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Begin by setting up your AWS environment, which includes creating an S3 bucket to serve as your data lake. Use the AWS Management Console or AWS CLI to create a new S3 bucket where you will store the Pexels data. Make sure to configure appropriate permissions and policies to allow data upload.
Sign up for a Pexels account and navigate to the API section. Apply for API access and obtain your API key. This key will be used to authenticate your requests to the Pexels API, allowing you to access the data.
Write a script in Python or another programming language of your choice to interact with the Pexels API. Use the requests library in Python to send HTTP GET requests to the API endpoints, retrieving data such as image URLs or metadata. Save this data in a structured format, such as JSON or CSV, for easier handling and storage.
Depending on your data storage requirements, you may need to transform the retrieved data. Use Python libraries like pandas to clean, filter, or reformat the data as necessary. This step ensures that the data is in the desired structure before uploading it to your AWS Data Lake.
Utilize the AWS SDK for Python (Boto3) to programmatically upload your transformed data to the S3 bucket created in Step 1. Write a script that authenticates with AWS using IAM credentials and uploads the data files to your specified bucket and folder structure. Ensure that the files are correctly named and organized.
After uploading, verify that the data has been successfully stored in the S3 bucket. List the contents of your bucket using the AWS CLI or Boto3 and check for the presence of your files. Optionally, download a sample file and compare it to the original to ensure data integrity.
Once the data flow is verified, automate the process using AWS Lambda or a cron job on an EC2 instance. This involves scheduling your data retrieval and upload scripts to run at regular intervals, ensuring that your data lake is continuously updated with the latest data from Pexels.
By following these steps, you can efficiently move data from the Pexels API to an AWS Data Lake using native AWS tools and custom scripts, without relying on 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.
The Pexels API enables programmatic access to the entire Pexels content library, including photos, videos. All content is free, and you're welcome to use Pexels content for anything, as long as it stays within our guidelines.The Pexels API is a RESTful JSON API, and you can interact with it from any language or framework with an HTTP library. Alternatively, Pexels maintains some official client libraries that you can use.
Pexels API provides access to a vast collection of high-quality images and videos that can be used for various purposes. The API offers a range of data categories, including:
- Images: Pexels API provides access to millions of high-quality images that can be used for commercial and personal projects. The images are available in various resolutions and formats, including JPEG and PNG.
- Videos: The API also offers access to a large collection of high-quality videos that can be used for commercial and personal projects. The videos are available in various resolutions and formats, including MP4 and MOV.
- Search: Pexels API allows users to search for images and videos based on keywords, categories, and other parameters. The search results can be filtered by various criteria, such as orientation, size, and color.
- Popular: The API provides access to a list of popular images and videos that are currently trending on the platform.
- Curated Collections: Pexels API offers access to a range of curated collections of images and videos that are organized by theme, such as nature, technology, and business.
- Contributors: The API also provides information about the contributors who have uploaded images and videos to the platform, including their names and profiles.
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:
