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Begin by understanding the data structure in Adjust. Familiarize yourself with the metrics, dimensions, and event data that Adjust provides. Export a sample dataset manually from Adjust to review its format, such as CSV or JSON.
Utilize Adjust’s built-in export feature to manually download the data you need. This could be done through the Adjust dashboard where you can select the desired data set and export it in a format such as CSV or JSON. Make sure to select relevant date ranges and required fields.
After exporting, clean and format the data to ensure it matches the schema requirements of Starburst Galaxy. This may involve renaming columns, converting data types, or removing unnecessary fields. Use tools like Excel or scripts in Python or shell to perform these transformations.
Log in to your Starburst Galaxy account and ensure your environment is ready to receive data. Create a new schema or table structure in Starburst Galaxy that matches the structure of your prepared data. Define the data types and constraints in Starburst Galaxy to match your dataset.
Use a secure method like SCP (Secure Copy Protocol) or SFTP (Secure File Transfer Protocol) to manually transfer the prepared data file from your local machine or server to the Starburst Galaxy environment. Ensure that Starburst Galaxy’s storage layer is accessible from your machine.
With the data file now in your Starburst Galaxy environment, utilize the Starburst Galaxy console or CLI to load the data into the defined tables. Execute SQL commands in Starburst to import the data file. Ensure that you handle any errors or exceptions during the load process.
After the data is loaded into Starburst Galaxy, run queries to verify that the data transfer was successful and accurate. Compare row counts and sample data between Adjust and Starburst Galaxy. Check that all fields have been imported correctly and that no data is missing or corrupted.
By following these steps, you can manually move data from Adjust to Starburst Galaxy without relying on third-party connectors or integrations, ensuring control and flexibility in the data transfer process.
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:
