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
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
"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
“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.”
“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
- Determine the Data to Export: Decide which collections or databases you need to export from MongoDB.
- Choose the Export Format: MongoDB’s mongodump utility can export data in BSON format, which is MongoDB’s binary format. However, for a data lake, you might want to convert the data into JSON or CSV for better compatibility.
- Use mongodump to Export Data:
mongodump --db your_database --collection your_collection --out /path/to/export/directory - Convert BSON to JSON (if Needed):
bsondump --outFile=/path/to/export/directory/your_collection.json /path/to/export/directory/your_database/your_collection.bson - (Optional) Convert JSON to CSV:
If your data processing or analytics tools require CSV, you can write a script or use a command-line JSON processor like jq to convert the JSON file to CSV.
- Set Up AWS CLI: Install and configure the AWS Command Line Interface (CLI) with the necessary permissions to access S3.
aws configure - Create an S3 Bucket: If you don’t already have an S3 bucket for your data lake, create one using the AWS Management Console or AWS CLI:
aws s3 mb s3://your-datalake-bucket-name
- Compress the Data (optional, but recommended to save space and transfer time):
tar -czvf your_data.tar.gz /path/to/export/directory/ - Upload Data to S3:
aws s3 cp your_data.tar.gz s3://your-datalake-bucket-name/path/to/store/or if you have multiple files and want to upload a directory:
aws s3 sync /path/to/export/directory/ s3://your-datalake-bucket-name/path/to/store/
- Catalog Data: Use AWS Glue or another data catalog tool to discover, prepare, and combine your data so it’s ready for analysis.
- Create a Database: Use AWS Glue or Amazon Athena to create a database that references the S3 data location.
- Define Table Schema: Define the schema that corresponds to your data format (JSON, CSV, etc.).
- Data Transformation: If necessary, use AWS Glue or a similar service to transform the data into a format that is optimized for analytics (e.g., Parquet).
- Query Data: Use Amazon Athena, Amazon Redshift Spectrum, or another querying tool to run SQL queries directly against your data in S3.
- Integrate with Analytics Tools: Connect your AWS Data Lake with analytics and BI tools to visualize and analyze your data.
- Write Automation Scripts: Write scripts to automate the export from MongoDB and the upload to S3.
- Schedule Regular Updates: Use cron jobs on Linux or Task Scheduler on Windows to schedule your scripts to run at regular intervals.
- Monitor Transfers: Set up monitoring and alerts to track the success or failure of the data transfer jobs.
- Data Encryption: Ensure data is encrypted in transit (using SSL/TLS) and at rest (using S3 server-side encryption).
- Access Control: Use IAM roles and policies to control access to the S3 bucket and data.
- Compliance: Ensure your data transfer and storage process complies with relevant data protection laws and regulations.
Remove Temporary Files: After the data is successfully transferred and verified, you can remove any temporary files from your local system and EC2 instances (if used).
- Document the Process: Write detailed documentation of the entire process for future reference and onboarding.
- Maintain and Update: Regularly review and update the scripts, AWS configurations, and security measures.
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.
MongoDB is a popular open-source NoSQL database that stores data in a flexible, document-based format. It is designed to handle large volumes of unstructured data and is highly scalable, making it a popular choice for modern web applications. MongoDB uses a JSON-like format to store data, which allows for easy integration with web applications and APIs. It also supports dynamic queries, indexing, and aggregation, making it a powerful tool for data analysis. MongoDB is widely used in industries such as finance, healthcare, and e-commerce, and is known for its ease of use and flexibility.
MongoDB gives access to a wide range of data types, including:
1. Documents: MongoDB stores data in the form of documents, which are similar to JSON objects. Each document contains a set of key-value pairs that represent the data.
2. Collections: A collection is a group of related documents that are stored together in MongoDB. Collections can be thought of as tables in a relational database.
3. Indexes: MongoDB supports various types of indexes, including single-field, compound, and geospatial indexes. Indexes are used to improve query performance.
4. GridFS: MongoDB's GridFS is a specification for storing and retrieving large files, such as images and videos, in MongoDB.
5. Aggregation: MongoDB's aggregation framework provides a way to perform complex data analysis operations, such as grouping, filtering, and sorting, on large datasets.
6. Transactions: MongoDB supports multi-document transactions, which allow multiple operations to be performed atomically.
7. Change streams: MongoDB's change streams provide a way to monitor changes to data in real-time, allowing applications to react to changes as they occur.
Overall, MongoDB provides access to a flexible and powerful data model that can handle a wide range of data types and use cases.
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:
