![]() ![]() In many cases, your MySQL 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 MySQL instance with modeled customer data directly from your warehouse. 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. 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. 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.Įmbedded analytics is also another extremely relevant use case for MySQL. Providing user-level recommendations to improve your on-site personalization requires you to categorize your users into groups based on their behavior. 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 MySQL. Transactional databases just aren't made for analytics use cases. 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 MySQL instance. 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. On the other hand, production databases like MySQL aren't designed to tackle large complex queries or transform your data. Platforms like Amazon Redshift 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. Why is it valuable to sync Amazon Redshift data to MySQL?
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