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)