Pipelines
A Pipeline moves your data from a Source system to a Destination database or data warehouse. It has the following components:
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Source: A Source can be a database, a SaaS-based application (an API endpoint) or a file storage which has the data that you want to analyze. Hevo integrates with a variety of Sources. Read Sources.
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Transformations: Transformations are useful when you want to clean, enrich, or transform your data before loading it to your Destination. Read Transformations.
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Schema Mapper: Schema Mapper helps you map your Source schemas to tables in your Destination. Read Schema Mapper.
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Destination: Destination is a data warehouse or database where the data fetched from a Source is loaded. Read Destinations.
You can connect only one Source and Destination in a Pipeline. However, multiple Pipelines may load to the same Destination.
- Articles in this section
- Data Flow in a Pipeline
- Familiarizing with the Pipelines UI
- Working with Pipelines
- Pipeline Objects
- Transformations
- Python Code-Based Transformations
- Transformation Methods in the Event Class
- Create an Event
- Retrieve the Event Name
- Rename an Event
- Retrieve the Properties of an Event
- Modify the Properties for an Event
- Fetch the Primary Keys of an Event
- Modify the Primary Keys of an Event
- Fetch the Data Type of a Field
- Check if the Field is a String
- Check if the Field is a Number
- Check if the Field is Boolean
- Check if the Field is a Date
- Check if the Field is a Time Value
- Check if the Field is a Timestamp
- TimeUtils
- Utils
- Examples of Python Code-based Transformations
- Transformation Methods in the Event Class
- Drag and Drop Transformations
- Special Keywords
- Transformation Blocks and Properties
- Add a Field
- Change Datetime Field Values
- Change Field Values
- Drop Events
- Drop Fields
- Find & Replace
- Flatten JSON
- Format Date to String
- Format Number to String
- Hash Fields
- If-Else
- Mask Fields
- Modify Text Casing
- Parse Date from String
- Parse JSON from String
- Parse Number from String
- Rename Events
- Rename Fields
- Round-off Decimal Fields
- Split Fields
- Examples of Drag and Drop Transformations
- Effect of Transformations on the Destination Table Structure
- Transformation Reference
- Transformation FAQs
- Python Code-Based Transformations
- Schema Mapper
- Using Schema Mapper
- Mapping Statuses
- Auto Mapping Event Types
- Manually Mapping Event Types
- Modifying Schema Mapping for Event Types
- Schema Mapper Actions
- Fixing Unmapped Fields
- Resolving Incompatible Schema Mappings
- Resizing String Columns in the Destination
- Schema Mapper Compatibility Table
- Limits on the Number of Destination Columns
- Troubleshooting Failed Events in a Pipeline
- Mismatch in Events Count in Source and Destination
- Pipeline FAQs
- Does creation of Pipeline incur cost?
- Why are my new Pipelines in trial?
- Can I connect tables from multiple Pipelines to a common Destination table?
- What happens if I delete a Pipeline and re-create a similar one again?
- Why is there a delay in my Pipeline?
- Can I delete the skipped objects in a Pipeline so they don’t appear in the UI?
- Can I change the Destination of the Pipeline after creating it?
- How does changing the query mode of a Pipeline in table mode affect data ingestion?
- Why is my billable Events count so high even though I selected Delta Timestamp as the query mode?
- Can I drop multiple Destination tables in a Pipeline at once?
- Does triggering ingestion using Run Now affect the scheduled ingestion frequency of the Pipeline?
- Will pausing the ingestion of some objects increase the overall ingestion speed of the Pipeline?
- Can I alphabetically sort Event Types listed in the Schema Mapper?
- How do I include new tables in the Pipeline?
- Can I see the historical load progress for my Pipeline?
- My Historical Load Progress is still at 0%. What does it mean?
- Why is the historical data not getting ingested?
- How do I restart the historical load for all the objects at once?
- How do I set a field as a primary key to avoid duplication?
- How can I load only filtered Events from the Source to the Destination?
- How can I make sure that each record is loaded only once?
- Why is there a mismatch in the count of Events in the Source and the Destination even though I selected the Unique and Incrementing query mode?