TimeXtender supports multiple deployment targets in Azure, enabling many different possible configurations of the Data Estate. The below diagram provides an overview of the supported data storage platforms for each layer.
To configure TimeXtender in Azure, decide which data storage options you desire for each layer, then select the appropriate Azure Marketplace template from the list below. Below are suggested use cases for each storage option, however, we highly recommend you consult your TimeXtender partner to help you select the options that will best fit your needs.
Azure SQL Single Database
|Availability||99.995%||Ease of Use||High|
|Ideal Layer||MDW, ODX||SQL Parity||Medium|
As a general guideline, Azure SQL Single Database is an ideal solution for ideal for a cloud data warehouse utilizing fewer than billion row tables and requiring less than several terabytes of storage. SQL Database is able to be deployed and scaled in minutes. Because cross-database queries are not supported, the stage, transform and presentation layers of the DWH reside on the same database assorted by schema and security. While access may be limited using firewall rules, private IP addresses cannot be assigned.
Azure SQL Managed Instance
|Availability||99.99%||Ease of Use||Med|
|Ideal Layer||MDW, ODX||SQL Parity||High|
Managed instance is closer in feature parity to the SQL Server database engine making it ideal if you intend to migrate an existing on-premise solution into Azure. While cross-database queries are supported, deployment and scaling of a managed instance can take many hours. This option also supports Private IP address available within Azure VNet. Managed Instance offers features such as point-in-time recovery, automated backups, and transparent data encryption by default. While these features provide very high data durability and security, they also take up additional processing power and log throughput which can cause some performance issues on large data sets. You can learn more about the performance of managed instance here.
Azure Synapse SQL Pool (Previously SQL Data Warehouse)
|Availability||99.9%||Ease of Use||Low|
|Ideal Layer||MDW||SQL Parity||Low|
Azure Synapse Analytics (formerly Azure SQL Data Warehouse) is a massively parallel processing database similar to other columnar-based scale-out database technologies such as Snowflake, Amazon Redshift, and Google BigQuery. As a general guideline, a Synapse SQL Pool is ideal for data warehouse workloads utilizing billion row tables and requiring more than several terabytes of storage. However, It may not be an ideal solution for smaller implementations. TimeXtender can instantly translate the code to fit the Synapse architecture, however because Synapse is so different from traditional SQL, we strongly encourage a ground up re-design of the DWH model. Azure Synapse performs best when used on top of Azure Data Lake. Learn more about Azure Synapse Analytics Here.
Azure Data Lake
|Availability||99.9%||Ease of Use||Low|
|Ideal Layer||ODX||SQL Parity||N/A|
Azure Data Lake file storage is PaaS for Hadoop, a massively scalable Hadoop distributed file store with integrated hierarchical namespace. TimeXtender's ODX Server enables the quick and easy movement of data into the Azure Data Lake, and back out into the Data Warehouse database of your choice (Options above). The Data Lake is ideal if you plan to replicate and store many of your source systems, files, relational, and non-relational data for Big Data and AI applications.
Azure Analysis Services
|Availability||99.9%||Ease of Use||High|
|Ideal Layer||Semantic||SQL Parity||High|
Azure Analysis Services delivers enterprise-grade BI semantic modeling capabilities with the scale, flexibility, and management benefits of the cloud. This is SQL Server Analysis Services Tabular, In the Cloud. Analysis Services is ideal for storing relevant aggregated data marts in-memory for lighting fast consumption by Power BI & similar reporting platforms.
If you're interested in how much these services may cost you can Estimate Cost of Azure Services.