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To begin, you need to access LinkedIn Ads data using their API. Sign up for a LinkedIn Developer account if you haven't already and create a new application to obtain your API credentials. Use these credentials to authenticate and make API calls to LinkedIn's Marketing Developer Platform, specifically targeting the endpoints related to your ad data (e.g., Campaigns, Ads, etc.).
Set up a programming environment where you can write scripts to fetch data from LinkedIn's API. Install necessary libraries for HTTP requests (e.g., `requests` for Python), and JSON parsing (since API responses are typically in JSON format). Ensure you have access keys and tokens securely stored in environment variables or a secure vault.
Write a script to send HTTP GET requests to LinkedIn's API endpoints to fetch your required ad data. Ensure you handle authentication by including the necessary headers, such as your access token. Additionally, manage pagination if your data is extensive, making multiple requests to retrieve all entries.
Once you have the raw JSON data from LinkedIn, parse it into a structured format that suits DynamoDB. This may involve iterating over JSON objects and extracting relevant fields such as ad ID, impressions, clicks, etc. Convert this data into a format compatible with DynamoDB, such as a dictionary with key-value pairs.
Create a DynamoDB table in your AWS account to store the LinkedIn Ads data. Define the primary key and sort key (if needed) based on the fields you plan to query. Ensure you have the AWS SDK installed in your development environment and configured with your AWS credentials.
Use the AWS SDK (e.g., `boto3` for Python) to write a script for inserting data into your DynamoDB table. Loop through the structured data from LinkedIn, and use the `put_item` method to add each record to DynamoDB. Handle exceptions to manage any failures during the data insertion process.
To ensure data is regularly and automatically updated, set up a cron job or a scheduled task to execute your data fetching and insertion scripts at desired intervals. Ensure that each run checks for the last updated record to avoid duplicate entries and maintain data consistency.
By following these steps, you can effectively move data from LinkedIn Ads to DynamoDB without relying on third-party connectors or integrations, using custom scripts and AWS tools.
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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