Below, we have listed some critical tasks that DevOps teams need to fulfil using specialized Data Engineering skills:

1. Integration of On-Premises and Cloud Systems:

• Connectivity: Data engineers establish secure and reliable connections between on-premises data sources (databases, applications, IoT devices) and cloud platforms (like AWS, Azure, Google Cloud)
• APIs and Middleware: Utilize APIs and middleware tools to facilitate data transfer and integration between different environments
• Data Gateways: Set up data gateways to securely transfer data from on-premises systems to the cloud

2. Streaming Data Ingestion:

• Choose Streaming Platforms: Select appropriate streaming platforms such as Apache Kafka, AWS Kinesis, or Azure Stream Analytics for collecting real-time data
• Setup Connectors: Implement connectors or adaptors to bridge on-premises data sources with cloud-based streaming platforms
• Schema Management: Ensure consistent schema management across on-premises and cloud systems to avoid compatibility issues

3. Data Transformation and Enrichment:

• ETL Processes: Create Extract, Transform, Load (ETL) processes to transform raw data into a usable format for analytics
• Enrichment: Add additional context or metadata to the streaming data for better insights
• Streaming SQL: Use streaming SQL languages (like KSQL, Spark SQL) for real-time transformations

4. Monitoring and Management:

• Performance Monitoring: Set up monitoring tools to track the performance of streaming workflows, both on-premises and in the cloud
• Alerting: Configure alerts for anomalies or issues in the data flow
• Resource Management: Optimize resource allocation and utilization for cost efficiency

5. Data Quality and Governance:

• Data Quality Checks: Implement checks to ensure data quality during ingestion and processing
• Metadata Management: Establish metadata catalogs to track data lineage, schemas, and usage
• Compliance: Ensure compliance with data governance policies and regulations across hybrid environments

6. Scalability and Fault Tolerance:

• Auto-Scaling: Configure auto-scaling mechanisms for streaming platforms to handle varying workloads
• High Availability: Design workflows with redundancy and failover mechanisms to ensure continuous operation

7. Data Security:

• Encryption: Implement end-to-end encryption for data in transit and at rest
• Access Control: Configure role-based access controls (RBAC) to restrict access to sensitive data
• Data Masking: Mask or anonymize sensitive data to protect privacy

8. Testing and Validation:

• Unit Testing: Develop and execute unit tests for individual components of the streaming workflow
• Integration Testing: Test the entire workflow end-to-end to ensure seamless data flow
• Data Lineage Testing: Verify data lineage to ensure data integrity and accuracy

9. Documentation and Knowledge Sharing:

• Workflow Documentation: Document the configuration, architecture, and processes for the streaming workflows
• Training: Conduct training sessions for other team members on how to manage and troubleshoot the hybrid cloud streaming environment