Fundamentals of Data Engineering

1. Data Storage

• Relational databases (e.g., SQL, MySQL, PostgreSQL)
• NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB)
• Data warehousing (e.g., Snowflake, Redshift, BigQuery)
• Distributed file systems (e.g., Hadoop HDFS, Amazon S3)

2. Data Modeling

• Relational data modeling
• Dimensional modeling
• Data normalization and denormalization
• Schema design

3. Data Integration

• Techniques and tools used for integrating data from different sources.
• Data cleansing, 
• Data profiling, 
• Data mapping, 
• Data transformation.

4. Data Ingestion

• Extract, Transform, Load (ETL) processes
• Real-time data streaming
• Data integration techniques
• Change Data Capture (CDC)

5. Data Transformation and Processing

• Data pipelines and workflows
• Batch processing (e.g., Apache Spark, Apache Flink)
• Stream processing (e.g., Apache Kafka, Apache Samza)
• Data orchestration (e.g., Apache Airflow, Luigi)

6. Data Quality and Governance:

• Data cleansing and validation
• Data deduplication and de-duplication techniques
• Data quality monitoring and profiling
• Data governance frameworks and practices

7. Data Integration and APIs

• API design and development
• API protocols (e.g., REST, SOAP)
• API authentication and authorization
• API versioning and management

8. Data Security and Privacy

• Data encryption techniques
• Access controls and permissions
• Data anonymization and pseudonymization
• Compliance with data protection regulations (e.g., GDPR, CCPA)

9. Data Pipeline Monitoring and Management

• Logging and monitoring techniques
• Performance optimization and tuning
• Error handling and retries
• Alerting and notification systems

10. Data Visualization and Reporting

• Data visualization tools (e.g., Tableau, Power BI)
• Dashboard design and development
• Data exploration and ad-hoc querying
• Report generation and distribution

11. Cloud Data Platforms

• Cloud storage services (e.g., Amazon S3, Google Cloud Storage)
• Cloud-based data warehouses (e.g., Snowflake, BigQuery)
• Serverless data processing (e.g., AWS Lambda, Google Cloud Functions)
• Data engineering on cloud platforms (e.g., AWS, Google Cloud, Azure)

12. Data Scalability and Performance

• Horizontal and vertical scaling techniques
• Partitioning and sharding strategies
• Indexing and query optimization
• Caching mechanisms

13. Data Governance and Compliance

• Data cataloging and metadata management
• Data lineage and traceability
• Data access controls and permissions
• Compliance with regulatory requirements (e.g., GDPR, HIPAA)