storage Layer: Databases, lakes, warehouses

  • Processing Layer: Data cleaning, transformation

  • Access Layer: Dashboards, APIs, queries

Security: CIA Triad

  • Confidentiality: Access control, encryption

  • Integrity: Validation, audit trails

  • Availability: Backups, redundancy, monitoring

Security must be applied across network, application, and storage layers. Monitoring and alerting are core to real-time detection.

Data Collection and Wrangling

  • Common sources: APIs, RSS, scraping, exchanges, sensors

  • Wrangling tasks: cleaning, normalizing, shaping, transforming

  • Tools: Pandas, OpenRefine, spreadsheets, Trifacta, Google DataPrep

Querying and Performance

  • SQL used for slicing, filtering, aggregating, grouping

  • Performance metrics: latency, throughput, failures

  • Techniques: indexing, partitioning, normalization, logging

Governance and Compliance

Data governance involves processes, people, and policies for:

  • Data ownership and stewardship

  • Policy enforcement (e.g., GDPR, HIPAA, SOX)

  • Access logging and metadata management

Compliance is Continuous

  • Applies across full data lifecycle: acquisition to disposal

  • Requires auditability, transparency, and secure erasure protocols

Bonus: DataOps and Real-World Maturity

DataOps brings software engineering practices into data pipelines:

  • Version control (Git)

  • CI/CD for pipelines

  • Metadata automation

  • Sprint-based iteration

It emphasizes trust, testing, reproducibility, and agility across teams.

Final Thoughts

The IBM course reinforces that data engineering is not about tools or code in isolation - it’s about system thinking, responsibility, and enabling reliable data access at scale. From ingestion to governance, each layer has trade-offs that need to be designed carefully.

Next in line is Python course, which I expect to be a great refresher of all the random materials I’ve gone through in the past, with a concrete focus on the specific application. In general, it feels like Data Engineering is not just a tech job, it’s an infrastructure discipline that shapes how organizations act with data.

Course Certificate: View on Coursera

All notes and opinions are personal interpretations of the IBM Introduction to Data Engineering course on Coursera.

Kamil Pospiech
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