How to load data from Recreation to S3 Glue

Learn how to use Airbyte to synchronize your Recreation data into S3 Glue within minutes.

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams  to meet all their growing custom business demands in the new AI era.

Building in-house pipelines

Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

After Airbyte

Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Recreation connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up S3 Glue for your extracted Recreation data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Recreation to S3 Glue in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

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What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

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Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman

Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

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Rupak Patel

Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Set Up AWS Environment

Begin by ensuring that you have an AWS account with permissions to use AWS Glue, S3, and IAM (Identity and Access Management). Create an S3 bucket to store your data. Note the bucket name as it will be used in later steps.

Step 2: Configure IAM Roles and Policies

Create an IAM role for AWS Glue that has the necessary permissions to read from your PostgreSQL database and write to your S3 bucket. The policy should include `s3:PutObject` access for your S3 bucket and relevant permissions for AWS Glue operations.

Step 3: Set Up PostgreSQL Database Access

Ensure your on-premises PostgreSQL database is accessible over the internet. If it's behind a firewall, configure the firewall to allow inbound connections from AWS Glue IP addresses. Create a database user with read-only access to the data you want to transfer.

Step 4: Create a JDBC Connection in AWS Glue

In the AWS Glue console, navigate to the "Connections" section and create a new JDBC connection. Enter the PostgreSQL JDBC URL, username, and password. This connection will allow AWS Glue to access your database.

Step 5: Define a Crawler in AWS Glue

Create a new AWS Glue crawler to inspect your PostgreSQL database and infer the schema. Use the JDBC connection created in the previous step. Configure the crawler to store metadata in the AWS Glue Data Catalog. Run the crawler to populate the Data Catalog with schema information.

Step 6: Create an AWS Glue ETL Job

In the AWS Glue console, create a new ETL job. Select the Data Catalog table created by the crawler as the data source. Set the S3 bucket as the destination. Use the AWS Glue script editor to transform the data as needed. Save and run the job to transfer data from PostgreSQL to S3.

Step 7: Verify Data Transfer and Schedule Jobs

Once the job completes, verify the data in your S3 bucket to ensure it has been transferred correctly. If needed, schedule the AWS Glue job to run at regular intervals using the AWS Glue scheduler or AWS CloudWatch Events to keep your S3 data up-to-date.

This guide provides a structured approach to moving data from an on-premises PostgreSQL database to Amazon S3 using AWS Glue, ensuring a seamless transfer without relying on third-party connectors or integrations.