- 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?
- 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
- 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 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
- 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
Jira Cloud
Jira is a project management tool from Atlassian, used for tracking tasks, issues, and bugs, and work progress of teams. Jira has two platforms: Jira Server and Jira Cloud. Each of these has various flavours, such as Jira Service Desk, Jira Software, and Jira Core. Jira Cloud is the cloud platform for Jira.
For creating Pipelines using this Source, Hevo provides you a fully managed BigQuery data warehouse as a possible Destination. This option remains available till the time you set up your first BigQuery Destination irrespective of any other Destinations that you may have. With the managed warehouse, you are only charged the cost that Hevo incurs for your project in Google BigQuery. The invoice is generated at the end of each month and payment is recovered as per the payment instrument you have set up. You can now create your Pipeline and directly start analyzing your Source data. Read Hevo Managed Google BigQuery.
You can replicate data from your Jira Cloud account to the Destination database or data warehouse using Hevo Pipelines.
Prerequisites
-
An Atlassian Jira Cloud instance.
-
READ
permissions on the data to be ingested for the authenticated user. -
You are assigned the Team Administrator, Team Collaborator, or Pipeline Administrator role in Hevo to create the Pipeline.
Creating the API Token
Create an API token from your Atlassian account:
-
Log in to your account.
-
In the API Tokens page, click Create API token.
-
In the dialog that appears, specify a short and easy to remember Label for your token and click Create.
-
Click Copy to copy the token to the clipboard, and paste the token somewhere to save it securely like any other password.
-
Click Close. You can now see the new token in the API Tokens list.
Note: For security reasons it is not possible to view the token after closing the creation dialog. If necessary, you can create a new token.
Configuring Jira Cloud as a Source
Perform the following steps to configure Jira as a Source in your Pipeline:
-
Click PIPELINES in the Navigation Bar.
-
Click + CREATE in the Pipelines List View.
-
In the Select Source Type page, select Jira Cloud.
-
In the Configure your Jira Cloud Source page, specify the following:
-
Pipeline Name: A unique name for your Pipeline.
-
API Token: The API token that you created in your Atlassian account. This authorizes Hevo to read data from your Jira account.
-
User Email: The email ID linked to your Jira account.
-
Site Name: Your Jira site name. Extract this from your dashboard URL. For example, if your dashboard URL is https://mycompany.atlassian.net/jira/dashboards/, your site name is mycompany.
-
Historical Sync Duration: The duration for which the existing data in the Source must be ingested. Default duration: 3 Months.
Note: If you select All Available Data, Hevo ingests all the data available in your Jira Cloud account since January 01, 2000.
-
-
Click TEST & CONTINUE.
-
Proceed to configuring the data ingestion and setting up the Destination.
Data Replication
-
Historical Data:
-
For existing Pipelines: In the first run of the Pipeline, Hevo ingests the data from the beginning of time for all the objects in your Jira Cloud account.
-
For Pipelines created after Release 1.80: You can select the Historical Sync Duration as per your requirement while creating a Pipeline. Default duration: 3 Months.
-
-
Incremental Data: Once the historical load is completed, every subsequent run of the Pipeline fetches the entire data for the objects you select. In case of the
Issue
object though, only the new and modified data is ingested.
Note: From Release 1.86, Hevo ingests only new and updated data for Full Load objects to optimize the quota consumption. This feature is currently available on request only. You need to contact Hevo Support to enable it for your team.
Custom frequency for Full Load objects
Hevo allows you to set the ingestion frequency for Full Load objects separately from the Pipeline ingestion frequency. You can reduce your Events quota consumption by ingesting Full Load objects at a lower frequency without affecting other objects in the Pipeline. Read Query Modes and Events Quota Consumption to know how different query modes affect your Events quota consumption.
You can identify the Full Load objects in the Pipelines Detailed View by the FL tag corresponding to their name. Alternatively, you can view only Full Load objects for your Pipeline by selecting Full Load in the Filter ( ) menu.
Perform the following steps to change the ingestion frequency for Full Load objects:
-
In the Pipelines Detailed View, click the More (
) icon to open the Pipeline’s Action menu, and click Change Schedule.
-
In the Change the Ingestion Schedule pop-up dialog, enable the Change Full Load Object Schedule option.
-
Select the ingestion frequency for the Full Load objects from the options displayed, or you can select Custom, and then enter the ingestion frequency in hours as an integer value.
-
Click SCHEDULE.
The changed schedule is applied immediately.
Schema and Primary Keys
Hevo uses the following schema to upload the records in the Destination:
Data Model
The following is the list of tables (objects) that are created at the Destination when you run the Pipeline.
Table Name | Description |
---|---|
board | A board displays issues from one or more projects, giving you a flexible way of viewing, managing, and reporting on the work in progress. All the boards that the user has permission to view. |
board_issue | IDs of all issues within a given board ID, which the user has permission to view. Note: Epic issues do not belongs to the scrum boards. |
board_project | All the projects, ordered by name, that are associated with a given board ID. If you do not have permission to view the board, no project is loaded. |
component | All components in a project. |
issue | All work-related information relating to a unit of work. This includes the description of the work to be done, its duration, type, assignee, and importance. Note: The custom fields created in an issue have the prefix custom_ in the field name. |
issue_changelog | A paginated list of all change logs for an issue, starting from the oldest. This also includes the meta data related to the changes. |
issue_comment | All comments logged by users for an issue. |
issue_component | The mapping between issues and project components. This is a custom table generated by Hevo to maintain data sanity. |
issue_fix_version | The mapping between issues and project versions. This is a custom table generated by Hevo to maintain data sanity. |
issue_label | All the labels assigned to an issue. |
issue_link | All the relational information between two issues. |
issue_link_type | The definitions of all relations that exist between two issues. Note: The issue linking option must be enabled. |
issue_subtask | All the subtasks related to a particular issue ID. |
issue_type | All issue types, such as bug, task, or story. |
issue_version | The mapping between issues and project versions. This is a custom table generated by Hevo to maintain data sanity |
issue_watcher | The users who are watching the developments for an issue. Note: The Allow users to watch issues option must be enabled. |
issue_worklog | The work logs for an issue, starting from the oldest work log or from the work log started on or after a date and time. Whenever an issue is updated, the work log object related to it is ingested from the beginning of time. Note: Time tracking must be enabled in Jira for this data to be fetched. |
priority | The list of all priorities that are assigned to an issue. |
project | The collection of issues that share a common project lead. |
project_category | The list of all project categories. |
resolution | The list of all resolution values that are assigned to an issue. |
status | The list of all statuses associated with workflows. |
status_category | The list of all status categories. Each status belongs to a status category. |
sprint | All sprints for a given board ID. This only includes sprints that you have permission to view. |
user | The list of all (active and inactive) users. |
version | All versions in a project. |
Additional Information
Read the detailed Hevo documentation for the following related topics:
Source Considerations
Jira’s REST APIs do not support identifying deleted issues due to which Hevo is unable to update the Events for deleted issues and they continue to remain in your Destination table. As a result, you see more Events in the Destination than in the Source.
Limitations
- Only Jira Core and Jira Software are currently supported.
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-20-2023 | NA | Updated section, Configuring Jira Cloud as a Source to update the information about historical sync duration. |
Sep-21-2022 | 1.98 | Added section, Custom frequency for Full Load objects to inform users about the option to change ingestion frequency for Full Load objects. |
Jul-27-2022 | NA | Updated Note in section, Data Replication. |
May-10-2022 | NA | Provided additional detail to extract the site name in the Configuring Jira Cloud as a Source section. |
Apr-11-2022 | 1.86 | - Added a note in section, Data Replication to inform about optimized quota consumption for Full Load objects. - Added the Source Considerations section to reflect Jira’s REST APIs limitation. |
Jan-24-2022 | 1.80 | Added information about configurable historical sync duration in the Data Replication section. |