
When this happens...
Automatically do this!
Enable Integrations or automations with these events of MYSQL and Coursera: Data Science Specialization
Find row(s) via a lookup column.
You can select, create, update or delete row(s) of table via custom SQL query.
Adds a new row into the table.
Update any existing row in the given table.
Deletes row from a table.
this is only for testing purpose
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To start, connect both your MYSQL and Coursera: Data Science Specialization accounts to viaSocket. Once connected, you can set up a workflow where an event in MYSQL triggers actions in Coursera: Data Science Specialization (or vice versa).
Absolutely. You can customize how MYSQL data is recorded in Coursera: Data Science Specialization. This includes choosing which data fields go into which fields of Coursera: Data Science Specialization, setting up custom formats, and filtering out unwanted information.
The data sync between MYSQL and Coursera: Data Science Specialization typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between MYSQL and Coursera: Data Science Specialization. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
MySQL is an open-source relational database management system (RDBMS) that uses Structured Query Language (SQL) for managing and querying data. It is widely used for its speed, reliability, and flexibility in handling large-scale database applications. MySQL is commonly employed in web development and is a key component of the popular LAMP stack (Linux, Apache, MySQL, PHP/Perl/Python).
Learn MoreMaster the art of data science with Coursera’s Data Science Specialization, created by Johns Hopkins University. This program guides you through the full data science pipeline—from data wrangling and statistical analysis to machine learning and data visualization—using R and real-world projects.
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