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Begin by familiarizing yourself with Adjust's API documentation. Adjust provides a RESTful API that allows you to extract reports and data. Ensure you understand how to authenticate requests, the available endpoints, and the data formats Adjust supports. This will be crucial for configuring your data extraction scripts.
You'll need to authenticate your API requests to Adjust. Typically, this involves using an API token or key provided by Adjust. Securely store this credential and use it to authenticate your requests. Test the authentication by making a simple API call to ensure it works as expected.
Write a script (using a language like Python) to call Adjust’s API and fetch the data you need. Use HTTP GET requests to extract the required data. Ensure your script can handle pagination if the data set is large, and consider other factors like rate limits and timeouts when designing your script.
Once you have the data, you may need to transform it into a format suitable for ClickHouse. ClickHouse supports various formats like CSV, JSON, and TSV. Convert the data from Adjust into one of these formats. Ensure the data types and structure align with the schema you plan to use in ClickHouse.
Ensure you have a running ClickHouse server where you can load the data. Set up your database and table schemas in ClickHouse to match the transformed data structure. Use the ClickHouse client or a suitable interface to create databases and tables.
Use the ClickHouse client or HTTP interface to load your transformed data into ClickHouse. If using the HTTP interface, make sure to specify the appropriate format (e.g., CSV) and handle any necessary configurations like batch sizes or input paths. Test with a small data set to ensure everything loads correctly before attempting a full data load.
Once you've successfully loaded data, automate the process for regular updates. Use a scheduling tool like cron jobs on a Unix-based system to run your extraction and loading scripts at regular intervals. Ensure the automation handles errors gracefully and logs activities for future reference.
By following these steps, you can efficiently transfer data from Adjust to ClickHouse 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.
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:
