Partition strategies, data exploration layers, and metadata management using Azure Data Lake Storage Gen2, Azure Synapse Analytics, and Microsoft Purview.
Covers designing and implementing data storage solutions on Azure, including partition strategies for files, analytical workloads, streaming workloads, and Azure Synapse Analytics. Includes creating and executing queries using SQL serverless and Spark clusters, recommending Azure Synapse Analytics database templates, and managing data lineage and metadata through Microsoft Purview Data Catalog. Also covers identifying when partitioning is needed in Azure Data Lake Storage Gen2. This domain represents 15–20% of the exam.
5 minutes
5 Questions
Design and Implement Data Storage is a critical domain in the Azure Data Engineer Associate certification that focuses on architecting efficient, scalable, and secure data storage solutions on Microsoft Azure.
**Azure Data Lake Storage (ADLS) Gen2** is a cornerstone, combining the scalability of blob storage with hierarchical namespace capabilities, enabling efficient big data analytics. Data engineers must understand how to design partition strategies, implement folder structures, and optimize file formats like Parquet, Avro, Delta, and ORC for analytical workloads.
**Azure Synapse Analytics** provides dedicated and serverless SQL pools for data warehousing. Engineers must design star and snowflake schemas, implement distribution strategies (hash, round-robin, replicated), create columnstore indexes, and manage partitioning for optimal query performance.
**Azure Databricks** with Delta Lake enables ACID transactions on data lakes, supporting upserts, time travel, and schema enforcement. Understanding Delta Lake architecture is essential for building reliable lakehouse solutions.
**Storage design considerations** include selecting appropriate storage tiers (Hot, Cool, Archive), implementing lifecycle management policies, configuring redundancy options (LRS, GRS, ZRS, RA-GRS), and designing for cost optimization.
**Security implementation** involves configuring role-based access control (RBAC), Access Control Lists (ACLs), Shared Access Signatures (SAS), encryption at rest and in transit, managed identities, Azure Key Vault integration, and implementing data masking and row-level security.
**Data partitioning strategies** are vital for performance optimization. Engineers must design effective partition keys, implement incremental loading patterns, and manage data skew.
**External tables and PolyBase** enable querying data across different storage systems without data movement, supporting hybrid architectures.
Key skills include implementing slowly changing dimensions (SCDs), designing metadata-driven pipelines, managing schema drift, and ensuring data quality. Engineers must also understand how to implement soft deletes, data retention policies, and purging strategies to comply with governance requirements. Mastering these concepts ensures building robust, performant, and cost-effective data storage solutions on Azure.Design and Implement Data Storage is a critical domain in the Azure Data Engineer Associate certification that focuses on architecting efficient, scalable, and secure data storage solutions on Microsoft Azure.
**Azure Data Lake Storage (ADLS) Gen2** is a cornerstone, combining the scalability of b…
