Securely integrate Google Cloud SQL for MySQL with Mode
About Google Cloud SQL for MySQL
Google Cloud SQL for MySQL is a fully-managed database service that makes it easy to set up, maintain, manage, and administer your relational MySQL databases in the cloud.
As an advanced, high-speed data analytics platform, Mode helps you understand the information in your data warehouses. As a hybrid solution Mode lets (1) data teams customize queries and visualizations in Python, SQL, or R; and (2) empowers non-tech-savvy users to dive into visualizations and explore live-updating reports to get the data they need.
Popular Use Cases
Bring all your Mode data to Amazon Redshift
Load your Mode data to Google BigQuery
ETL all your Mode data to Snowflake
Move your Mode data to MySQL
Integrate Google Cloud SQL for MySQL With Mode Today
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Mode's End Points
Mode White-Label Embeds and Visualizations
Mode SQL Editor
Mode's SQL editor features a schema browser to explore the tables and columns of your connected data sources. It also has an autocomplete function to assist with query writing, a query history to browse and reuse old queries, and advanced logic tools for creating loops, IF statements, and more.
Mode Helix Data Engine
The technology behind Mode's analytics solution is Helix, a high-performance in-memory data engine that combines modern drag-and-drop business intelligence tools with a code-first (SQL, Python, R) data science platform to offer an instantly responsive user experience. Mode streams query results into the Helix data engine to reduce database load, provide quick responsiveness, and give users the ability to visually navigate as much as 10 gigabytes of data.
Mode Dashboard Features
Powered by SQL, Python, and R, Mode dashboards display automatically-refreshing metrics without the need for proprietary tools. Customize your dashboards the way you want, with on-brand reporting and interactive features to facilitate collaboration with others across your organization.
Mode Native Python and R Notebooks
Mode features native Python and R Notebooks. This allows you to query in SQL, send the results to a dataframe in R or pandas, and conveniently access them in the platform's native Notebook. At the same time, a sidebar provides tips, shortcuts, and documentation to help you get the most of these features.