- 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
- 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
- ActiveCampaign
- 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
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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 1.93 & 1.94
- Release Version 1.92
- 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
Loading Events in Batches
For some Destinations, Events may be loaded in batches of files to improve the performance. This is specially applicable to data warehouse Destinations such as the following:
-
Amazon Redshift
-
Google BigQuery
-
Snowflake
-
S3
The writes to the warehouses include scanning of the tables for deduplication of the Events, which incurs costs for users. Major cloud-based data warehouses, such as, Amazon Redshift and Google BigQuery recommend loading Events through files in batches. Batches provide much better performance at a much lower cost compared to direct and individual writes to the tables.
Advantages of loading Events in batches
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Batches allow Hevo to load millions of Events in the warehouse without consuming a lot of resource bandwidth.
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Loading in batches is faster at scale than direct inserts.
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Deduplication needs to be done fewer times for batches as compared to individual records.
Disadvantages of loading Events in batches
The batching process understandably introduces some delay in loading the data. The delay usually varies between 5-15 minutes. This means that once an Event is ingested by the Pipeline, and provided it is mapped and does not encounter any other failure, the Event should be visible in the Destination within 5-15 minutes.
In case you have stricter SLAs in terms of data latency, contact Hevo Support.