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."
Before you begin, familiarize yourself with Instagram's Graph API. This API allows developers to access Instagram data programmatically. Review the API documentation to understand the available endpoints and data formats.
Create a developer account on the Instagram platform and register your application. This step is crucial for obtaining the necessary API credentials (client ID and client secret) that will allow you to authenticate and interact with Instagram's API.
Use the OAuth 2.0 protocol to authenticate your application with Instagram. This involves redirecting users to Instagram’s authorization URL and handling the callback to obtain an access token. Make sure to request permissions that allow you to access the necessary data.
Once authenticated, use the access token to make API requests to Instagram. Determine the specific data you need (e.g., user profiles, media, comments) and use the appropriate endpoints to fetch this data. Store the retrieved data temporarily, perhaps in a JSON format, for further processing.
Ensure that you have an Oracle Database set up and accessible. You will need the database connection details such as host, port, service name, username, and password. Create the necessary tables and schemas in Oracle to store Instagram data if they do not exist.
Process the fetched Instagram data to match the schema of your Oracle database. This may involve transforming JSON data into SQL insert statements, ensuring data types are compatible, and handling any necessary data cleansing or enrichment.
Use a programming language like Python, Java, or any language that supports database connectivity to connect to your Oracle Database. Execute the SQL insert statements to transfer the data from your temporary storage into the Oracle tables. Ensure proper error handling and logging for successful data transfer.
By following these steps, you can move data from Instagram to an Oracle database without using third-party connectors or 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.
Instagram is a popular photo/video sharing application that enables users to share images and text captions with other people on social media. The app allows users to apply a variety of custom filter effects to enhance their images. Instagram is a free service and offers the ability to follow others, make user profiles private or public, post to other linked social accounts, and tag people or a location.
Instagram's API provides access to a wide range of data related to user accounts, media, and interactions. Here are the categories of data that can be accessed through Instagram's API:
1. User data: This includes information about a user's profile, such as their username, bio, profile picture, follower count, and following count.
2. Media data: This includes information about the media that a user has posted, such as the caption, location, likes, comments, and tags.
3. Hashtag data: This includes information about hashtags that are used in posts, such as the number of posts that have used a particular hashtag, and the top posts for a given hashtag.
4. Location data: This includes information about the locations that are associated with posts, such as the name of the location, the latitude and longitude, and the number of posts associated with a particular location.
5. Comment data: This includes information about the comments that are posted on media, such as the text of the comment, the username of the commenter, and the time the comment was posted.
6. Like data: This includes information about the likes that are given to media, such as the username of the user who liked the media, and the time the like was given.
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:
