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First, familiarize yourself with the data export capabilities in Adjust. Adjust allows you to export raw data through their Data Export feature. Identify the specific data you need and the format in which Adjust can provide it, typically in CSV format.
Use Adjust's dashboard to export the required data. Depending on your needs, you can schedule exports or manually download them. Ensure that data is exported in a CSV format, as this will be compatible with DuckDB.
Install DuckDB on your local machine or server where you plan to load the data. DuckDB is a self-contained database, so ensure your system meets its requirements and that you have sufficient permissions to install and run it.
Before importing, review the exported CSV files for any inconsistencies or errors that could cause issues during import. Clean the data as necessary, ensuring correct delimiters, consistent data types, and no corrupt entries.
Launch DuckDB and create a schema that matches the structure of your Adjust data. Use DuckDB's SQL interface to define tables with appropriate data types that correspond to the CSV data columns. This step is crucial for smooth data ingestion.
Use the DuckDB SQL CLI to execute a COPY command to load the CSV data into the appropriate tables. For example:
```sql
COPY my_table FROM 'path/to/your/data.csv' (DELIMITER ',', HEADER TRUE);
```
This command loads the CSV file into the specified table, assuming the CSV has headers and uses a comma as a delimiter.
After loading the data, run SQL queries to verify that the data in DuckDB matches the original data from Adjust. Check for any discrepancies in row counts, data types, and values. Perform spot checks and summarize key metrics to ensure data integrity.
By following these steps, you'll be able to move data from Adjust to DuckDB directly, without relying on any 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:
