| 1. Data Engineering Fundamentals | ETL Pipelines (Extract, Transform, Load)**: Building ETL pipelines | Python, SQL, Apache Airflow, Data Factory | High |
| Data Warehousing: Columnar storage, optimizing for analytics | Amazon Redshift, Google BigQuery, Azure Synapse | High |
| Batch vs. Real-Time Processing: Understanding when to use batch vs. real-time | Apache Spark, Apache Kafka, AWS Glue, Dataflow | Medium |
| Data Integration: Integrating various data sources (APIs, databases, streaming) | Python, Apache Kafka, APIs | High |
| 2. Programming Skills | Python: Core programming language | Python (libraries like Pandas, NumPy, PySpark) | High |
| SQL: Querying and manipulating data | MySQL, PostgreSQL, SQL Server, SQLite | High |
| Other Languages (Java, Scala): Beneficial for distributed systems | Java, Scala, Spark | Medium |
| 3. Big Data Technologies | Apache Hadoop: Framework for large dataset storage and processing | Hadoop, HDFS | Medium |
| Apache Spark: Distributed data processing | Apache Spark, Databricks | High |
| Apache Kafka: Real-time data streaming | Apache Kafka | High |
| Apache Flink: Real-time data processing | Apache Flink | Low |
| 4. Data Storage Technologies | Relational Databases: Structured data storage | MySQL, PostgreSQL, SQL Server | High |
| NoSQL Databases: Semi-structured/unstructured data | MongoDB, Cassandra, Redis | Medium |
| Data Lakes: Raw data storage for big data | AWS S3, Azure Data Lake, Google Cloud Storage | High |
| Columnar Databases: Optimized for analytical queries | Amazon Redshift, Google BigQuery, Azure Synapse | High |
| 5. Cloud Computing & Platforms | Cloud Providers: AWS, Azure, GCP | AWS, Azure, GCP | High |
| Data Engineering Services on the Cloud: ETL and data processing | Azure Data Factory, AWS Glue, Google Dataflow | High |
| Containers & Orchestration: For deploying services | Docker, Kubernetes | Medium |
| 6. Data Processing Frameworks | Apache Spark: Large-scale data processing | Apache Spark, Databricks | High |
| MapReduce: Hadoop processing model | Hadoop, Apache Spark | Medium |
| Airflow: Orchestrating data workflows | Apache Airflow | High |
| 7. Data Transformation & Modeling | ETL Processes: Extract, transform, and load processes | Python (Pandas), Spark | High |
| Data Cleansing: Handling missing data, outliers, and inconsistencies | Python (Pandas, NumPy) | High |
| Data Enrichment: Enhancing data with additional context | SQL, Python, Data Integration tools | Medium |
| Dimensional Modeling: Building data models for analytical queries (star/snowflake schema) | SQL, Dimensional modeling concepts | Low |
| 8. Business Intelligence (BI) & Visualization | BI Tools: Building dashboards and reports | Power BI, Tableau, Looker | Medium |
| KPI Identification: Understanding KPIs for business decision-making | Power BI, Tableau, business analysis | Medium |
| 9. DevOps & Automation | CI/CD: Automating data pipeline deployment | Jenkins, GitLab CI, GitHub Actions | Medium |
| Infrastructure as Code: Automating cloud infrastructure setup | Terraform, AWS CloudFormation | Medium |
| Monitoring & Logging: Ensuring the health of pipelines | Prometheus, Grafana, AWS CloudWatch | Medium |
| 10. Data Governance & Security | Data Governance: Managing data availability and usability | Azure Data Lake, AWS S3 | Medium |
| Data Privacy & Compliance: Ensuring data protection and legal compliance | GDPR, HIPAA | Medium |
| Security Best Practices: Encrypting and securing data | Encryption, AWS IAM, Azure Security | High |
| 11. Machine Learning (Optional but Useful) | Basic ML Concepts: Understanding integration into data pipelines | Scikit-learn, TensorFlow, PyTorch | Low |
| Model Deployment: Deploying models for real-time predictions | Flask, Docker, Kubernetes | Low |