Introducing Schema Mapper
While ingesting your data from different sources into a central data warehouse, handling data conversions and mapping data to relevant tables and columns can be tricky. Hand coding each mapping for every individual column in each table is the biggest pain point. Additionally, with new sources getting added from time to time or incoming data schema changing – this becomes an ongoing pain point.
Hevo’s Schema Mapping
Hevo automates this tedious task of manually mapping source schema with destination schema. As soon as Hevo extracts the data from the source, it intelligently infers the schema for the incoming data and suggests the schema. You can map this to your destination schema in a single click or override the suggestion as per your need.
Here’s how Schema Mapper can help you:
It is very easy to handle the unstructured and semi-structured data coming into Hevo. Say you have a JSON data in one of the columns in the input source, you can easily convert the nested JSON object into columns and map it to the destination table.
You can also create new data fields based on the incoming fields. This new field can then be mapped and sent to the destination for all incoming data. Let’s say you added an additional field to hold the location details of IPs while transforming the data. You can on the fly add an additional column through the Mapper onto your destination warehouse schema to include this.
Automatic Schema Change Detection
Schema Mapper helps you be prepared for any incoming data schema change through its advanced schema change detection algorithm. Upon change detection at the source or destination, you are notified with a suggested action to be taken to handle this. Mapper also parks these events in the replay queue for future review. Since Mapper lets you create additional columns or fresh tables from with the tool as needed, handling schema changes become effortless.Automatic Schema Detection and Schema Mapping on Hevo
With Automation of Schema mapping, we want to remove all the tedious and painful tasks from the Data Integration. What are your thoughts on the Mapper? Let us know in comments.
No-code Data Pipeline for your Data Warehouse