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Begin by setting up your development environment. Make sure you have Python installed as it will be used to interact with the Pexels API. Additionally, ensure you have Java and Hadoop installed since Apache Iceberg operates on top of these platforms.
Sign up or log in to Pexels to obtain an API key. This key is essential for authenticating your requests to the Pexels API. Store this key securely as it will be used to fetch data.
Use Python to send HTTP requests to the Pexels API. Utilize libraries such as `requests` to make GET requests to the API endpoints. Parse the JSON responses to extract the data you need, such as image metadata or other relevant information.
After fetching the data, transform it into a structure compatible with Apache Iceberg. This typically involves converting the JSON data into a tabular format such as CSV or Parquet. Use Python libraries like `pandas` for data manipulation and `pyarrow` to convert data into Parquet format.
Configure your Hadoop environment to support Apache Iceberg. Download and add the Iceberg libraries to your Hadoop classpath. Configure necessary properties in your Hadoop and Hive configurations to support Iceberg tables.
Use Hive or Spark SQL to create an Iceberg table. Define the schema that matches your transformed data. Once the table is created, load the Parquet or CSV files into the table using Hive or Spark SQL commands.
Finally, run a series of queries to verify that the data is correctly loaded into your Iceberg table. Use Hive or Spark SQL to perform simple queries and ensure that the data appears as expected. This step ensures that your data pipeline from Pexels API to Apache Iceberg is successful and accurate.
By following these steps, you can manually move data from the Pexels API to an Apache Iceberg table 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.
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