Low Level Design For Batch and Streaming Data Platform In Azure

@Relay

It sounds like you’re diving into creating a low-level design document for a batch and streaming data platform on Azure. That's quite the task! Here are some components and considerations you might want to keep in mind for each element of your design:

Components to Consider

Data Sources - Identify all data sources including IoT devices, databases, and external APIs. Ensure that you have mechanisms to validate data integrity and handle data source unavailability.

Data Ingestion:

• Batch: Use tools like Azure Data Factory for scheduled data extraction.
• Streaming: Consider Azure Stream Analytics or Azure Event Hubs for real-time data ingestion.

Data Storage - Design your data lake or storage solution.

• Delta Lake: Great for both batch and streaming; ensures ACID properties.
• Blob Storage: Use for storing raw data and for batch processing.

Data Processing:

• Batch Processing - Using Azure Databricks with scheduled notebooks or Azure Data Factory for transformations.
• Streaming Processing - Utilize Azure Stream Analytics or Spark Streaming in Databricks for real-time data processing.

Data Transformation - Incorporate ETL processes tailored to your architecture. Be sure to consider both batch transformations (e.g., Aggregation, Joins) and streaming transformations (Real-time filtering, aggregations).

Error Handling and Failure Management: Retries - Implement retry policies for transient errors.
Dead Letter Queue - For messages that fail processing to be reviewed later.
Monitoring and Alerts - Use Azure Monitor to track the health of your pipelines and set up alerts for failures.

Data Quality Checks - Consider validations to ensure data integrity and accuracy. Integrate checkpoints in your processing flows.

Performance Optimization - Scale your Spark clusters based on workload. Leverage partitioning strategies to improve performance for both batch and streaming jobs.

Security and Compliance - Implement access controls, encryption for data at rest and in transit. Ensure compliance with data handling regulations relevant to your business.

Documentation and Training - Make sure to document all components and provide guidelines for those who will maintain this architecture.

Restartability

For restartability in your system, you need to - Maintain stateful information in workflows, so on failure, the process resumes without data loss (e.g., using checkpointing in Spark).
Record the last processed records' offsets for message-based systems to replay data if necessary.
Design your batch jobs to be idempotent, enabling safe retries. For more details refer:

• Azure Stream Analytics: Real-time event processing reference architecture
• Azure HDInsight highly available solution architecture case study
• Big data architectures
• Business continuity and disaster recovery for cloud-scale analytics

I hope this information helps.

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