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Begin by accessing your Adjust account and familiarize yourself with their API documentation. Use the Adjust API to programmatically extract the data you need. Create a script using a programming language like Python to authenticate and make API requests. The script should handle pagination and rate limits to ensure all data is retrieved efficiently.
Once the data is extracted, transform it into a structured format like CSV or JSON. This step involves parsing the JSON response from the API and converting it into a format suitable for storage and further processing. Use libraries such as Pandas in Python to assist with this conversion process.
Log into your AWS account and navigate to the S3 service to create a new bucket where the data will be stored. Ensure that you choose a globally unique name for the bucket and configure the appropriate permissions and policies, allowing access to the users or applications that require it.
Use the AWS SDK for Python (Boto3) to upload your transformed data files to the S3 bucket. Write a script that connects to your AWS account, accesses the S3 service, and uploads your CSV or JSON files to the designated bucket. Ensure you handle exceptions and potential errors during the upload process.
Once data is in S3, navigate to AWS Glue in the AWS Management Console. Create a new Glue Crawler that will scan the data in your S3 bucket to create a schema. Configure the crawler to point to your S3 bucket, and specify the IAM role with the necessary permissions to access the bucket.
After the crawler has cataloged the data, set up a Glue Job to process it. Define the ETL (Extract, Transform, Load) operations needed to transform the data further if necessary. Use Python or Scala scripts within Glue to perform these transformations. Configure the job to read from the Data Catalog created by the crawler.
Execute the Glue Job and monitor its progress through the AWS Management Console. Check logs and metrics to ensure the job completes successfully. Automate the process using AWS Glue Workflows if needed, allowing for regular updates and processing of new data as it arrives in the S3 bucket. Ensure alerts are in place for any errors or failures during execution.
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.
Adjust is a favorite mobile attribution and deep-linking platform that makes mobile marketing easy. It is a mobile marketing analytics platform trusted by marketers around the world. This permits you to understand your users through attribution, giving you detailed insights into their journey and overall product experience. With a special focus on fraud prevention and data protection, Adjust also provides sophisticated app analytics capabilities to drive your project strategy and optimize your customer experience.
Adjust's API provides access to a wide range of data related to mobile app marketing and user engagement. The following are the categories of data that can be accessed through Adjust's API:
1. Attribution data: This includes information about the source of app installs, such as the ad network, campaign, and creative.
2. In-app events data: This includes data related to user actions within the app, such as purchases, registrations, and other custom events.
3. User engagement data: This includes data related to user behavior within the app, such as session length, retention rate, and user churn.
4. Ad performance data: This includes data related to the performance of ad campaigns, such as impressions, clicks, and conversions.
5. Audience data: This includes data related to the demographics and behavior of app users, such as age, gender, location, and interests.
6. Fraud prevention data: This includes data related to the detection and prevention of fraudulent activity within the app, such as click spamming and install fraud.Overall, Adjust's API provides a comprehensive set of data that can be used to optimize mobile app marketing campaigns and improve user engagement.
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:
