Optimize Data Loading in Azure Synapse Analytics | Staging Table Design
Question
You are designing an enterprise data warehouse in Azure Synapse Analytics. You plan to load millions of rows of data into the data warehouse each day.
You must ensure that staging tables are optimized for data loading.
You need to design the staging tables.
What type of tables should you recommend?
Answers
Explanations
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In Azure Synapse Analytics, when designing staging tables for loading millions of rows of data each day, the recommended type of tables is Hash-distributed tables.
Hash-distributed tables divide the data into multiple nodes using a hash function, which results in an even distribution of data across the nodes. This allows for high-performance data loading and querying by parallelizing the operations across multiple nodes, resulting in faster data processing.
Round-robin distributed tables distribute data evenly across nodes sequentially. This approach can result in uneven data distribution, making it less efficient than hash-distributed tables for large-scale data loading.
Replicated tables create identical copies of the data across all nodes. This approach is useful for small tables that need to be frequently accessed but can be expensive in terms of storage and processing resources when used for larger tables.
External tables are used to access data stored outside of the data warehouse, such as in Azure Blob storage or Azure Data Lake Storage. They do not store data within the data warehouse, so they are not suitable for staging tables.
In summary, when designing staging tables for loading millions of rows of data each day in Azure Synapse Analytics, it is recommended to use Hash-distributed tables due to their ability to evenly distribute data across nodes and parallelize operations for faster data processing.
