Connect Elasticsearch and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Elasticsearch
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Elasticsearch and TimescaleDB
Enable Integrations or automations with these events of Elasticsearch and TimescaleDB
Actions
Create Document
Creates a document in Elasticsearch using the index name specified inside the document.
Bulk Add Documents to an index
add many documents in one go
Delete Index
delete an index
Create Index
create an index
Fuzzy Search
fuzzy search
Update Document
update a doc
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Frequently Asked Questions
To start, connect both your Elasticsearch and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Elasticsearch triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Elasticsearch 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 Elasticsearch 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 Elasticsearch 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 Elasticsearch
Elasticsearch is a powerful, open-source search and analytics engine built on Apache Lucene. It is designed for horizontal scalability, real-time search, and support for multi-tenant environments.
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
