How to load data from Postgres to DynamoDB
Learn how to use Airbyte to synchronize your Postgres data into DynamoDB 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
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes



Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
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.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
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.
An Extensible Open-Source Standard
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.
Full Control & Security
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
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Chase Zieman

“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.”

Rupak Patel
"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."
How to Sync to Manually
Step 1: Understand the Data Schema
Begin by examining the schema of your PostgreSQL database. Identify the tables, data types, constraints, and relationships between tables. This understanding will be crucial in mapping the data to a DynamoDB-compatible format since DynamoDB is a NoSQL database and has different requirements for data structure.
Step 2: Set Up AWS DynamoDB Environment
Log in to your AWS Management Console and navigate to the DynamoDB service. Create a new DynamoDB table that corresponds to the data you plan to migrate. Define the primary key and any secondary indexes you might need. Keep in mind DynamoDB's limitations such as item size and attribute types when planning your table structure.
Step 3: Export Data from PostgreSQL
Execute a SQL query for each table you want to export and save the results in a CSV or JSON format. Use the PostgreSQL `COPY` command to write the table data to a file on your disk:
```sql
COPY your_table_name TO '/path/to/your_file.csv' DELIMITER ',' CSV HEADER;
```
Ensure that the data types are compatible and that you handle NULL values appropriately.
Step 4: Transform Data to DynamoDB Format
Write a script to transform the exported data into a format that DynamoDB can accept (JSON is recommended). This transformation may involve converting data types and restructuring data to fit DynamoDB's flat schema requirements. Python's `boto3` library can be useful for this task:
```python
import csv
import json
def transform_csv_to_json(csv_file_path, json_file_path):
with open(csv_file_path, mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
data = [row for row in csv_reader]
with open(json_file_path, mode='w') as json_file:
json.dump(data, json_file, indent=4)
```
Step 5: Batch Write to DynamoDB
Use the AWS SDK (e.g., `boto3` for Python) to write the transformed data to your DynamoDB table in batches, as DynamoDB has a limit on the number of write operations per second. Here's a basic example using `boto3`:
```python
import boto3
def batch_write_to_dynamodb(json_file_path, table_name):
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table(table_name)
with open(json_file_path) as json_file:
data = json.load(json_file)
with table.batch_writer() as batch:
for item in data:
batch.put_item(Item=item)
batch_write_to_dynamodb('/path/to/transformed_data.json', 'your_dynamo_table_name')
```
Step 6: Verify Data Integrity
After the data transfer, verify the integrity of the data in DynamoDB. You can do this by running queries to ensure that key data points match between PostgreSQL and DynamoDB. Check the count of items and spot-check individual records for accuracy.
Step 7: Optimize and Monitor Performance
Once the data migration is complete, monitor the performance of your DynamoDB queries and optimize as necessary. This may involve adjusting the read/write capacity units, creating additional indexes, or restructuring data access patterns to fit DynamoDB's strengths. Utilize AWS CloudWatch to keep an eye on the performance metrics of your DynamoDB tables.
By following these steps, you can effectively transfer data from PostgreSQL to DynamoDB without relying on third-party tools.