Elastic Search

The beauty of Elastic Search is it's ability to search data with great flexibility and fuzziness. It is also supports perform semantic search very well.

Hubspot Example

Let's say you have a lot of data in Hubspot and you want to be able to search with more flexibility than Hubspot itself offers.

You can create Ziggy Flows that load data into an Elastic Index that manages very flexible searching, presents a list of records using Slack, ChatterBox or your own UI. From there you can directly jump into Hubspot records using a simple link

Data can be bulk loaded initially and then updated in real-time using WebHooks and another simple Flow whenever related Hubspot data is edited.

Database/Data Warehouse Example

Similarly, you may have your data in a Database or Data Warehouse and want to have flexible searching. Although databases can be tuned with special indexes to perform such tasks (good fuzzy matching can be challenging to configure well) it is very easy to simply load the data you want to search on into an Elastic search.

Ziggy Blocks

To support Elastic, Ziggy has three Blocks for quickly and easily working with Elastic.

AI Search Prompt Block

The AI Search Prompt Block turns any natural language query into a platform specific query.

Elastic Ingest Block

The Elastic Ingest Block handles both Elastic Index configuration and data ingestion. It is used as a part of an ingest Flow.

Elastic Search Block

This is used in a Flow, in conjunction with the AI Search Prompt Bock, to perform the actual Elastic search.