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Begin by utilizing Instagram's Graph API. You’ll need to register as a developer on the Facebook for Developers platform. Once registered, create an Instagram Basic Display or Graph API app to get the necessary credentials (app ID, app secret, and access tokens). This will allow you to authenticate and access Instagram data programmatically.
Set up your local development environment to interact with the Instagram API. Install necessary libraries such as `requests` or `http.client` in Python to make HTTP requests. Ensure that your environment is capable of handling OAuth processes to manage access tokens securely.
Use the access tokens obtained to authenticate your API requests. Fetch the desired data (like user profiles, media, comments, etc.) by making API calls. Handle pagination if your dataset is large, as Instagram's API will likely return data in chunks.
Once the data is retrieved, transform it into a structured format such as JSON or CSV. This transformation is important for ensuring compatibility with the Databricks Lakehouse. Consider normalizing the data if it includes nested or complex structures.
Set up your Databricks environment if it’s not already configured. Create a cluster in Databricks Lakehouse and ensure you have the necessary permissions to write data to the Lakehouse.
Use Databricks’ file upload capabilities to transfer your locally stored JSON or CSV files to the Lakehouse. You can directly upload files using the Databricks UI, or use the Databricks CLI or REST API for batch uploads.
Once the data is uploaded to Databricks, use Spark SQL or Databricks Delta Lake to create tables from your data files. This involves writing Spark jobs to read the CSV or JSON files and converting them into Delta tables, which are optimized for analytics in Databricks.
By following these steps, you can manually move data from Instagram to a Databricks Lakehouse without relying on third-party connectors or integrations. Each step requires careful attention to detail, especially when handling API authentication and data transformation processes.
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:
