Datawarehouse on SQL database and Synapse analytics

Question

Hi friends, I have to design a data warehouse star schema model, can we build a data warehouse on Azure SQL database and we can also build on synapse analytics? What are the differences in terms of scalability and performance between these two? which is better option and what reason for that?

Answer 1

Hello Anshal :)

Yes, you can build a data warehouse using BOTH.

Both platforms offer capabilities for handling large volumes of data. But you should keep in mind that the top cases they deal with are different and the specifics of scalability/performance are not the same either.

Azure SQL Database

Azure SQL Database is a fully managed relational database service that offers built-in intelligence and scalability : designed for a wide range of applications, from small to large scale, providing dynamic scalability with minimal downtime.

Pros:

• You can scale the resources up or down based on your workload
• Offers automatic performance tuning and threat detection
• Built-in high availability and automatic backups

Cons:

• For extremely large datasets or complex queries, you might encounter performance limitations compared to a solution specifically optimized for data warehousing
• Optimized for OLTP workloads, which might not be ideal for complex analytical queries typical of data warehousing

Azure Synapse Analytics

Azure Synapse Analytics is a unified analytics service that combines the big data and data warehousing capabilities in a single framework. It allows working with data at scale incorporating both on-demand and provisioned methods of working.

Pros:

• It can handle petabytes of data, making it well-suited for big data scenarios
• Allows you to query data as it lands in your data lake, without the need to move or transform data
• Integrates with other Azure services like Power BI for visualization and Azure Machine Learning for advanced analytics
• Optimized for OLAP, ideal for complex queries and aggregations needed in data warehousing

Cons:

• Can be more expensive than Azure SQL Database, especially for on-demand query processing and large-scale storage
• Might be overkill for smaller datasets or simpler analytical requirements.

How to decide ?

• For typical data warehousing scenarios involving complex queries over large datasets, Azure Synapse Analytics is generally the better choice due to its massive scale and OLAP optimizations.
• If your workload is more transactional with smaller datasets, or if you're looking for a more cost-effective solution for less complex queries, Azure SQL Database might suffice.

Examples of use case:

• Use Azure SQL Database if you're dealing with transactional workloads or smaller datasets where real-time operational analytics are needed.
• Opt for Azure Synapse Analytics for large-scale data warehousing projects where you need to perform complex analytics across big data and relational data sources.

You can read more : https://www.cdata.com/blog/azure-synapse-vs-azure-sql

https://hevodata.com/learn/azure-synapse-vs-azure-sql-db/

https://www.schgroup.com/resource/blog-post/microsoft-azure-data-platform-comparison/

Answer 2

Hello Anshal,

Welcome to the Microsoft Q&A and thank you for posting your questions here.

I understand that you want to understand the differences in terms of scalability and performance between these two options, and ultimately determine which is the better option for your requirements because, you need to design a data warehouse star schema model and are considering whether to build it on Azure SQL Database or Azure Synapse Analytics.

Understanding Azure SQL Database and Azure Synapse Analytics

Azure SQL Database: Azure SQL Database is a fully managed relational database service provided by Microsoft Azure. It is based on the SQL Server database engine and is designed for building scalable, secure, and highly available databases in the cloud.

Azure Synapse Analytics: Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) is an analytics service that enables you to build, manage, and scale data warehouses in the cloud. It is designed for processing and analyzing large volumes of data and supports both relational and non-relational data.

Differences in Scalability and Performance

Scalability: Azure Synapse Analytics is designed for handling large-scale data processing and analytics workloads, offering elastic scalability to adjust resources based on demand. Azure SQL Database also supports scalability features but may have limitations compared to Synapse Analytics for very large datasets and complex analytics queries.

Performance: Azure Synapse Analytics is optimized for analytics and data warehousing workloads, with features such as massively parallel processing (MPP) and columnar storage for improved query performance. Azure SQL Database provides strong performance for OLTP (Online Transaction Processing) workloads but may not be as optimized for analytical queries as Synapse Analytics.

About OLAP and OLTP:

Azure Synapse Analytics leverages MPP architecture to distribute OLAP queries across multiple nodes, enabling parallel processing and faster analytical insights. Also, offers horizontal scalability for OLTP workloads by distributing the load across multiple nodes. This allows for handling high transaction volumes efficiently and many other benefits.

Azure SQL Database offers horizontal scalability through the use of elastic pools and managed instances, allowing for dynamic scaling of resources to handle varying workloads in OLTP environments. Also, offers integration with Azure Analysis Services and Azure Machine Learning for advanced analytics capabilities, allowing users to perform complex analytical tasks such as predictive modeling and data mining directly within the database environment.

Therefore, both Azure Synapse Analytics and Azure SQL Database provide a range of optimization features for OLTP and OLAP workloads, including scalability, indexing strategies, query performance tuning, and advanced analytics capabilities. However, the specific features and capabilities may vary between the two services, depending on the requirements of the workload and the desired level of performance optimization.

Choosing the Better Option

Consider your specific requirements, including the size of your dataset, the complexity of your queries, and your budget.

• If you need a highly scalable solution for processing large volumes of data and complex analytics queries, Azure Synapse Analytics may be the better option.
• If your requirements are more focused on transactional processing and you have a smaller dataset, Azure SQL Database may provide a cost-effective solution with sufficient performance.
• Evaluate the pricing and features of both options to make an informed decision based on your needs.

Both Azure SQL Database and Azure Synapse Analytics can be used to build a data warehouse star schema model, but the choice between them depends on factors such as scalability, performance, and specific business requirements.

I hope this is helpful! Do not hesitate to let me know if you have any other questions.

Please remember to "Accept Answer" if answer helped, so that others in the community facing similar issues can easily find the solution.

Best Regards,

Sina Salam