Trusted by data-driven companies
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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- 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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
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."
To access files on an SFTP server, you need the server's URL, port, username, and password. Use a secure method to store these credentials, such as environment variables or a configuration file with restricted permissions. Verify access by using an SFTP client or command-line tool to list files on the server.
Write a script to download files from the SFTP server. This can be done using Python's `pysftp` or `paramiko` library. The script should connect to the SFTP server, navigate to the appropriate directory, and download files to a local directory. Ensure the script handles exceptions and logs any errors.
Once files are downloaded, parse them to extract the data. Depending on the file format (CSV, JSON, XML, etc.), use the appropriate Python library such as `csv`, `json`, or `xml.etree.ElementTree`. Convert the data into a format suitable for insertion into DynamoDB, typically a list of dictionaries where keys are the column names.
Install and configure the AWS SDK for Python, `boto3`. Set up AWS credentials with sufficient permissions to access DynamoDB. You can do this by configuring the `~/.aws/credentials` file or by setting environment variables. Verify the setup by listing DynamoDB tables using a simple `boto3` script.
Ensure the target DynamoDB table exists. If not, create it using the AWS Management Console or `boto3`. Define the primary key (partition key and optionally a sort key) based on the data structure. Set appropriate read/write capacity modes (on-demand or provisioned) according to expected traffic.
Write a script to insert parsed data into the DynamoDB table. Use `boto3` to batch write items to the table. To optimize performance and avoid throttling, use the `batch_write_item` method, which supports writing up to 25 items at a time. Implement error handling to manage failed writes and retry logic.
Automate the entire process by scheduling the script to run at desired intervals using tools like `cron` on Linux or Task Scheduler on Windows. Ensure the script logs its operations and errors for monitoring. Consider adding notifications, such as email alerts upon success or failure, to monitor the process effectively.
By following these steps, you can efficiently move data from an SFTP server to DynamoDB using in-house tools and AWS services without relying on third-party integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
SFTP (Secure File Transfer Protocol) is a secure way to transfer files between two computers over the internet. It uses encryption to protect the data being transferred, making it more secure than traditional FTP (File Transfer Protocol). SFTP is commonly used by businesses and organizations to transfer sensitive data such as financial information, medical records, and personal data. It requires authentication using a username and password or public key authentication, ensuring that only authorized users can access the files. SFTP is also platform-independent, meaning it can be used on any operating system, making it a versatile and reliable option for secure file transfers.
SFTP provides access to various types of data that can be used for different purposes. Some of the categories of data that SFTP's API gives access to are:
1. File data: SFTP's API allows users to access and transfer files securely over the internet. This includes uploading, downloading, and managing files.
2. User data: SFTP's API provides access to user data such as usernames, passwords, and permissions. This allows users to manage and control access to their files and folders.
3. Server data: SFTP's API gives access to server data such as server logs, server configurations, and server status. This allows users to monitor and manage their server resources.
4. Security data: SFTP's API provides access to security data such as encryption keys, certificates, and security policies. This allows users to ensure that their data is secure and protected from unauthorized access.
5. Network data: SFTP's API gives access to network data such as IP addresses, network configurations, and network traffic. This allows users to monitor and manage their network resources.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
