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
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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."
First, you'll need to download the data from your S3 bucket to a local machine or an intermediate server. Use the AWS CLI (Command Line Interface) for this task. Install AWS CLI, configure it with your credentials, and run a command like `aws s3 cp s3://your-bucket-name/your-data-file /local-directory/` to download your data.
Once the data is downloaded, prepare it for insertion into Oracle. Depending on your data format (CSV, JSON, etc.), you might need to clean or transform it. Ensure the data types and formats match those expected by your Oracle database. You can use local scripting languages like Python or shell scripts for data preparation.
Ensure that you have access to your Oracle database. Install necessary Oracle client tools like SQL*Plus or SQL Developer on your local machine or server. These tools will allow you to execute SQL commands and scripts to insert data into Oracle.
Before loading data, ensure that the target table structure in Oracle matches the data structure from S3. Use SQL commands or your preferred Oracle client tool to create a table, specifying appropriate data types and constraints.
Convert your prepared data into a format that Oracle can easily import. If your data is in CSV, ensure that it adheres to Oracle's import requirements, such as proper delimiters and encoding. Tools like SQL Loader can be particularly useful for bulk inserting CSV data into Oracle tables.
Use Oracle’s SQL Loader or other command-line utilities to load the data into your Oracle database. For SQL Loader, create a control file that specifies how to load your data and execute it with a command like `sqlldr userid=username/password control=your-control-file.ctl`.
After loading, verify that the data has been correctly inserted into your Oracle database. Run SQL queries to check the row count and data consistency, ensuring no data was lost or corrupted during the transfer process. Make adjustments and re-load if necessary.
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.
Amazon S3 (Simple Storage Service) is a cloud-based object storage service that provides developers and IT teams with secure, durable, and scalable storage for their data. It allows users to store and retrieve any amount of data from anywhere on the web, making it easy to build and scale applications, backup and archive data, and analyze data. S3 is designed to provide high availability and durability, with data automatically replicated across multiple availability zones within a region. It also offers a range of features such as versioning, lifecycle policies, and access control to help users manage their data effectively.
Amazon S3's API provides access to a wide range of data types, including:
1. Object data: This includes the actual files stored in S3 buckets, such as images, videos, documents, and other types of files.
2. Metadata: S3 stores metadata about each object, including information such as the object's size, creation date, and last modified date.
3. Access control data: S3 provides access control mechanisms to restrict access to objects in a bucket. The API provides access to information about access control policies and permissions.
4. Bucket data: S3 buckets are containers for objects. The API provides access to information about buckets, such as their names, creation dates, and region.
5. Logging data: S3 can log access requests to objects in a bucket. The API provides access to these logs, which can be used for auditing and compliance purposes.
6. Inventory data: S3 can generate inventory reports that provide information about the objects stored in a bucket. The API provides access to these reports.
7. Metrics data: S3 can generate metrics about the usage of a bucket, such as the number of requests and the amount of data transferred. The API provides access to these metrics.
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:
