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Begin by accessing your Adjust account and navigating to the raw data export section. Adjust allows you to export raw data files based on specific criteria such as date range, event types, etc. Make sure you have the necessary permissions to access and download these datasets.
Configure the data export settings in Adjust to generate the data files you need. Choose the desired format, typically CSV or JSON, and set the parameters for the data you want to export. Schedule a one-time or regular export according to your data transfer needs.
Once the export is configured, download the raw data files from Adjust. This might be a manual download or an automated process if your organization has a script or tool to automate file downloads via Adjust’s API. Ensure that the files are stored securely in a location accessible for further processing.
Pre-process the downloaded data files to ensure they are in a format compatible with BigQuery. If you downloaded CSV files, ensure they are properly formatted with consistent delimiters. For JSON files, verify that the structure is correct. Check for any data cleaning needs, such as removing duplicates or correcting data types.
Log in to your Google Cloud Platform account and create a Google Cloud Storage bucket where you will upload the prepared data files. This serves as a staging area before loading data into BigQuery. Use the Google Cloud Console to create a bucket if one does not already exist.
Upload the pre-processed data files to the Google Cloud Storage bucket. You can use the Google Cloud Console interface to manually upload files or use the `gsutil` command-line tool for batch uploads. Ensure that the files are uploaded to the correct bucket and path within the storage.
Use BigQuery’s web UI, command-line tool, or API to load the data from Google Cloud Storage into BigQuery. Specify the data source (your Cloud Storage bucket), the destination dataset and table, and configure the schema if necessary. Execute the data load job and monitor its progress to ensure successful completion.
By following these steps, you can efficiently transfer data from Adjust to BigQuery 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:
