- Introduction
- Getting Started
- Data Ingestion
- Data Loading
- Loading Data in a Database Destination
- Loading Data to a Data Warehouse
- Optimizing Data Loading for a Destination Warehouse
- Manually Triggering the Loading of Events
- Scheduling Data Load for a Destination
- Loading Events in Batches
- Data Loading Statuses
- Name Sanitization
- Table and Column Name Compression
- Parsing Nested JSON Fields in Events
- Pipelines
- Data Flow in a Pipeline
- Ingestion Modes
- Familiarizing with the Pipelines UI
- Pipeline Objects
- Working with Pipelines
- Transformations
-
Schema Mapper
- Using Schema Mapper
- Mapping Statuses
- Auto Mapping Event Types
- Mapping a Source Event Type with a Destination Table
- Mapping a Source Event Type Field with a Destination Table Column
- Schema Mapper Actions
- Fixing Unmapped Fields
- Resolving Incompatible Schema Mappings
- Resizing String Columns in the Destination
- Schema Mapper Compatibility Table
- Failed Events in a Pipeline
- Pipeline FAQs
- Events Usage
- Sources
- Free Sources
- Analytics
- Collaboration
- CRM
- Data Warehouses
- Databases
- E-Commerce
- File Storage
- Finance & Accounting
-
Marketing
- AdRoll
- Apple Search Ads
- AppsFlyer
- Criteo
- Delighted
- Facebook Ads
- Facebook Page Insights
- Front
- Google Ads
- Google Campaign Manager
- Google Play Console
- Google Search Console
- HubSpot
- Instagram Business
- Klaviyo
- LinkedIn Ads
- Mailchimp
- Marketo
- Microsoft Advertising
- Outbrain
- Pardot
- Pinterest Ads
- Segment
- SendGrid
- SendGrid Webhook
- Salesforce Marketing Cloud
- Snapchat Ads
- Taboola
- Twilio
- TikTok Ads
- Twitter Ads
- Typeform
- Streaming
- Source FAQs
- Destinations
- Transform
- Activate
- Alerts
- Account Management
- Troubleshooting
-
Troubleshooting Sources
- Troubleshooting Amazon DynamoDB
- Troubleshooting FTP/SFTP
- Troubleshooting MongoDB
- Troubleshooting MS SQL
- Troubleshooting MySQL
- Troubleshooting Oracle
-
Troubleshooting PostgreSQL
-
Errors during Pipeline creation
- Error 1003 - Authentication failure
- Error 1006 - Connection settings errors
- Error 1011 - Access role issue for logical replication
- Error 1012 - Access role issue for logical replication
- Error 1014 - Database does not exist
- Error 1017 - Connection settings errors
- Error 1023 - No pg_hba.conf entry
- Error 1024 - Number of requested standby connections
- Errors Post-Pipeline Creation
-
Errors during Pipeline creation
- Troubleshooting Salesforce
- Troubleshooting Destinations
-
Troubleshooting Sources
- Glossary
- Release Notes
- Release Version 1.91
- Release Version 1.90
- Release Version 1.89
- Release Version 1.88
- Release Version 1.87
- Release Version 1.86
- Release Version 1.84 & 1.85
- Release Version 1.83
- Release Version 1.82
- Release Version 1.81
- Release Version 1.80 (Jan-24-2022)
- Release Version 1.79 (Jan-03-2022)
- Release Version 1.78 (Dec-20-2021)
- Release Version 1.77 (Dec-06-2021)
- Release Version 1.76 (Nov-22-2021)
- Release Version 1.75 (Nov-09-2021)
- Release Version 1.74 (Oct-25-2021)
- Release Version 1.73 (Oct-04-2021)
- Release Version 1.72 (Sep-20-2021)
- Release Version 1.71 (Sep-09-2021)
- Release Version 1.70 (Aug-23-2021)
- Release Version 1.69 (Aug-09-2021)
- Release Version 1.68 (Jul-26-2021)
- Release Version 1.67 (Jul-12-2021)
- Release Version 1.66 (Jun-28-2021)
- Release Version 1.65 (Jun-14-2021)
- Release Version 1.64 (Jun-01-2021)
- Release Version 1.63 (May-19-2021)
- Release Version 1.62 (May-05-2021)
- Release Version 1.61 (Apr-20-2021)
- Release Version 1.60 (Apr-06-2021)
- Release Version 1.59 (Mar-23-2021)
- Release Version 1.58 (Mar-09-2021)
- Release Version 1.57 (Feb-22-2021)
- Release Version 1.56 (Feb-09-2021)
- Release Version 1.55 (Jan-25-2021)
- Release Version 1.54 (Jan-12-2021)
- Release Version 1.53 (Dec-22-2020)
- Release Version 1.52 (Dec-03-2020)
- Release Version 1.51 (Nov-10-2020)
- Release Version 1.50 (Oct-19-2020)
- Release Version 1.49 (Sep-28-2020)
- Release Version 1.48 (Sep-01-2020)
- Release Version 1.47 (Aug-06-2020)
- Release Version 1.46 (Jul-21-2020)
- Release Version 1.45 (Jul-02-2020)
- Release Version 1.44 (Jun-11-2020)
- Release Version 1.43 (May-15-2020)
- Release Version 1.42 (Apr-30-2020)
- Release Version 1.41 (Apr-2020)
- Release Version 1.40 (Mar-2020)
- Release Version 1.39 (Feb-2020)
- Release Version 1.38 (Jan-2020)
- Upcoming Features
Clustering in BigQuery
Hevo allows you to create clustered tables in your BigQuery Destination. Clustering helps you narrow down the searches, thereby enhancing the performance of your queries. For example, queries that use filter clauses, or aggregate data. To organize the data into clusters, you must identify the Source fields to be used as cluster keys.
You can specify up to four cluster keys. When you create a clustered table using multiple Source fields, the order in which you specify those fields determines the sorting order of the data in your table.
Enabling Clustering in Hevo
Clustering cannot be enabled on an existing table. Therefore, you need to manually create a new table and define the cluster keys. For this, you must disable Auto Mapping.
Note: If you had previously enabled Auto Mapping and now disable it, the existing mapping is not affected and the Event Type still carries the status MAPPED. You must specifically edit the mapping to apply any changes.
Once cluster keys are defined for a table, you cannot change these. If you want to use a different set of Source fields as cluster keys, you must create a new table.
Note: You can select the same Source field as the cluster key and primary key.
If the cluster key is deleted in the Source or skipped in the Schema Mapper, the clustered table reflects the field values as NULL.
Destination Considerations
- BigQuery does not support clustering on Array and Struct data types.
Revision History
Refer to the following table for the list of key updates made to this page:
Date | Release | Description of Change |
---|---|---|
Apr-06-2021 | 1.60 | New document. |