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Begin by extracting data directly from Recurly using their RESTful API. Recurly provides a comprehensive API that allows you to pull data such as accounts, invoices, subscriptions, and transactions. Use HTTP GET requests to access the endpoints relevant to your data needs. Ensure you handle pagination and authentication (typically using API keys) to retrieve all necessary data.
Once you've extracted the data, transform it into a suitable format for processing. Typically, JSON or CSV formats work well. Use a scripting language like Python or JavaScript to parse the JSON responses from the API and convert them into a structured format, ensuring that data types and structures align with your end schema in Iceberg.
Set up your Apache Iceberg environment. This involves configuring your Hadoop/HDFS environment, or using a compatible cloud storage like AWS S3, Azure Blob Storage, or Google Cloud Storage. Install Apache Iceberg following their official documentation and ensure that your storage environment is correctly configured to support Iceberg tables.
Define the schema for your Iceberg tables that will store the Recurly data. The schema should match the structure and data types of the transformed data. Use SQL or a data definition language supported by your environment (e.g., Apache Spark SQL, Hive DDL) to create tables with appropriate partitioning and indexing strategies for efficient querying and storage.
Load the transformed data into the Iceberg tables. This can be done by writing a data ingestion script using Apache Spark or another supported processing engine. The script should read the transformed data files, apply any additional transformations if necessary, and write the data into the Iceberg tables, ensuring that the data types and structures align with the defined schema.
Once the data is loaded, verify its integrity and consistency. Run validation queries to ensure that the data in Iceberg matches what was extracted from Recurly. Check for any discrepancies or errors, such as missing records or incorrect data types, and adjust your extraction and transformation processes as needed to resolve these issues.
Finally, automate the process to ensure regular and consistent updates from Recurly to Iceberg. Use cron jobs, scheduled tasks, or workflow orchestrators like Apache Airflow to schedule regular data extraction, transformation, and loading processes. This ensures that your Iceberg tables remain up-to-date with the latest data from Recurly without manual intervention.
By following these steps, you can effectively move data from Recurly 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.
Recurly is an SaaS subscription billing management platform that powers over 2,000 brands, including Asana, BarkBox, Cinemark, Sling TV, and Twitch. Automating the repetitive task of sending recurring bills month after month, Recurly provides management for thousands of subscription-based businesses worldwide. Recurly is quick and easy to set up and integrate into existing systems, and sales include service support so merchants can get help as needed. Recurly is a powerful tool that reduces subscriber churn and increases business revenue.
Recurly's API provides access to a wide range of data related to subscription management and billing. The following are the categories of data that Recurly's API gives access to:
1. Accounts: Information about customer accounts, including contact details, billing information, and subscription status.
2. Subscriptions: Details about active and inactive subscriptions, including plan information, billing cycles, and renewal dates.
3. Transactions: Information about all transactions related to a customer's account, including payments, refunds, and credits.
4. Invoices: Details about all invoices generated for a customer's account, including invoice items, due dates, and payment status.
5. Plans: Information about the different subscription plans offered by a business, including pricing, features, and billing intervals.
6. Add-ons: Details about additional products or services that can be added to a subscription, including pricing and billing intervals.
7. Coupons: Information about discounts or promotions offered to customers, including coupon codes, expiration dates, and usage limits.
8. Metrics: Data related to subscription and revenue metrics, including churn rate, customer lifetime value, and monthly recurring revenue.
Overall, Recurly's API provides businesses with a comprehensive set of data to manage their subscription-based business models effectively.
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:
