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On-Prem SQL Server Reference Architecture

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This is a standard reference architecture to implement TimeXtender fully On-Premises.

To prepare your TimeXtender environment, here are the steps we recommend.

  1. Create Application Server - Local Server
  2. Create ODX Storage - SQL Server Data Storage
  3. Create MDW (and DSA) Storage - SQL Server Data Storage
  4. (optional) Create Semantic Model - SQL Server Analysis Services
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1. Create Application Server - Local Server

To serve the TimeXtender application on-premise, we recommend using an Windows environment such as Windows Server sized according to your solution's requirements.
Guide:

Configure an On-Prem Application Server & Storage

Considerations:

 

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2. Create ODX Storage - SQL Server Data Storage

To integrate your siloed data, on-prem SQL DB can store your raw data quickly and easily.
Guide:

Configure the ODX Server

Considerations:
  • ODX Server Storage database can be created directly in TimeXtender
  • ODX Server Service should be hosted locally on the App Server
  • SQL Server 17 or higher is recommended

 

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3. Create MDW Storage - SQL Server Data Storage

SQL DB is a great option for the data warehouse storage, both Modern Data Warehouse (MDW) and Data Staging Area (DSA).
Guide:

Data Warehouses

Considerations:
  • A data warehouse in TimeXtender is a SQL Server database on premise (or in Azure) where your data is stored for queries and analysis.
  • Often, a TimeXtender project consists of one data warehouse where you consolidate data from one of more staging databases and a number of data sources.
  • SQL Server 2017 or higher is recommended
  • If using a Data Staging Area (DSA) in your solution, you can use a separate SQL DB as the MDW.

 

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4. (optional) Create Semantic Model - SQL Server Analysis Services

SQL Server Analysis Services is the ideal way to serve data to Power BI and Excel. Since there are many ways to connect visualization tools directly to the MDW, this is an optional step.
Guide:

Building Semantic Models

Considerations:
  • TimeXtender stores semantic models in Analysis Services Tabular model behind the scenes.
  • Each semantic model can be deployed to one or more endpoints.

 

 

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