- About Hevo
- Hevo Features
- Hevo System Architecture
- Core Concepts
- Free Trials
- Regulatory Compliance
- Hevo Support
- General FAQs
- Getting Started
- Creating an Account in Hevo
- Connection Options
- Familiarizing with the UI
- Creating your First Pipeline
- Data Loss Prevention and Recovery
- Data Ingestion
- Types of Data Synchronization
- Ingestion Modes and Query Modes for Database Sources
- Ingestion and Loading Frequency
- Ingestion Frequency and Data Synchronization
- Data Ingestion Statuses
- Deferred Data Ingestion
- Handling of Primary Keys
- Handling of Updates
- Handling of Deletes
- Hevo-generated Metadata
- 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
- Data Flow in a Pipeline
- Familiarizing with the Pipelines UI
Working with Pipelines
- Best Practices for Creating Database Pipelines
- Creating a Pipeline
- Connectivity Check for RDBMS Sources
- Scheduling a Pipeline
- Modifying a Pipeline
- Prioritizing a Pipeline
- Viewing Pipeline Progress
- Pausing and Deleting a Pipeline
- Log-based Pipelines
- Troubleshooting Data Replication Errors
- Managing Objects in Pipelines
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
- 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
- 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
- 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
- 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
- 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?
- How do I include new tables in the Pipeline?
- 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
- Free Sources
Databases and File Systems
- Data Warehouses
- Connecting to a Local Database
- Amazon DocumentDB
- Amazon DynamoDB
- Generic MongoDB
- MongoDB Atlas
- Support for Multiple Data Types for the _id Field
- Example - Merge Collections Feature
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
- Amazon RDS SQL Server
- Azure SQL Server
- Google Cloud SQL Server
- Generic SQL Server
- Troubleshooting SQL Server
- SQL Server FAQs
- Amazon Aurora MySQL
- Amazon RDS MySQL
- Azure MySQL
- Google Cloud MySQL
- Generic MySQL
- MariaDB 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
- Amazon Aurora PostgreSQL
- Amazon RDS PostgreSQL
- Azure PostgreSQL
- Google Cloud PostgreSQL
- Generic PostgreSQL
- Heroku 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
- Amazon S3
- Azure Blob Storage
- FTP / SFTP
- Google Cloud Storage (GCS)
- Google Drive
- Google Sheets
- Android SDK
- 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?
- Jira Cloud
- Finance & Accounting Analytics
- Apple Search Ads
- Facebook Ads
- Facebook Page Insights
- Firebase Analytics
- Google Campaign Manager
- Google Ads
- Google Analytics
- Google Analytics 4
- Google Analytics 360
- Google Play Console
- Google Search Console
- Instagram Business
- LinkedIn Ads
- Microsoft Advertising
- Pinterest Ads
- SendGrid Webhook
- Salesforce Marketing Cloud
- Snapchat Ads
- TikTok Ads
- Twitter Ads
- YouTube Analytics
- Product Analytics
Sales & Support Analytics
- Help Scout
- Hub Planner
- Toggl Track
- 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?
- Familiarizing with the Destinations UI
- Amazon Aurora MySQL
- SQL Server
- Connecting to a Local Database
- Limitations of using MySQL as a Destination
- Structure of Data in the Amazon Redshift Data Warehouse
- Loading Data to an Amazon Redshift Data Warehouse
- Troubleshooting Amazon Redshift Destination
- Amazon Redshift FAQs
- Azure Synapse Analytics
- Clustering in BigQuery
- Partitioning in BigQuery
- Loading Data to a Google BigQuery Data Warehouse
- Near Real-time Data Loading using Streaming
- Troubleshooting Google BigQuery
- Google BigQuery FAQs
- Hevo Managed Google BigQuery
- Structure of Data in the Snowflake Data Warehouse
- Loading Data to a Snowflake Data Warehouse
- Troubleshooting Snowflake
- Snowflake FAQs
- Amazon Redshift
- 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?
- dbt™ Models
- Familiarizing with the Models UI
- Types of Models
- Key Features
- Working with SQL Models
- Previewing a Model
- Viewing the Query History
- Legacy Models
- Models FAQs
- Account Management
- Personal Settings
- Team Settings
- Pricing Plans
- Time-based Events Buffer
- Setting up Pricing Plans, Billing, and Payments
- On-Demand Purchases
- Billing Alerts
- Viewing Billing History
- Billing Notifications
- Can I try Hevo for free?
- 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 Concepts
- Familiarizing with the Activate UI
- Working with Activate
- Activate Warehouses
- Activate Targets
- 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
Google BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over huge sizes of data. Hevo allows users to migrate multiple datasets and tables within a BigQuery project to any other data warehouse of their choice.
Organization of data in BigQuery
Google BigQuery uses Projects to store data. An organization can have multiple projects associated with it. However, each Pipeline can be associated with only one BigQuery project.
Within a project, the data tables are organized into units called datasets.
Hevo needs permission to access your data in BigQuery as well as GCS. The files written to GCS are deleted as soon as they are moved to the next stage in the Pipeline. These permissions are assigned to the account you use to authenticate Hevo on BigQuery. Read Google Account Authentication Methods for more information.
Data Replication Strategy
Hevo adopts one of the following strategies to replicate data from your Google BigQuery Source:
Direct Query: Hevo adopts this replication strategy to ingest data from non-partitioned tables in your dataset. This is also used with partitioned tables if a GCS bucket is not specified at the time of creating the Pipeline. In this strategy, Hevo first scans the selected objects (tables), and then reads data from them. To identify the incremental data, Hevo scans the entire table to find the difference between the new and existing data.
GCS Export: Hevo adopts this replication strategy to ingest data from partitioned tables in your dataset if you have specified a GCS bucket at the time of creating the Pipeline. In this strategy, Hevo first ingests data from the partitions and then temporarily exports it into a bucket in your Google Cloud Storage. From there, the data is loaded into the Destination. An offset is maintained to help identify the latest partition, and data from that partition is ingested as incremental data.
Read Introduction to partitioned tables to understand how partitioning affects the data processing performance and costs in Google BigQuery.
Access to a BigQuery project with one or more datasets containing at least one table.
Access to an existing GCS bucket in the BigQuery location as your datasets. You can specify this when creating your Pipeline to enable Hevo to use the GCS Export strategy for partitioned tables.
You are assigned the Team Administrator, Team Collaborator, or Pipeline Administrator role in Hevo to create the Pipeline.
Note: You can select only one project per Pipeline.
Configuring Google BigQuery as a Source
Perform the following steps to configure BigQuery as the Source in your Pipeline:
Click PIPELINES in the Navigation Bar.
Click + CREATE in the Pipelines List View.
In the Select Source Type page, select Google BigQuery.
In the Configure your BigQuery Account page, connect to your BigQuery data warehouse using one of the following ways:
Note: You cannot switch from a service account to a user account and vice-versa once you create the Pipeline.
To connect using a User Account:
Click + ADD BIGQUERY ACCOUNT.
Sign in to your account, and click Allow to authorize Hevo to access your data.
To connect using a Service Account,
Select the Service Account option.
Attach the Service Account Key JSON file that you created in Google Cloud Platform (GCP).
Click CONFIGURE BIGQUERY ACCOUNT.
In the Configure your BigQuery Source page, specify the following:
Pipeline Name: A unique name for your Pipeline, not exceeding 255 characters.
Project ID: The project ID for which you want to create the Pipeline.
GCS Bucket (Optional): The name of an existing container in your Google Cloud Storage, into which Hevo exports the ingested data before loading it to the Destination.
Select Dataset ID: Select one or more datasets that contain the data tables. You can select the tables you want to replicate from these datasets in subsequent Pipeline configuration steps.
Include New Tables in the Pipeline: If enabled, Hevo automatically ingests data from tables created after the Pipeline has been built. If disabled, the new tables are listed in the Pipeline Detailed View in Skipped state, and you can manually include the ones you want and load their historical data.
You can change this setting later.
Click TEST & CONTINUE.
Proceed to configuring the data ingestion and setting up the Destination.
Selecting Source Objects for Ingestion
By default, all datasets and the tables within these are selected to be replicated. You can change this setting by selecting specific dataset IDs while configuring your Pipeline. In the subsequent configuration pages, you can select the tables (Source objects) of these datasets that you want to ingest.
You can edit these settings at a later time through the Overview tab of the Pipeline Detailed View page as follows:
Note: Re-run the Pipeline for changes to take effect.
To include/skip a table:
Click the Kebab menu of the object
Click Include Object or Skip Object. Excluded tables have the status, SKIPPED.
To add or remove a dataset:
Click the Settings icon next to the Source name.
Click the Edit icon in the pop-up dialog and re-configure the integration.
Datasets that do not have tables are not included in the Pipeline.
Hevo automatically loads the historical data for the newly added tables. If you are creating a table in a dataset that is included in the Pipeline, Hevo automatically starts ingesting its Events in the Pipeline. If you exclude a table, its status is updated to SKIPPED in the Pipeline Overview section.
|Default Pipeline Frequency||Minimum Pipeline Frequency||Maximum Pipeline Frequency||Custom Frequency Range (Hrs)|
|3 Hrs||15 Mins||24 Hrs||1-24|
Note: You must set the custom frequency in hours as an integer value. For example, 1, 2, 3 but not 1.5 or 1.75.
Historical Data: In the first run of the Pipeline, Hevo ingests data from tables in the selected datasets in your BigQuery project in the following manner:
For partitioned tables: If you specified a GCS bucket at the time of creating the Pipeline, Hevo ingests all the data up to the latest table partition; else, Hevo ingests the entire existing data.
For non-partitioned tables: Hevo ingests the entire data existing in your table. This behavior is unchanged even if a GCS bucket is specified at the time of creating the Pipeline.
Incremental Data: Once the historical load is complete, Hevo synchronizes data with your Destination as per the ingestion frequency in the following manner:
For partitioned tables: Hevo performs a full load to ingest all the data available in the latest partition of your table.
For non-partitioned tables: Hevo synchronizes all new and updated records.
Hevo does not track deletes or support Change Data Capture for BigQuery.
Schema and Primary Keys
The Schema is derived based on the data in your BigQuery Source tables.
Read the detailed Hevo documentation for the following related topics:
- The Cloud Storage bucket into which Hevo temporarily exports your ingested data must exist in the BigQuery location as your dataset with the exception of the datasets in the US multi-region. Read Location considerations for further details on the requirements.
Updates in the BigQuery Source data are appended as new rows in the Destination. The existing rows are not modified. Therefore, both old and new entries exist in the Destination.
Deleted data is not marked or removed in the Destination.
Hevo requests access to your data in Cloud Storage even if you do not specify a GCS bucket while configuring the Pipeline.
Refer to the following table for the list of key updates made to this page:
|Date||Release||Description of Change|
|Mar-09-2023||NA||Updated section, Configuring Google BigQuery as a Source to add a note about switching your authentication method post-Pipeline creation.|
|Dec-07-2022||2.03||Updated section, Configuring Google BigQuery as a Source to add information about support for service accounts.|
|Sep-13-2022||1.97||- Added the Data Replication Strategy subsection in the overview text to explain the different data ingestion strategies,
- Added the Source Considerations section,
- Updated the Configuring your Google BigQuery Source section to add the GCS bucket field description,
- Updated the Limitations section to inform about Hevo requesting access to data in GCS,
- Modified the content for historical and incremental data in the Data Replication section to describe the impact of providing a GCS bucket on data ingestion.
|Mar-22-2022||NA||Updated information regarding Historical Data in the Data Replication section to remove the mention of historical sync duration.|
|Jul-12-2021||1.67||Added the field Include New Tables in the Pipeline under Source configuration settings.|