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 which database or data warehouse Looker is connected to.
- Identify the specific datasets, tables, or queries you want to move to the AWS Data Lake.
- Connect to your database or data warehouse using the appropriate credentials and interface (e.g., command line, database management tool, etc.).
- Export the data you want to move. This might involve running SQL queries to extract the data and then saving it to a file format suitable for the Data Lake, such as CSV, JSON, or Parquet.
- Set up an Amazon S3 bucket, which will serve as a primary storage component of your AWS Data Lake.
- Configure the bucket with the necessary permissions to ensure that only authorized users and services can access the data.
- Install the AWS Command Line Interface (CLI) on your local machine or server.
- Configure the AWS CLI with your AWS credentials and default region.
- Use the AWS CLI to upload the extracted data files to your S3 bucket using the aws s3 cp command.
- Use AWS Glue to create a crawler that will scan your S3 bucket and catalog the data.
- Run the crawler to populate the AWS Glue Data Catalog with metadata about the data stored in S3. This makes the data searchable and queryable.
- Once your data is in S3 and cataloged by AWS Glue, you can use services like Amazon Athena to query the data directly in S3 without needing to load it into a database.
- Alternatively, you can load the data into Amazon Redshift or another AWS data warehousing service for more complex analytics.
Detailed Steps for Key Operations
#Extracting Data from the Source Database
-- Example SQL query to extract data
SELECT * FROM your_table WHERE your_conditions;
- Save the output to a CSV file (this could be done within a database management tool or by command line utilities like mysqldump for MySQL).
#Uploading Data to S3
# Example command to copy a file to S3
aws s3 cp /path/to/yourdata.csv s3://your-bucket-name/data-folder/
#Creating a Crawler in AWS Glue
- Navigate to AWS Glue in the AWS Management Console.
- Click on “Crawlers” and then “Add crawler.”
- Follow the wizard to specify the S3 source path, IAM role, and schedule.
#Querying Data with Amazon Athena
- Navigate to Amazon Athena in the AWS Management Console.
- Ensure that the AWS Glue Data Catalog is selected as the source.
- Write SQL queries to analyze your data directly in S3.
- Ensure that all data transfers are encrypted in transit and at rest.
- Use IAM roles and policies to control access to the S3 bucket and other AWS resources.
- Monitor access and usage of the AWS resources with AWS CloudTrail and Amazon CloudWatch.
- Set up lifecycle policies on your S3 buckets to archive or delete old data.
- Regularly review and update IAM roles and policies to adhere to the principle of least privilege.
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.
Looker is a Google-Cloud-based enterprise platform that provides information and insights to help move businesses forward. Looker reveals data in clear and understandable formats that enable companies to build data applications and create data experiences tailored specifically to their own organization. Looker’s capabilities for data applications, business intelligence, and embedded analytics make it helpful for anyone requiring data to perform their job—from data analysts and data scientists to business executives and partners.
Looker's API provides access to a wide range of data categories, including:
1. User and account data: This includes information about users and their accounts, such as user IDs, email addresses, and account settings.
2. Query and report data: Looker's API allows users to retrieve data from queries and reports, including metadata about the queries and reports themselves.
3. Dashboard and visualization data: Users can access data about dashboards and visualizations, including the layout and configuration of these elements.
4. Data model and schema data: Looker's API provides access to information about the data model and schema, including tables, fields, and relationships between them.
5. Data access and permissions data: Users can retrieve information about data access and permissions, including which users have access to which data and what level of access they have.
6. Integration and extension data: Looker's API allows users to integrate and extend Looker with other tools and platforms, such as custom applications and third-party services.
Overall, Looker's API provides a comprehensive set of data categories that enable users to access and manipulate data in a variety of ways.
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:
