From Hubspot to Amazon S3: managed, scheduled, no code.
Kondado replicates data from 24 Hubspot pipelines including Contacts, Companies, Deals, Email Events, and Tickets to Amazon S3 on a configurable schedule ranging from every 5 minutes to daily, with no coding required.
Kondado connects Hubspot to Amazon S3, allowing you to replicate your marketing, sales, and customer service data without writing code. Simply connect your Hubspot account, configure your Amazon S3 bucket as the destination, and select which pipelines you want to replicate. The platform automatically handles data extraction and loading on a configurable schedule, ensuring your S3 bucket always contains fresh data for analysis.
Once your Hubspot data lands in Amazon S3, you can query it directly using Amazon Athena, Presto, or Dremio, or load it into BigQuery, PostgreSQL, or Power BI for advanced analytics. This workflow eliminates manual exports and gives your team consistent, analysis-ready datasets for building custom reports and dashboards.
With Kondado, you can replicate the Contacts and Companies pipelines to track customer journey patterns across your Amazon S3 data lake, enabling sophisticated segmentation analysis when combined with other sources. The Deals and Line Items pipelines provide granular sales data perfect for forecasting models and revenue attribution studies using tools like Athena or Presto directly on your S3 storage. Additionally, Email Events and Marketing Emails pipelines let you analyze campaign performance metrics alongside web analytics data, creating comprehensive views of marketing ROI and engagement trends without manual data preparation.
Try out all the features for free for 14 days
Replicated to Amazon S3
Hubspot data available for Amazon S3
Tables Kondado writes into your Amazon S3, on a schedule you control.
24
available pipelines
805
extractable fields
Amazon S3
Destination
Available integrations
Companies
Includes information such as id, name, and properties related to contacts and deals.
Contact lists
Presents contact lists with details on membership and relevant properties.
Contacts
Brings contact information such as id, email, name, and created date.
Contacts: form submissions
Includes form submissions with fields such as conversion id, content type, and form id.
Contacts: lists membership
Displays the association of contacts to specific lists and their properties.
Custom object
Represents custom objects with fields such as id and specific properties.
Deals
Contains information about deals, including id, value, and related contacts.
Email events
Records email events with details on sending, opens, and clicks.
Engagements: Calls
Includes call details such as id, dispositions, and related objects.
Engagements: Emails
Presents email details including id and related objects.
Engagements: Meetings
This table contains information about meetings, including fields such as id, contact_id, company_id, and updated_at.
Engagements: Notes
Notes table includes fields such as id, contact_id, body, and created_at, allowing tracking of relevant information.
Engagements: Tasks
Tasks table presents fields such as id, contact_id, due_date, and status, facilitating activity management.
Feedback Submissions
Feedback submissions table includes fields such as id, contact_id, feedback_type, and created_at, allowing analysis of responses.
Leads
Leads table contains information such as id, email, status, and created_at, essential for managing potential customers.
Line Items
Line items table presents fields such as id, deal_id, product_id, and quantity, useful for tracking sales.
Marketing Emails
Marketing emails table includes fields such as id, subject, sent_date, and open_rate, allowing analysis of campaigns.
Owners
Owners table contains fields such as id, name, email, and created_at, essential for user management.
Payments
Payments table includes fields such as id, amount, payment_date, and status, useful for financial control.
Pipelines
Pipelines table presents fields such as id, name, stage, and created_at, important for sales management.
Products
Table contains information about products, including fields such as id, name, price, and description.
Properties Metadata
Table presents metadata of properties, including fields such as name, type, and description.
Property History
Table logs changes to properties, with fields such as id, created_at, and previous_value.
Tickets
Table includes information about tickets, with fields such as id, status, priority, and created_at.
Sync data automatically — no code, no manual exports.
1
Connect your Hubspot data source
Authorize Kondado to access your Hubspot account using OAuth authentication, selecting the appropriate Hub ID and API scopes for the pipelines you need.
2
Configure Amazon S3 destination
Enter your AWS credentials and specify the S3 bucket name and folder path where Hubspot data should be stored, ensuring the account has write permissions to that location.
3
Select pipelines and schedule
Choose from 24 available pipelines such as Contacts, Deals, or Email Events, then set your preferred replication frequency from every 5 minutes to daily updates.
Try out all the features for free for 14 days
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Why Kondado?
Focus on extracting value from your data and leave the rest to us
Answers about sending Hubspot data to Amazon S3 automatically
What data format does Hubspot data use in Amazon S3?▼
Kondado stores your Hubspot data in Amazon S3 as compressed JSON or Parquet files, organized by pipeline and date partitions for efficient querying. This structure allows you to run high-performance analytics using Amazon Athena, Presto, or Dremio without transforming the data first. You can also load these files into BigQuery or PostgreSQL for additional processing.
How often can I update Hubspot data in my S3 bucket?▼
You can configure replication schedules ranging from every 5 minutes to daily updates, depending on your analytics requirements and API limits. This flexibility lets agencies handle high-frequency campaign monitoring while enterprise teams opt for hourly or daily batches for historical analysis. The schedule applies individually to each pipeline, so you can update Email Events frequently while refreshing Companies data less often.
Can I combine Hubspot data with other sources in Amazon S3?▼
Yes, you can replicate data from multiple sources into the same Amazon S3 bucket to create unified data lakes for cross-platform analysis. Combine Hubspot engagement data with advertising platforms, CRMs, or databases to build comprehensive customer profiles and attribution models. This approach enables you to query joined datasets using SQL in Athena or load everything into BigQuery for visualization in Looker Studio or Power BI.
Which Hubspot pipelines support incremental updates?▼
Pipelines such as Contacts, Companies, Deals, and Email Events support incremental replication, meaning only new or modified records transfer to Amazon S3 during each update cycle. This optimizes API usage and storage costs while keeping your datasets current. Historical pipelines like Property History and Engagements capture full activity logs to maintain complete audit trails.
Do I need to create folders in Amazon S3 before replicating Hubspot data?▼
No, Kondado automatically manages the file structure within your S3 bucket, creating organized paths for each pipeline without manual setup. The platform handles data type mapping and file partitioning, so your data arrives ready for immediate querying. You simply provide write permissions to the designated bucket, and Kondado manages the rest.
Can I send Hubspot data to Amazon S3 and Power BI simultaneously?▼
Yes, Kondado supports multiple destinations, allowing you to replicate Hubspot data to Amazon S3 while also sending it directly to Power BI, BigQuery, or Google Sheets. This enables you to maintain a data lake in S3 for archival and complex analysis while powering immediate dashboards in your preferred visualization tool. Configure separate schedules for each destination to optimize performance and costs.
What happens to historical property changes when replicating to S3?▼
The Property History pipeline captures every change made to contact, company, and deal properties, storing these records as separate files in your Amazon S3 bucket. This allows you to reconstruct historical states and analyze trend evolution over time using SQL queries in Athena or Presto. You can track field-level changes without losing granular context about when values changed and what they were previously.
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