Table Storage Design Patterns

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Note: Azure Table and Queue Storage support NoSQL and message handling for scalable cloud apps

 Table Storage Design Patterns

Azure Table Storage design patterns optimize how data is stored, queried, and scaled in a NoSQL key-value store. Data is identified by PartitionKey (grouping) and RowKey (unique within a partition). Good design ensures high performance and low cost.

Common Patterns:

Vertical Partitioning: Split unrelated data into multiple tables to keep queries fast.

Horizontal Partitioning (Sharding): Distribute entities across partitions using smart PartitionKeys to avoid bottlenecks.

Denormalization: Duplicate data in multiple entities to avoid expensive cross-partition joins.

Compound Keys: Combine values in RowKey (e.g., date + ID) for sorting and range queries.

Time-based Partitioning: Use date/time in PartitionKey for time-series data to optimize retrieval.

Index Table Pattern: Create additional tables as secondary indexes for alternate query paths.

Single Table Inheritance: Store multiple entity types in one table with a Type column for flexibility.

Best Practices: Choose PartitionKey to spread workload evenly, avoid “hot” partitions, and keep entity size ≤1 MB. Proper pattern selection ensures scalability, predictable performance, and cost efficiency.

Read More

Indexing in Azure SQL

Partitioning in Data Lake

Normalization vs Denormalization

Star and Snowflake Schemas

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