Connect IndiaMART and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Lead Created
New Buy Lead
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of IndiaMART and TimescaleDB
Enable Integrations or automations with these events of IndiaMART and TimescaleDB
Triggers
New Lead Created
Runs when new lead is created
New Buy Lead
Runs when a new buy lead is received on IndiaMART.
Actions
Get IndiaMART Leads By Time Range
Get IndiaMART leads between a start and end timestamp in IST.
Get Buy Leads
Retrieve all current buy leads from IndiaMART.
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Frequently Asked Questions
To start, connect both your IndiaMART and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in IndiaMART triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how IndiaMART data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between IndiaMART and TimescaleDB 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 IndiaMART and TimescaleDB. 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 IndiaMART
IndiaMART is a leading online marketplace connecting buyers and suppliers across various industries in India. It provides a platform for businesses to showcase their products and services, facilitating B2B transactions and networking.
Learn MoreAbout TimescaleDB
TimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
Learn More
