Sync data from
Microsoft SQL Server to MongoDB
Connect your data from Microsoft SQL Server to MongoDB with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.
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Integrate your data in 3 easy steps
Add your source and destination
Connect to 15+ data sources, like Microsoft SQL Server, and 150+ destinations, like MongoDB.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to MongoDB, and start syncing.
Model your Microsoft SQL Server data using any of these methods
dbt Model Selector
Sync directly with your dbt models saved in a git.
Create and Edit SQL from your browser. Hightouch supports SQL native to Microsoft SQL Server.
Select available tables and sheets from Microsoft SQL Server and sync using existing views without having to write SQL.
For less technical users, pass traits and audiences from Microsoft SQL Server using our visual segmentation builder.
Why is it valuable to sync Microsoft SQL Server data to MongoDB?
Platforms like Microsoft SQL Server have become the standard for modeling and transforming large quantities of data so you can answer complex analytics questions as quickly and efficiently as possible.
On the other hand, production databases like MongoDB aren't designed to tackle large complex queries or transform your data. They're built to power your product experiences, and handle large volumes of small transactions in real-time, whether it's looking up or editing information about a single user, processing orders, accepting payments, or even granting access to specific product features.
Unless you have an unlimited amount of time and money, it's impossible to calculate core metrics about your customers and build behavioral prediction models in your MongoDB instance. Transactional databases just aren't made for analytics use cases. Most likely you're already ingesting all of your production level data into your data warehouse via an ETL pipeline, so the logical step is simply to sync that data to MongoDB.
Providing user-level recommendations to improve your on-site personalization requires you to categorize your users into groups based on their behavior. You might want to offer a coupon to customers with certain products in their cart, or maybe you want to group users into specific categories (e.g., power users, garden lovers, high-value customers, etc.) Either way, it's only possible and performant to build these data models in your warehouse.
Embedded analytics is also another extremely relevant use case for MongoDB. If your app offers built-in reporting and visualization features it's much easier to do aggregations and transformations in your warehouse and sync those results to your application database to power your user-facing visualizations.
If you've ever built an internal application like an in-house CRM or a marketing platform, there's a good chance it's running off of your application database. In many cases, your MongoDB database doesn't have all of the modeled data you need to power this end tool and that's why it's so important to hydrate your MongoDB instance with modeled customer data directly from your warehouse.
Why should you use reverse ETL to connect Microsoft SQL Server and MongoDB data?
In the past syncing data from your data warehouse to MongoDB required you to integrate with various APIs and build and maintain in-house pipelines. Even if your engineering team successfully builds a custom pipeline to your production database, a single API change or rate limit can quickly break everything.
Integrating with third-party APIs is complex, expensive, and time-consuming, so the path of least resistance is often downloading and uploading manual CSV files. CSVs are not a long-term solution because they go stale quickly, and you can never fully trust the accuracy of your data.
Workflow automation tools have arisen to solve this problem, but managing various if/then statements creates an intricate web of dependencies prone to failure. On the other hand, customer data platforms (CDPs) force you to create a second source of truth and pay for another storage layer in addition to your warehouse.
Hightouch eliminates these problems with Reverse ETL. You can query directly against your data warehouse using standard SQL, your current tables, and even use your existing data models. All your data is automatically diffed between sync runs to ensure you're only syncing the freshest data.
Any failed rows are automatically retried in the next sync. You can easily view live API responses/requests and use a live debugger to identify failed runs and problematic data. Hightouch will write the results of your sync back to your warehouse so you can easily analyze your logs.
Hightouch even integrates with git so you can manage and update your syncs bi-directionally in your git repo. You can even send alerts to your favorite messaging tools like Slack or email.
With Hightouch, all you have to do is connect to your data warehouse and map the proper columns in your data warehouse to the appropriate fields in your destination.
Run complex queries on your data source and write the results into MongoDB
About Microsoft SQL Server
SQL Server is a relational database management system, or RDBMS, developed and marketed by Microsoft.Learn more about Microsoft SQL Server
MongoDB is an open-source, NoSQL, and document-oriented database that is used for high-volume data storage.Learn more about MongoDB
Other Microsoft SQL Server Integrations
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Hightouch Playbooks: Best practices to leverage reverse ETL
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