- 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
- Data Spike Alerts
- Name Sanitization
- Table and Column Name Compression
- Parsing Nested JSON Fields in Events
- Pipelines
- Data Flow in a Pipeline
- Familiarizing with the Pipelines UI
- Working with Pipelines
- Managing Objects in Pipelines
-
Transformations
-
Python Code-Based Transformations
- Supported Python Modules and Functions
-
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
- Convert date string to required format
- Convert date to required format
- Convert datetime string to required format
- Convert epoch time to a date
- Convert epoch time to a datetime
- Convert epoch to required format
- Convert epoch to a time
- Get time difference
- Parse date string to date
- Parse date string to datetime format
- Parse date string to time
- Utils
- Examples of Python Code-based Transformations
-
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
- File Log
- Troubleshooting Failed Events in a Pipeline
- Mismatch in Events Count in Source and Destination
- Activity Log
-
Pipeline FAQs
- Does creation of Pipeline incur cost?
- Why are my new Pipelines in trial?
- Can multiple Sources connect to one Destination?
- What happens if I re-create a deleted Pipeline?
- Why is there a delay in my Pipeline?
- Can I delete skipped objects in a Pipeline?
- Can I change the Destination post-Pipeline creation?
- How does changing the query mode affect data ingestion?
- Why is my billable Events high with Delta Timestamp mode?
- Can I drop multiple Destination tables in a Pipeline at once?
- How does Run Now affect scheduled ingestion frequency?
- Will pausing some objects increase the ingestion speed?
- Can I sort Event Types listed in the Schema Mapper?
- Can I see the historical load progress?
- Why is my Historical Load Progress still at 0%?
- Why is historical data not getting ingested?
- How do I restart the historical load for all the objects?
- How do I set a field as a primary key?
- How can I load only filtered Events to the Destination?
- How do I ensure that records are loaded only once?
- Why do the Source and the Destination events count differ?
- Events Usage
- Sources
- Free Sources
-
Databases and File Systems
- Data Warehouses
-
Databases
- Connecting to a Local Database
- Amazon DocumentDB
- Amazon DynamoDB
- Elasticsearch
-
MongoDB
- Generic MongoDB
- MongoDB Atlas
- Support for Multiple Data Types for the _id Field
- Example - Merge Collections Feature
-
Troubleshooting MongoDB
-
Errors During Pipeline Creation
- Error 1001 - Incorrect credentials
- Error 1005 - Connection timeout
- Error 1006 - Invalid database hostname
- Error 1007 - SSH connection failed
- Error 1008 - Database unreachable
- Error 1011 - Insufficient access
- Error 1028 - Primary/Master host needed for OpLog
- Error 1029 - Version not supported for Change Streams
- SSL 1009 - SSL Connection Failure
- Troubleshooting MongoDB Change Streams Connection
- Troubleshooting MongoDB OpLog Connection
-
Errors During Pipeline Creation
- SQL Server
-
MySQL
- Amazon Aurora MySQL
- Amazon RDS MySQL
- Azure MySQL
- Google Cloud MySQL
- Generic MySQL
- MariaDB MySQL
-
Troubleshooting MySQL
-
Errors During Pipeline Creation
- Error 1003 - Connection to host failed
- Error 1006 - Connection to host failed
- Error 1007 - SSH connection failed
- Error 1011 - Access denied
- Error 1012 - Replication access denied
- Error 1017 - Connection to host failed
- Error 1026 - Failed to connect to database
- Error 1027 - Unsupported BinLog format
- Failed to determine binlog filename/position
- Schema 'xyz' is not tracked via bin logs
- Errors Post-Pipeline Creation
-
Errors During Pipeline Creation
- MySQL FAQs
- Oracle
-
PostgreSQL
- Amazon Aurora PostgreSQL
- Amazon RDS PostgreSQL
- Azure PostgreSQL
- Google Cloud PostgreSQL
- Generic PostgreSQL
- Heroku PostgreSQL
-
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
- PostgreSQL FAQs
- Troubleshooting Database Sources
- File Storage
-
Engineering Analytics
- Apify
- Asana
- Buildkite
- GitHub
-
Streaming
- Android SDK
- Kafka
-
REST API
- Writing JSONPath Expressions
-
REST API FAQs
- Why does my REST API token keep changing?
- Can I use a bearer authorization token for authentication?
- Does Hevo’s REST API support API chaining?
- What is the maximum payload size returned by a REST API?
- How do I split an Event into multiple Event Types?
- How do I split multiple values in a key into separate Events?
- Webhook
- GitLab
- Jira Cloud
- Opsgenie
- PagerDuty
- Pingdom
- Trello
- Finance & Accounting Analytics
-
Marketing Analytics
- ActiveCampaign
- AdRoll
- Apple Search Ads
- AppsFlyer
- CleverTap
- Criteo
- Drip
- Facebook Ads
- Facebook Page Insights
- Firebase Analytics
- Freshsales
- Google Campaign Manager
- Google Ads
- Google Analytics
- Google Analytics 4
- Google Analytics 360
- Google Play Console
- Google Search Console
- HubSpot
- Instagram Business
- Klaviyo
- Lemlist
- LinkedIn Ads
- Mailchimp
- Mailshake
- Marketo
- Microsoft Advertising
- Onfleet
- Outbrain
- Pardot
- Pinterest Ads
- Pipedrive
- Recharge
- Segment
- SendGrid Webhook
- SendGrid
- Salesforce Marketing Cloud
- Snapchat Ads
- SurveyMonkey
- Taboola
- TikTok Ads
- Twitter Ads
- Typeform
- YouTube Analytics
- Product Analytics
- Sales & Support Analytics
-
Source FAQs
- From how far back can the Pipeline ingest data?
- Can I connect to a Source not listed in Hevo?
- Can I connect a local database as a Source?
- How can I push data to Hevo API?
- How do I connect a CSV file as a Source?
- Why are my selected Source objects not visible in the Schema Mapper?
- How can I transfer Excel files using Hevo?
- How does the Merge Table feature work?
- Destinations
- Familiarizing with the Destinations UI
- Databases
-
Data Warehouses
- Amazon Redshift
- Azure Synapse Analytics
- Databricks
- Firebolt
- Google BigQuery
- Hevo Managed Google BigQuery
- Snowflake
-
Destination FAQs
- Can I move data between SaaS applications using Hevo?
- Can I change the primary key in my Destination table?
- How do I change the data type of table columns?
- Can I change the Destination table name after creating the Pipeline?
- How can I change or delete the Destination table prefix?
- How do I resolve duplicate records in the Destination table?
- How do I enable or disable deduplication of records?
- Why does my Destination have deleted Source records?
- How do I filter deleted Events from the Destination?
- Does a data load regenerate deleted Hevo metadata columns?
- Can I load data to a specific Destination table?
- How do I filter out specific fields before loading data?
- How do I sort the data in the Destination?
- Transform
- Alerts
- Account Management
- Personal Settings
- Team Settings
-
Billing
- Pricing Plans
- Time-based Events Buffer
- Setting up Pricing Plans, Billing, and Payments
- On-Demand Purchases
- Billing Alerts
- Viewing Billing History
- Billing Notifications
-
Billing FAQs
- Can I get a plan apart from the Starter plan?
- Are free trial Events charged once I purchase a plan?
- For how long can I stay on the Free plan?
- How can I upgrade my plan?
- Is there a discount for non-profit organizations?
- Can I seek a refund of my payment?
- Do ingested Events count towards billing?
- Will Pipeline get paused if I exceed the Events quota?
- Will the initial load of data be free?
- Does the Hevo plan support multiple Destinations?
- Do rows loaded through Models count in my usage?
- Is Hevo subscription environment-specific?
- Can I pause billing if I have no active Pipelines?
- Can you explain the pricing plans in Hevo?
- Where do I get invoices for payments?
- Account Suspension and Restoration
- Account Management FAQs
- Activate
- Glossary
- Release Notes
- Release Version 2.13
- Release Version 2.12
- Release Version 2.11
- Release Version 2.10
- Release Version 2.09
- Release Version 2.08
- Release Version 2.07
- Release Version 2.06
- Release Version 2.05
- Release Version 2.04
- Release Version 2.03
- Release Version 2.02
- Release Version 2.01
- Release Version 2.00
- Release Version 1.99
- Release Version 1.98
- Release Version 1.97
- Release Version 1.96
- Release Version 1.95
- 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
Conversion Window and Pipeline Frequency in Ad-based Sources
On This Page
Ad-based Sources such as Apple Search Ads, Facebook Ads, Google Ads, Microsoft Ads and Twitter Ads use the concept of conversion or attribution. A conversion is the process of a user interacting with an ad and subsequent purchase or sign-up for the related product or service. When an ad-based Source links this purchase to the previous ad interaction of the user, it is called attribution. The time period between this conversion or attribution is called a conversion or attribution window. The data about these interactions is called insights.
For these Sources, Hevo refreshes the data in the Destination to capture the attribution details as per the conversion window you specify. This data is the key component that affects your Events quota consumption. The number of Events that are ingested during each data refresh and the number of refresh cycles that occur depend on the conversion window and Pipeline frequency, respectively.
In order to optimize your Events usage and the resulting costs, Hevo recommends a combination of the following methods:
1. Reduce the Pipeline Frequency
The data refresh task occurs along with every Pipeline run. Therefore, a high Pipeline frequency leads to a higher data refresh frequency and hence, higher quota consumption, while a lower Pipeline frequency leads to a lower data refresh frequency and hence, lesser quota consumption.
Example:
Let us suppose you have an ad account that receives 1K insights every day.
Conversion window = 30 days.
Scenario 1:
Pipeline frequency = 1 Hour.
Number of refresher Events ingested every one hour = 30K (1K * 30 days).
Total number of Events ingested in one day = 720K (24 * 30K).
Scenario 2:
Pipeline frequency = 12 Hours.
Number of refresher Events ingested every 12 hours = 30K (1K * 30 days).
Total number of Events ingested in one day = 60K (2 * 30K).
So, after one day:
Events ingested and loaded in Scenario 1 = 720K.
Events ingested and loaded in Scenario 2 = 60K.
Therefore, lower Pipeline frequency can help you ingest all the attribution Events with much lower quota consumption.
Read Pipeline Frequency and Events Quota Consumption to know more about the effect of Pipeline frequency on your quota consumption.
2. Keep the Conversion Window in Sync with the Source
When you set up your ad-based Source, you can specify the time window you will use to track conversions. Many ad-based Sources provide you with conversion windows to select from. Hevo recommends that you select or specify the same duration while setting up your Pipeline in Hevo.
Note: The conversion window can also be modified post-Pipeline creation for some Sources.
Specifying a lower window size than the one configured in your respective Source account can lead to loss of data, whereas a higher window size can lead to higher quota consumption.
Example:
Let us suppose while setting up your Facebook Ads account, you selected a conversion window of 28 days.
Scenario 1:
You select a conversion window of 21 days while setting up your Pipeline. This will lead to loss of data, as the refresh data will be ingested for the past 21 days instead of 28 days.
Scenario 2:
You select a conversion window of 35 days while setting up your Pipeline. This will lead to higher data ingestion, as the refresh data will be ingested for 35 days instead of 28 days.
Scenario 3:
You select the conversion window of 28 days while setting up your Pipeline. This will lead to optimal data ingestion, as the refresh data will be ingested for that specified duration as mentioned in your Source account.
Therefore, you must select the conversion window mentioned in your Source account to optimize your Events quota consumption.
See Also
Revision History
Refer to the following table for the list of key updates made to this page:
Date | Release | Description of Change |
---|---|---|
Feb-21-2022 | NA | New document. |