
The growth of e-commerce and online payment platforms leads to the generation of massive amounts of transaction data. By accessing and analyzing real-time transactional data, you can gain actionable insights for informed decision-making. Stripe is among the leading payment processing platforms that benefit businesses of all sizes. So, moving data from Stripe to Redshift will allow you to harness the full potential of your payment data.
The high-performance analytical capabilities of Redshift can provide a comprehensive understanding of revenue patterns, customer behavior, and market trends. This will lead to improved data-driven decisions and accelerated growth for the business.
There are different methods to ingest data from Stripe to Redshift. But, before we learn about the methods, let’s quickly look at both platforms to gain a better understanding of how connecting Stripe to Redshift could benefit your data management and analysis processes.
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What Is Stripe?
Stripe is an online payment processing platform that enables businesses to accept payments over the Internet. It offers advanced subscription management tools that allow setting up recurring payments and subscription billing models. This makes it convenient for subscription-based businesses to automate customer payments and recurring billing.
Businesses can accept payments from customers worldwide using Stripe. The various payment methods it supports include debit and credit cards, digital wallets like Google Pay and Apple Pay, and bank transfers. Stripe prioritizes security and supports 3D Secure 2 for frictionless authentication, helping reduce fraud and providing added security to online payments. It also uses fraud detection tools to help reduce the risk of fraudulent transactions.
Here are some key features of Stripe:
- Developer-Friendly API: Stripe provides a robust set of APIs that allows you to integrate payment functionality directly into your websites and applications. The well-documented API supports different programming languages like Ruby, Python, Java, and .NET.
- Global Availability: The platform supports payments in 135+ currencies and operates in 46 countries. This makes it an ideal choice if your business has an international customer base.
- Stripe Connect: This allows you to build and manage platforms that facilitate payments between multiple parties. Stripe Connect allows you to split funds between multiple users, instantly route payments across borders, and specify your earnings on each transaction.
What Is Redshift?
Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It is a fully managed and high-performance platform that can store and analyze petabyte-scale data workloads efficiently.
One of the factors that contribute to Redshift’s efficiency is the columnar storage of data. Redshift organizes its data in columns instead of rows, contributing to improved query performance. It only accesses the required columns to execute a query, thereby reducing I/O overhead. You can use your existing BI tools to analyze your data stored in Redshift, making it a cost-effective solution.
Some of the key features of Amazon Redshift include:
- Massively Parallel Processing (MPP): Redshift has a distributed architecture with multiple nodes and slices. Large processing jobs are broken into smaller jobs that are distributed across a cluster of compute nodes. It executes queries in parallel across these nodes, resulting in faster processing of complex queries, even on large datasets.
- Integration with other AWS Services: Redshift can seamlessly integrate with other AWS Services, including Amazon S3, AWS Glue, and Amazon QuickSight.
- Automatic Data Backup: Redshift automatically creates incremental data backups and stores them in Amazon S3, which is internally managed by Amazon Redshift. It also replicates data to different nodes to ensure high availability and data durability.
How to Ingest Data From Stripe to Redshift
There are several ways to set up a Stripe to Redshift integration, each with different levels of complexity, cost, and freshness of data. Depending on your team’s needs, you can choose from the following approaches:
- Method #1: Stripe Data Pipeline – Stripe’s native option that delivers data to Redshift on a scheduled refresh.
- Method #2: Real-time integration tools like Estuary Flow – A no-code platform that streams Stripe events directly into Redshift within seconds.
- Method #3: Custom scripts – Manually using Stripe APIs, webhooks, and COPY commands to push data into Redshift.
In the next sections, we’ll break down how each method works, their pros and cons, and when you should choose one over another.
Method #1: Using Stripe Data Pipeline to Sync with Redshift
The Stripe Data Pipeline is a no-code product that sends your Stripe data and reports to Amazon Redshift or Snowflake. After you subscribe, Stripe performs an initial load within 12 hours. Once that is complete, your data is refreshed every 3 hours.
This setup is tightly integrated with Stripe’s platform and ensures data integrity, but it still leaves you with several hours of delay between events happening in Stripe and data appearing in Redshift. For financial teams that need real-time visibility, this lag can be limiting.
Read more about the steps here to set up the connection between Stripe Data Pipeline and Redshift.
Here are some benefits of using Stripe Data Pipeline for this integration:
- Automatically export your Stripe data to Redshift in a fast and reliable way.
- Combine data from different Stripe accounts into a single data warehouse.
- Stop relying on third-party ETL tools or home-built API integrations.
- Supports large data volumes for improved data integrity or quality with scaling businesses.
However, this method is also associated with certain limitations:
- There is a 12-hour delay for the initial load of Stripe data upon subscribing to Data Pipeline.
- Some data regions aren’t supported.
Method #2: Using Estuary Flow to Load Stripe Data into Redshift in Real Time
While Stripe Data Pipeline ensures managed exports, its 3-hour refresh window may not be enough if your business depends on up-to-the-second analytics. Delays of even a few hours can make it harder to detect fraud, monitor revenue in real time, or power live dashboards.
That’s where Estuary Flow makes the difference. Estuary continuously streams Stripe events into Redshift, so your warehouse reflects the latest charges, subscriptions, refunds, and payouts within seconds, not hours. There are no scripts to maintain and no batch jobs to manage — just a reliable, always-on pipeline.
👉 To get started, register for a free Estuary account or log in here if you already have one.
Here are the steps to follow to set up the integration between the two platforms:
Step 1: Set Up Stripe as the Data Source
- In the Estuary dashboard, go to the Sources tab and click + New Capture.
- Search for Stripe in the connector list and select Stripe Real-time.
- Fill in the configuration form:
- Access Token: Enter your Stripe API key (sk_live_... for production or sk_test_... for testing).
- Start Date: Optional. Provide a UTC timestamp in the format YYYY-MM-DDTHH:MM:SSZ. If left blank, Flow defaults to 30 days prior.
- Capture Connected Accounts: Enable if you want to include data from connected accounts. Flow will add an account_id field to each record.
- Click Next to validate the connection. Flow will automatically detect the available Stripe resources.
- Click Save and Publish. Flow will backfill historical data and then switch to real-time event streaming.
Supported Stripe resources include: Charges, Customers, Subscriptions, Subscription Items, Invoices, Invoice Items, Payouts, Refunds, Disputes, Balance Transactions, Credit Notes, Events, Payment Intents, Payment Methods, Setup Intents, Transfers, Usage Records, and more. Each resource is captured into its own Flow collection.
📌 Reference: Stripe Real-time connector documentation
Step 2: Set Up Redshift as the Destination
- After your Stripe capture is published, go to the Destinations tab in the Estuary dashboard and click + New Materialization.
- Search for Amazon Redshift and select it from the list.
- Fill in the configuration form:
- Name: Enter a unique name (for example: stripe_to_redshift).
- Address: Enter your Redshift cluster endpoint in the format host:port (port defaults to 5439).
- User: Provide a database user with permissions to create tables and insert, update, and delete rows.
- Password: Password for the database user.
- Database: The name of your Redshift database.
- Schema: Defaults to public, but you can specify another schema.
- Region: Your Redshift cluster’s AWS region.
- S3 Staging Bucket: Provide an S3 bucket and IAM role or credentials. Flow stages data here temporarily before loading it into Redshift using the COPY command.
- Hard Delete (optional): Enable if you want deletions in Stripe to also remove rows in Redshift. Otherwise, Flow applies soft deletes with metadata flags.
- Under Source Collections, link the Stripe capture you created in Step 1. Flow will display the available collections (e.g., charges, customers, subscriptions).
- Bind each collection to a target Redshift table. Flow will mirror the schema automatically.
- Click Save and Publish to deploy the materialization.
📌 Reference: Amazon Redshift connector documentation
Step 3: Monitor and Query Your Data
- In the Estuary dashboard, check the status of your capture and materialization. Both should show as running.
- Open your Redshift cluster and run a query to validate the sync. For example:
plaintextSELECT *
FROM public.charges
ORDER BY created DESC
LIMIT 10;
- Estuary Flow enforces schema validation and exactly-once delivery, so your Redshift tables always remain consistent with Stripe data.
Why choose Estuary Flow?
Unlike Stripe Data Pipeline (which refreshes every 9 hours) or custom scripts (which require ongoing maintenance), Estuary Flow streams events continuously. You get fresh Stripe data in Redshift within seconds, backed by enterprise-grade security, schema evolution, and built-in monitoring.
👉 To get started, register for a free Estuary account or log in here if you already have one.
Method #3: Using Custom Scripts to Move Data from Stripe to Redshift
Stripe supports REST API to access and retrieve data from the platform. The API responses are in JSON format. With Stripe, you can use two different types of keys for authentication, one for live mode and one for testing mode. You can use the testing mode to test every aspect of API without messing up your actual data.
To access the API, you can use tools like CURL, Postman, or an HTTP client for the language or framework of your choice. Stripe API is built around ten core resources, including Customers, Disputes, Tokens, and Transfers.
This method involves extracting data from Stripe and using Webhooks to stream it into your data warehouse. Since data exports from Stripe are in JSON format, you must map your data with an appropriate data type supported by Redshift.
Once the data is extracted from Stripe, load it into an intermediary source, like Amazon S3. Then, use the Redshift COPY command to load the S3 files to Redshift.
Your data has then been successfully loaded from Stripe to Redshift.
Some of the limitations of the method include:
- Prior knowledge of APIs and Webhooks is necessary. Those without the required technical know-how will find it difficult to implement this method.
- Dedicated resources are required to create the custom scripts and maintain them. This makes it time-consuming and resource-intensive.
- If the script encounters errors during data extraction, transformation, or loading, it results in data integrity issues and incomplete or inaccurate data.
Comparing Methods to Sync Stripe with Redshift
Method | How It Works | Pros | Cons |
Stripe Data Pipeline | Stripe-managed export to Redshift or Snowflake. Initial load in ~12 hours, then refreshes every 3 hours. | - Native Stripe integration- No scripts required- Secure and reliable | - 3-hour refresh delay (not real time)- AWS-only regional support- Limited flexibility |
Custom Scripts | Use Stripe APIs or webhooks to extract data → stage in S3 → load with Redshift COPY. | - Full control- Can be customized to unique needs | - High development effort- Ongoing maintenance required- Prone to errors and schema drift |
Estuary Flow (Recommended) | Real-time capture from Stripe API → streaming sync to Redshift with exactly-once delivery. | - Real-time data (seconds, not hours)- No-code setup- Schema-aware, auto-evolving- Secure and compliance-ready | - Requires Estuary account (free to start)- May be more than needed for one-time exports |
See how other teams use Estuary Flow for real-time financial data pipelines. Explore our customer success stories.
Conclusion
Ingesting data from Stripe to Redshift gives your team the ability to analyze payments alongside other business data for richer insights. There are several ways to achieve this: Stripe Data Pipeline, custom scripts, or a no-code real-time ETL platform like Estuary Flow.
Stripe Data Pipeline is simple to enable, but its 3-hour refresh cycle means your Redshift data always lags behind what’s actually happening in Stripe. Custom scripts give you flexibility, but they require ongoing maintenance and can introduce errors at scale.
With Estuary Flow, you get the best of both worlds:
- Real-time streaming so every Stripe charge, refund, or subscription lands in Redshift within seconds.
- Exactly-once delivery and schema enforcement, so you can trust your data.
- Secure and flexible deployment options, including private cloud setups for compliance needs.
🚀 Ready to make your Stripe data truly real-time? Sign up free, join the Slack community, or contact us for tailored help.
FAQs
1. What is the best ETL tool to sync data from Stripe to Redshift?
2. How do I connect Stripe to Amazon Redshift without coding?
3. Can I use Stripe APIs or webhooks to load data into Redshift?
4. Is syncing Stripe to Redshift secure?

About the author
With over 15 years in data engineering, a seasoned expert in driving growth for early-stage data companies, focusing on strategies that attract customers and users. Extensive writing provides insights to help companies scale efficiently and effectively in an evolving data landscape.
