Differences between Azure SQL Database and Azure Synapse Analytics

IT
  • What is the difference between Azure SQL Database and Azure Synapse Analytics in terms of usage?
  • What are the advantages and disadvantages of each?

Here is an article that can answer the above questions.

By reading the article “What are the differences between Azure SQL Database and Azure Synapse Analytics and their advantages and disadvantages”, you will know the criteria for selecting the right product for your requirements.

Differences between Azure SQL Database and Azure Synapse Analytics uses

In conclusion, the respective differences are as below.

Azure SQL Database.

In case, there is not a lot of data stored in a single table, or where a single piece of data is quickly retrieved, such as in OLTP.

Azure Synapse Analytics

In case, there are multiple tables of large size containing more than 60 million rows and where a large amount of data is read and analyzed at once.

Details

Azure Synapse Analytics is suitable for the following scenarios.

  • Large numbers of rows of data in a single table (more than 60 million rows).
  • Reading large amounts of data at a time.

 

Azure Synapse Analytics is optimized with clustered column store indexes by default.

The clustered column store index enables fast reading of large volumes of data by compressing them into row groups of approximately one million rows.

This row group is further distributed across 60 distributions for parallel processing.

Therefore, when the number of rows of data stored in a single table is more than 60 million (1 million rows x 60 distributions), optimum performance can be achieved.

However, if tables with less than 60 million rows of data, the compression efficiency may be poor and performance may not be maximized.

Columnstore tables generally won’t push data into a compressed columnstore segment until there are more than 1 million rows per table. Each dedicated SQL pool table is partitioned into 60 tables. As such, columnstore tables won’t benefit a query unless the table has more than 60 million rows.

 Tip

For tables with less than 60 million rows, having a columnstore index may not be the optimal solution.

Reference:https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool

Conversely, Azure SQL Database is more suitable when there is not a lot of data stored in a single table or a single piece of data is retrieved quickly, such as OLTP.

Advantages and disadvantages

Advantages

Azure Synapse Analytics is optimised for large data sets and faster than symmetric multi-processing (SMP) based systems such as Azure SQL Database for batch-oriented workloads of reads and analyses using that data.

Disadvantages

Due to its characteristics, it incurs overheads such as analyzing queries on the front control node, creating queries to be executed on each node, and also sending and receiving data between nodes.

This causes more overhead when executing small queries with short execution times, and may result in slower processing than the Azure SQL Database.

Conclusion

I hope that the above information will help you understand the differences between Azure SQL Database and Azure Synapse Analytics.

Let’s go over the content one last time.

  • Azure SQL Database

In case, there is not a lot of data stored in a single table, or where a single piece of data is quickly retrieved, such as in OLTP.

  • Azure Synapse Analytics

In case, there are multiple tables of large size containing more than 60 million rows and where a large amount of data is read and analyzed at once.

 

  • Advantages

Azure Synapse Analytics is optimised for large data sets and faster than symmetric multi-processing (SMP) based systems such as Azure SQL Database for batch-oriented workloads of reads and analyses using that data.

  • Disadvantages

Due to its characteristics, it incurs overheads such as analyzing queries on the front control node, creating queries to be executed on each node, and also sending and receiving data between nodes.

This causes more overhead when executing small queries with short execution times, and may result in slower processing than the Azure SQL Database.

 

Reference

Best practices for dedicated SQL pools in Azure Synapse Analytics

Best practices for serverless SQL pool in Azure Synapse Analytics

What is Azure SQL Database?