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To manually access LinkedIn Ads data, you'll need to use LinkedIn's Marketing Developer Platform. First, create a LinkedIn Developer account and register your application. Obtain the necessary OAuth 2.0 credentials to authenticate API requests. This will allow you to programmatically access your LinkedIn Ads data.
Utilize the LinkedIn Ads API to extract the necessary data. Write a script (using Python, for example) that sends HTTP requests to the LinkedIn Ads API endpoints. Ensure your script handles pagination and rate limiting to efficiently retrieve large datasets. Parse the JSON responses to extract relevant ad performance metrics.
Once you have the data, transform it into a format compatible with Apache Iceberg. Iceberg works well with Parquet or Avro formats. Use a data processing library like Pandas in Python to clean and structure the data. Convert the structured data into Parquet format using PyArrow or a similar library.
Apache Iceberg requires a Hadoop-compatible environment. Install and configure a Hadoop cluster if you haven't already. Ensure that your Hadoop Distributed File System (HDFS) is set up and accessible. This environment will serve as the storage layer for Apache Iceberg.
Download and install Apache Iceberg on your Hadoop environment. Follow the official Iceberg documentation to configure it properly, ensuring that it can interact with your Hadoop cluster. Create a new Iceberg table schema matching the structure of your transformed LinkedIn Ads data.
Use a script to load your Parquet files into the Apache Iceberg table. This can be done using Apache Spark or Hive, which have native support for Apache Iceberg. Write a Spark job that reads the Parquet files and writes them into the Iceberg table, ensuring data integrity and proper schema adherence.
After loading the data, run queries to validate that the data in Apache Iceberg matches your expectations. Use SQL syntax supported by your Hadoop environment through Spark or Hive to verify row counts and data correctness. Finally, optimize the Iceberg table by compacting small files and optimizing data layouts to improve query performance.
By following these steps, you can effectively move data from LinkedIn Ads to Apache Iceberg using custom scripts and a Hadoop environment, 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.
LinkedIn ads helps businesses of any size achieve their goals and reach their target market. Over 850M active professionals are on LinkedIn. Target your audience them by job title, function, industry, and more.
LinkedIn Ads API provides access to a wide range of data related to LinkedIn advertising campaigns. The following are the categories of data that can be accessed through the API:
1. Ad Campaign Data: This includes data related to the performance of ad campaigns such as impressions, clicks, conversions, and spend.
2. Audience Data: This includes data related to the audience targeted in the ad campaigns such as demographics, job titles, industries, and locations.
3. Account Data: This includes data related to the LinkedIn advertising account such as account balance, billing information, and account settings.
4. Ad Creative Data: This includes data related to the ad creatives used in the campaigns such as ad formats, images, and headlines.
5. Conversion Tracking Data: This includes data related to the conversion tracking set up for the campaigns such as conversion events, conversion values, and conversion tracking tags.
6. Engagement Data: This includes data related to the engagement of the audience with the ad campaigns such as likes, comments, and shares.
7. Performance Data: This includes data related to the overall performance of the ad campaigns such as click-through rates, conversion rates, and cost per click.
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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