Connect Aha! and Google Cloud Bigtable to Build Intelligent Automations
Choose a Trigger
When this happens...
New Idea User
New Product
Choose an Action
Automatically do this!
- Request a new Action for Google Cloud Bigtable
Enable Integrations or automations with these events of Aha! and Google Cloud Bigtable
Enable Integrations or automations with these events of Aha! and Google Cloud Bigtable
Triggers
New Idea User
Triggers when a new idea user is created in sceduled time.
New Product
Triggers when a new product is created in scheduled time.
Actions
Create a Feature
Creates a new feature in Aha.
Create an Idea User
Create an idea user.
Create a product
Create a product.
Create a release
Create a release.
Create a Note
Create a note.
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Frequently Asked Questions
To start, connect both your Aha! and Google Cloud Bigtable accounts to viaSocket. Once connected, you can set up a workflow where an event in Aha! triggers actions in Google Cloud Bigtable (or vice versa).
Absolutely. You can customize how Aha! data is recorded in Google Cloud Bigtable. This includes choosing which data fields go into which fields of Google Cloud Bigtable, setting up custom formats, and filtering out unwanted information.
The data sync between Aha! and Google Cloud Bigtable 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 Aha! and Google Cloud Bigtable. 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.
About Aha!
Aha! is a comprehensive product management software that helps teams set strategy, prioritize features, create visual roadmaps, and track progress.
Learn MoreAbout Google Cloud Bigtable
Google Cloud Bigtable is a fully managed, scalable NoSQL database service designed for large analytical and operational workloads. It is ideal for applications that require high throughput and low latency, such as IoT, user analytics, and financial data analysis.
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