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Begin by accessing the data you wish to move from Adjust. This is typically done by exporting reports or data files from the Adjust dashboard. Adjust provides CSV or JSON formatted data exports, which can be downloaded directly from the platform. Ensure you have the necessary permissions and API access if required.
Set up a local environment that can handle data processing tasks. This includes installing necessary tools like Python, Apache Spark, or other data processing libraries that can read and transform CSV or JSON files. Ensure your system has sufficient resources to process the data files.
Analyze the data structure from Adjust and determine how it maps to your Apache Iceberg schema. Use a script or tool to transform the data into the appropriate format. This step may involve data cleaning, type conversions, and ensuring the data matches the Iceberg table schema. Use Python scripts or Spark jobs to automate these transformations.
Set up Apache Iceberg on your data storage platform. Iceberg works with various storage systems like Hadoop, AWS S3, or Azure Data Lake. Install the necessary Iceberg libraries and configure them with your chosen storage backend. Ensure your environment is compatible with the version of Iceberg you plan to use.
With your transformed data ready, use Apache Spark or another compatible engine to load the data into Iceberg tables. Write Spark jobs that read the transformed data files and insert them into the Iceberg tables using the Iceberg API. This step involves specifying the target table and ensuring data is correctly partitioned and stored.
Once the data is loaded into Iceberg, perform checks to verify data integrity and consistency. This involves running queries to ensure that all data has been accurately transferred and that there are no discrepancies between the source data from Adjust and the data now stored in Iceberg. Use Iceberg's metadata tables to assist in this verification.
After successful data migration, implement strategies to optimize and maintain your Iceberg tables. This includes compacting small files, optimizing partitions, and configuring retention policies. Regularly monitor table performance and update strategies as necessary to ensure efficient query performance and storage utilization over time.
By following these steps, you can ensure a successful and direct data migration from Adjust to Apache Iceberg, 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:
