- 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
- Pipeline Objects
-
Transformations
- 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
-
Schema Mapper
- Using Schema Mapper
- Mapping Statuses
- Auto Mapping Event Types
- Mapping a Source Event Type Field with a Destination Table Column
- 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
- Troubleshooting Failed Events in a Pipeline
- Mismatch in Events Count in Source and Destination
-
Pipeline FAQs
- Does creation of Pipeline incur cost?
- Why are my new Pipelines in trial?
- Can multiple Sources connect to one Destination?
- Is my data stored after I delete a Pipeline?
- What happens if I re-create a deleted Pipeline?
- When should I pause vs delete my Pipeline?
- Why am I getting warnings while adding Pipelines?
- Why is there a delay in my Pipeline?
- Can I delete skipped objects in a Pipeline?
- How do I disable Auto Mapping?
- How do I change the ingestion frequency for tables?
- Can I change the Destination post-Pipeline creation?
- How do I change the query mode for my Pipelines?
- How does changing the query mode affect data ingestion?
- Why is my billable Events high with Delta Timestamp mode?
- How does the timing of Change Schedule work?
- 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 the 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?
- How can I transfer Excel files using Hevo?
- How can I load an XML file from an S3 folder?
- 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
- GitHub
-
Streaming
- Android SDK
- Kafka
-
REST API
- Writing JSONPath Expressions
-
REST API FAQs
- Why does my REST API token keep changing?
- Is it possible to use a Bearer Authorization token?
- 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
- 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
- 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?
- What is a free Source?
- Why am I unable to modify my OAuth Source connection settings?
- 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?
- Which file formats are supported by file storage-based Sources?
- Why are my selected Source objects not visible in the Schema Mapper?
- How does the Merge Table feature work?
- Destinations
- Familiarizing with the Destinations UI
- Databases
-
Data Warehouses
- Amazon Redshift
- Databricks
- Firebolt
- Hevo Managed Google BigQuery
- Google BigQuery
- Snowflake
-
Destination FAQs
- Can I create a Destination through API?
- 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?
- When will I be charged for my subscription?
- 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?
- What is a free Source?
- 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?
- What are the payment methods available in Hevo?
- Can you explain the pricing plans in Hevo?
- Where do I get invoices for payments?
- Is my billing information removed upon account deletion?
- Account Suspension and Restoration
- Account Management FAQs
- Activate
- Glossary
- Release Notes
- 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
Data Pipelines
Organizations are aspiring to become data driven. Intuitive decision-making is getting replaced by fact-based decision-making that is backed by data. Often, enterprises find it difficult to implement this data-driven decision-making as most of the workforce performing this task could be non-technical. Integrating data to make it accessible for analytics is a highly technical process. This is what data pipelines enable organizations to do; making data analysis easier for business analysts.
Pipelines in Hevo
A data pipeline, or Pipeline in Hevo is a no-code data processing framework that loads data from any Source such as a database, SaaS application, or file into a Destination database or data warehouse. For example, you may load data from your Facebook Ads account to a Google BigQuery data warehouse for analysis.
With just a few clicks you can have analysis-ready data at your finger tip, without any data loss. You can even view samples of incoming data in real-time as it loads from your Source into your Destination.
Pipeline Components
The following form the key components of a Pipeline:
-
Source: A Source is a database, an API endpoint or a file storage that holds the data that you want to analyze. Hevo integrates with over 100+ Sources. Read Sources.
-
Transformations: Python or UI-based Transformations are useful when you want to clean, enrich, or transform your data before loading it to your Destination. Read Transformations.
-
Schema Mapper: The Schema Mapper lets you map your Source schemas to tables in your Destination. Read Schema Mapper.
-
Destination: The Destination is the data warehouse or database where the data collected from Sources is stored. Read Destinations.
In a Pipeline, one Source maps to one Destination only. However, the same database or data warehouse may be configured as a Destination for multiple Sources.
Benefits of Creating Pipeline Using Hevo
Following are some of the benefits of creating Pipeline using Hevo:
-
Near Real-Time Data Transfer: Hevo provides real-time data migration, so you can perform data analysis anytime as per your business need.
-
100% Complete and Accurate Data Transfer: Your Hevo Pipeline ensures reliable data transfer with zero data loss.
-
Scalable Infrastructure: Seamless integrations with Sources can help you scale your data infrastructure as per your business need.
-
Live Monitoring: Options to view the data and its movement along different stages of the Pipeline allows you to monitor the progress of your data replication. Read Data Ingestion Statuses and Viewing Pipeline Progress.
-
Connectors: Hevo supports integration with various Destinations including Google BigQuery, Amazon Redshift, Snowflake data warehouses; Amazon S3 data lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few. Read Sources. Read Sources.
-
Transformations: Pipeline created in Hevo provides preload transformations. You can also use the Python or the drag and drop transformations like Date and Control Functions, JSON and Event Manipulation to perform your own transformations. These can be configured and tested before putting them to use. Read Transformations. Transformations.
-
Schema Management: Pipeline created in Hevo takes away the tedious task of mapping and managing the Destination schema and automatically schema management & automatically detects the schema of incoming data and maps it to the destination schema. Read Schema Mapper. Read Schema Mapper.
Revision History
Refer to the following table for the list of key updates made to this page:
Date | Release | Description of Change |
---|---|---|
Mar-21-2022 | NA | New document. |