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First, you need to access your data from Adjust. Adjust offers an API that allows you to fetch reports and data. Use the Adjust API Documentation to authenticate and make requests to fetch the required data. Ensure you have the API key and other necessary credentials to access the API.
Write custom scripts (using a language like Python, Java, or Node.js) to extract data from Adjust. Use the requests or HTTP client libraries to make API calls and retrieve the data. Parse the JSON or CSV responses as required, and prepare the data for transformation.
Once you have the raw data, transform it to match the schema of your TiDB database. This may involve data cleaning, renaming fields, changing data types, and ensuring all necessary fields are correctly formatted. You can use data manipulation libraries such as Pandas in Python to handle the transformation.
Before inserting data, ensure that your TiDB database is ready to receive it. This involves creating the necessary tables and schemas to match the transformed data. Use TiDB's SQL interface to define tables, columns, and data types that correspond to your transformed data.
Convert your transformed data into SQL INSERT statements or use TiDB's bulk data load features like the `LOAD DATA` command for CSV files. If using INSERT statements, make sure to handle batch processing to efficiently load large datasets.
After loading the data, perform validation checks to ensure data integrity and consistency. Compare row counts, perform spot checks on random data samples, and check for any transformation errors. Use SQL queries to validate data within TiDB.
For ongoing data synchronization, automate the process using cron jobs or scheduling tools. This involves automating the data extraction, transformation, and loading scripts to regularly update the TiDB database with new data from Adjust. Ensure error handling and logging are in place for monitoring and troubleshooting.
By following these steps, you can manually transfer and synchronize data from Adjust to TiDB while maintaining control over the process 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:
