1. Design and implement data storage

• Azure Data Lake Storage Gen2: Understanding its features, including hierarchical namespace, security (ACLs, shared access signatures), and how it serves as a data lake for analytics.
• Data Partitioning Strategies: Designing efficient partitioning schemes for various data types (files, analytical workloads, Azure Synapse Analytics) to optimize performance and scalability.
• Data Archiving Solutions: Implementing strategies to manage data lifecycle and cost-effectively archive data.
• Serving Layer Design: Designing star schemas, dimensional hierarchies, and analytics stores for efficient reporting and analysis.
• Data Models: Planning and designing data models that support reporting, analytics, and operational dashboards.
• File Types: Recommending appropriate file types for storage and analytical queries based on use cases.
• Efficient Querying: Designing for efficient querying, including data pruning and appropriate data distribution strategies.
• Implementing Physical and Logical Structures: Implementing data compression, partitioning, sharding, data redundancy, and building logical folder structures and external tables.

2. Design and develop data processing

• Azure Data Factory (ADF): Creating and managing data pipelines for ETL/ELT workflows, including linked services, datasets, triggers, and data flows.
• Azure Synapse Analytics Pipelines: Orchestrating data movement and transformation activities within Azure Synapse Analytics.
• Apache Spark (in Azure Synapse and Databricks): Utilizing Spark for data exploration, transformation, and batch processing.
• Azure Databricks: Leveraging the collaborative analytics platform built on Apache Spark for data engineering, analytics, and machine learning, notes Koenig-solutions.com.
• Azure Stream Analytics: Processing real-time streaming data from various sources (like IoT devices or application logs).
• Hybrid Transactional/Analytical Processing (HTAP): Using Azure Synapse Link for near real-time analytics with Azure Cosmos DB and SQL.
• Data Ingestion Techniques: Implementing various data ingestion methods for batch and real-time data, handling large volumes of sales data into Azure Synapse, and dealing with duplicate, missing, and late-arriving data.
• Data Transformation: Applying transformations using Transact-SQL, Data Factory mapping data flows, Apache Spark, and exploratory data analytics techniques.
• Managing Batches and Pipelines: Automating tasks with Azure Functions, handling failed batch loads, validating batch loads, scheduling data pipelines, and implementing version control.

3. Design and implement data security

• Data Encryption: Designing and implementing encryption for data at rest and in transit using Azure's native features and potentially customer-managed keys with Azure Key Vault.
• Access Control: Implementing Azure Role-Based Access Control (RBAC), row-level security, and column-level security for fine-grained access management.
• Network Security: Configuring firewall rules, private endpoints, and network security groups (NSGs).
• Data Auditing and Privacy: Designing data auditing strategies and implementing data masking techniques.
• Compliance: Ensuring data processing and storage practices meet regulatory standards (e.g., GDPR, HIPAA, SOC).

4. Monitor and optimize data storage and data processing

• Monitoring Tools: Implementing logging and monitoring using Azure Monitor, Log Analytics, and CloudWatch events for performance and operational insights.
• Performance Tuning: Optimizing query performance (e.g., using indexes, caching, partitioning, understanding skew and spill), tuning Spark jobs, and optimizing Azure Synapse Analytics storage and processing.
• Troubleshooting: Identifying and resolving issues related to failed Spark jobs, pipeline runs, and connectivity problems.
• Cost Optimization: Optimizing storage, compute, and networking resources to reduce operational expenses.