Data Engineering & Analytics Skill

Quick Start – SQL Data Pipeline

-- Create staging table
CREATE TABLE staging_events AS
SELECT 
  event_id,
  user_id,
  event_type,
  event_time,
  properties
FROM raw_events
WHERE event_time >= CURRENT_DATE - INTERVAL '1 day'
AND event_type IN ('click', 'purchase', 'view');

-- Aggregate metrics
SELECT
  DATE(event_time) as date,
  user_id,
  COUNT(*) as event_count,
  COUNT(DISTINCT event_type) as unique_events
FROM staging_events
GROUP BY 1, 2
ORDER BY date DESC, event_count DESC;

Core Technologies

Data Processing

• Apache Spark
• Apache Flink
• Pandas / Polars
• dbt (data transformation)

Data Warehousing

• Snowflake
• BigQuery (GCP)
• Redshift (AWS)
• Azure Synapse

ETL/ELT Tools

• dbt
• Airflow
• Talend
• Informatica

Streaming

• Apache Kafka
• AWS Kinesis
• Apache Pulsar

ML & Analytics

• scikit-learn
• TensorFlow
• Tableau / Power BI

Best Practices

1. Data Quality – Validation and testing
2. Documentation – Clear metadata
3. Performance – Query optimization
4. Governance – Data security
5. Monitoring – Pipeline alerts
6. Scalability – Design for growth
7. Version Control – Git for code and configs
8. Testing – Data and pipeline testing

Resources

• Apache Spark Documentation
• dbt Documentation
• SQL Mode Tutorial
• Kaggle