Referred Link - https://www.linkedin.com/posts/riyakhandelwal_data-engineering-isnt-complicated-its-activity-7438056244818464769-IDJ7
Data Engineering isn’t complicated.
If
you're building data platforms, pipelines, or analytics systems, here
are 12 core data engineering concepts worth understanding 👇
1. Data Ingestion
↳ The process of collecting data from multiple sources like APIs, databases, logs, and applications.
↳ Used in: ETL pipelines, streaming platforms, analytics systems
2. ETL / ELT
↳ Moving and transforming raw data into usable datasets.
ETL: Transform before loading
ELT: Load first, transform later
↳ Used in: Data warehouses, lakehouse platforms
3. Data Lakes
↳ Central storage designed to hold massive volumes of raw structured and unstructured data.
↳ Used in: Large-scale analytics, machine learning workloads
4. Data Warehouses
↳ Systems optimized for analytical queries and reporting.
↳ Used in: BI dashboards, business reporting, analytics teams
5. Batch Processing
↳ Processing large datasets at scheduled intervals.
↳ Used in: Daily reports, periodic data transformations
6. Stream Processing
↳ Handling data in real-time as it arrives.
↳ Used in: Fraud detection, monitoring systems, real-time analytics
7. Data Modeling
↳ Structuring data into schemas like star schema or snowflake schema to make analysis faster and more reliable.
↳ Used in: Warehouses, semantic layers, BI systems
8. Orchestration
↳ Managing pipeline dependencies, scheduling workflows, and ensuring jobs run in the right order.
↳ Used in: Complex data pipelines
9. Distributed Processing
↳ Splitting large workloads across multiple machines to process massive datasets efficiently.
↳ Used in: Big data platforms and scalable pipelines
10 Data Quality
↳ Ensuring data is accurate, consistent, and trustworthy before it reaches analysts or models
↳ Impact: Reliable dashboards and business decisions
11. Data Governance
↳ Managing data access, security, lineage, and compliance.
↳ Impact: Trust, security, and regulatory alignment
12. Observability
↳ Monitoring pipelines with logs, metrics, and alerts so issues can be detected quickly.
↳ Impact: Faster debugging and reliable data platforms
Tags:
#DataEngineering, #DataAnalytics,
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