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AMP Analytics SDK MS SQL Server

Integrate your AMP pages with MS SQL Server

Don’t go through the pain of direct integration. RudderStack’s AMP Analytics SDK makes it easy to send data from your AMP pages to MS SQL Server and all of your other cloud tools.

Easy AMP Analytics SDK to MS SQL Server Integration with RudderStack

RudderStack’s open source AMP Analytics SDK allows you to integrate RudderStack with your AMP pages to track event data and automatically send it to MS SQL Server. With the RudderStack AMP Analytics SDK , you do not have to worry about having to learn, test, implement or deal with changes in a new API and multiple endpoints every time someone asks for a new integration.

Popular ways to use MS SQL Server

Load event data

Easily send event stream and batch data to MS SQL Server from multiple sources.

Automatic schema creation

Skip the formatting work in MS SQL Server and move faster with predefined data structures.

Customize sync scheduling

Easily configure custom sync schedules to MS SQL Server that work with your existing data workflows.

Frequently Asked Questions

With Rudderstack, integration between AMP Analytics SDK and MS SQL Server is simple. Set up a AMP Analytics SDK source and start sending data.

Pricing AMP Analytics SDK and MS SQL Server can vary based on the way they charge. Check out our pricing page for more info. Or give us a try for FREE.

Timing can vary based on your tech stack and the complexity of your data needs for AMP Analytics SDK and MS SQL Server.

Use the MS SQL Server integration with other popular sources

Hugo

Go SDK

RudderStack SDKs

RudderStack MS SQL Server Documentation

Refer to our step-by-step guide and start using MS SQL Server today

Use the AMP Analytics SDK integration with other popular destinations

RudderStack AMP Analytics SDK Documentation

Refer to our step-by-step guide and start using AMP Analytics SDK today

About MS SQL Server

Microsoft SQL Server is a popular relational database management system (RDBMS). It is ideal for a variety of audiences and data workloads - from small-scale, single-machine data applications to large applications with thousands of concurrent users.

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