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data engineering - ibm overview
source: ibm data engineering
🔹 what data engineers do
- create and deploy algorithms, pipelines, and workflows that convert raw data → ready-to-use datasets
- enable analysis and application of data regardless of source or format
- follow data mesh principles — a decentralized architecture where data is organized by business domains (marketing, sales, services, etc.)
🔹 key use cases
- data collection, storage, and management
- real-time data analysis
- machine learning integration
🔹 de and core datasets
- data engineers build systems that turn massive raw data into core, usable datasets
- focus on data as a product (daap) — different from data as a service (daas)
- real-world examples:
- retail and e-commerce data standardization
- fraud detection pipelines
- manufacturing analytics pipelines
⚙️ how data engineering works
data engineering designs and builds data pipelines that convert unstructured raw data into unified, high-quality datasets.
steps:
- data ingestion – movement of data from multiple sources into a single ecosystem
- data transformation – cleaning, correcting, normalizing data to ensure consistency and integrity
🧩 data normalization (detailed concept)
definition:
data normalization means organizing and transforming data to make it consistent, structured, and comparable.
it removes redundancy (duplicates) and ensures data integrity (accurate relationships between entities).
🔹 simple analogy
like cleaning and organizing your messy room:
- books on shelves (grouped)
- clothes in the cupboard
- shoes on the rack
→ not deleting anything, just organizing logically for easier retrieval.
🔹 in data context
in databases (like sql):
- normalization = splitting large tables into smaller ones and defining relationships (foreign keys)
- it follows normal forms (1nf, 2nf, 3nf, etc.) — each removing specific redundancy or dependency type.
🔹 real-world example
before normalization:
| student_id | student_name | course | teacher | teacher_phone |
|---|---|---|---|---|
| 1 | alex | math | mr. raj | 9999991111 |
| 2 | mia | science | ms. neha | 9999992222 |
| 3 | sam | math | mr. raj | 9999991111 |
redundancy: teacher info repeats for each student.
after normalization:
students table
| student_id | student_name | course_id |
|---|---|---|
| 1 | alex | 101 |
| 2 | mia | 102 |
| 3 | sam | 101 |
courses table
| course_id | course_name | teacher_id |
|---|---|---|
| 101 | math | t1 |
| 102 | science | t2 |
teachers table
| teacher_id | teacher_name | teacher_phone |
|---|---|---|
| t1 | mr. raj | 9999991111 |
| t2 | ms. neha | 9999992222 |
→ cleaner, consistent, efficient for updates and queries.
🧠 da vs ds vs de
| role | focus | purpose |
|---|---|---|
| da (data analyst) | analyze large datasets | extract insights for present-day decisions |
| ds (data scientist) | build ml models | predict future outcomes |
| de (data engineer) | design pipelines & infra | deliver reliable, structured data for da/ds |
🧰 tools & pipeline formats
data engineers primarily use:
- etl → extract → transform → load (see
/learning/etl) - elt → extract → load → transform (see
/learning/elt)
💾 data storage solutions
- cloud computing services
- relational databases
- nosql databases
- data warehouses
- data lakes
- data lakehouses
🔄 other data integration methods
- change data capture (cdc): tracks and streams only changes (inserts, updates, deletes) from databases in real time.
- data replication: creates exact copies of data across systems to ensure consistency and availability.
- data visualization: converts datasets into graphical insights for easy pattern and trend detection.
- stream data integration (sdi): continuously processes and merges live data streams from multiple real-time sources.
💻 core programming languages
- sql → querying and data modeling
- python → scripting, orchestration, and analytics
- scala → big data and spark pipelines
- go → building concurrent, high-performance data services
- node.js → managing real-time, event-driven data integrations
- java → enterprise-scale data processing