🏗️ Data Engineering

Data Engineering

Technical blog about ETL/ELT pipelines, data warehouses, and data lakes

What is Data Engineering?

Data Engineering is the process of collecting, transforming, and storing raw data to make it available for analysis and decision-making. Key areas include building ETL/ELT pipelines, designing data warehouses, and implementing data lake architectures.

🏗️ Data Engineering Architecture

5. Serving Layer

Data consumption and utilization

BI Tools (Tableau, Power BI)

ML Models and APIs

Data Applications

↓

4. Processing Layer

Data transformation and processing

ETL/ELT Pipelines

Batch and Streaming Processing

Data Cleaning and Transformation

↓

3. Metadata and Governance Layer

Data management and control

Schema and Table Version Management

Permissions and Access Control

Data Quality and Governance

↓

2. Storage Layer

Data storage and management

Data Lake (S3, ADLS)

Data Warehouse

Lakehouse (Delta Lake, Iceberg)

↓

1. Ingestion Layer

Data collection and ingestion

Batch Data Sources

Streaming Data Sources

CDC and Real-time Ingestion

📋 Key Technology Stack by Layer

Ingestion: Apache Kafka, Apache Flume, AWS Kinesis

Storage: AWS S3, Azure Data Lake, Snowflake, BigQuery

Metadata: Apache Atlas, AWS Glue, Delta Lake

Processing: Apache Spark, Apache Airflow, Apache Flink

Serving: Tableau, Power BI, Looker, REST API

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Posts Coming Soon

Additional posts for the Data Engineering category will be released soon!

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