How to load data from Azure Blob Storage to DynamoDB
Learn how to use Airbyte to synchronize your Azure Blob Storage 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: Prepare Your Data Source
Begin by ensuring that your data source is in a readable format, such as CSV, JSON, or any structured format that can be easily parsed. If your data source is a database, export the data into one of these formats. Ensure that the data is clean and structured properly to match the schema you will use in DynamoDB.
Step 2: Set Up AWS Environment
Log in to your AWS Management Console. Ensure you have the necessary permissions to create and manage DynamoDB resources. If you don't have an AWS account, sign up for one. Familiarize yourself with the AWS CLI (Command Line Interface) as it will be essential for uploading data to DynamoDB.
Step 3: Create a DynamoDB Table
Navigate to the DynamoDB service in the AWS Management Console and create a new table. Define the primary key (partition key and optional sort key) according to your needs. Choose the appropriate read/write capacity mode (on-demand or provisioned) based on your expected data usage.
Step 4: Install and Configure AWS CLI
Download and install the AWS CLI on your local machine. After installing, configure it using the `aws configure` command, providing your AWS Access Key ID, Secret Access Key, default region, and output format. This setup will allow you to interact with AWS services directly from your terminal.
Step 5: Convert Data to DynamoDB JSON Format
Write a script in your preferred programming language (e.g., Python, Node.js) to read your data source and convert it into DynamoDB's JSON format. DynamoDB requires specific data types like `S` for string, `N` for number, etc. Ensure each item in your script matches the structure and types defined in your DynamoDB table.
Step 6: Batch Write Data to DynamoDB
Use the AWS CLI or SDKs to batch write data into DynamoDB. For example, with the AWS CLI, use the `aws dynamodb batch-write-item` command. Since batch writes are limited to 25 items per request, ensure that your script handles this by splitting the data into appropriate chunks and iterating over them to load all items.
Step 7: Verify Data Integrity
Once the data is loaded, verify that it has been accurately transferred by querying the DynamoDB table. Use the AWS Management Console or AWS CLI to perform queries or scans to check the data. Ensure the data matches the source in terms of structure and content, and troubleshoot any discrepancies as necessary.