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Begin by exporting the data from Zenloop. Zenloop may provide an option to export data in formats like CSV or JSON. Use the platform's built-in export functionality to download the data to your local machine in a structured format.
Prepare an environment for Apache Iceberg. Install a compatible version of Apache Iceberg in your local or cloud-based Apache Spark or Hadoop environment. Ensure that your environment is configured correctly for Iceberg, including setting up necessary configurations in your `spark-defaults.conf` or Hadoop configuration files.
Use a programming language like Python or Scala to transform the exported CSV or JSON data into Parquet format, which is columnar and optimized for performance in Iceberg. You can use libraries like Pandas with PyArrow in Python to read the data and convert it to Parquet files.
Define the schema for your Iceberg table. This should mirror the structure and data types of your exported Zenloop data. Write a schema definition in SQL or use Apache Spark to create the table schema, ensuring it matches the transformed Parquet data.
Load the transformed Parquet data files into the Iceberg table. Use Apache Spark to read the Parquet files and write the data to the Iceberg table using Spark SQL. This involves using Spark to create a DataFrame from the Parquet files and executing a write command to populate the Iceberg table.
After loading the data, optimize the Iceberg table for performance by compacting small files and ensuring optimal data layout. Use Iceberg’s built-in methods to perform operations like data compaction and partitioning to enhance query performance.
Finally, verify the integrity and consistency of the data within your Iceberg table. Run validation checks by querying the Iceberg table using Spark SQL to ensure the data matches the original export from Zenloop. Check for data accuracy and completeness to confirm successful migration.
By following these steps, you can efficiently move data from Zenloop 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.
To sync data the Zenloop API can assist both full refresh and incremental for both answer endpoints. One can select this connector that will copy only the new or updated data, or all rows in the tables and columns you establish for replication, a sync is always run. Zenloop combines perfect customer relationships and it is an integrated experience management floor which based on the Net Promoter Score. The Zenloop API contributes programmatic entry and integration to a customer feeback platform.
Zenloop's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Zenloop's API are:
1. Feedback data: This includes all the feedback received from customers through various channels such as email, web forms, and social media.
2. Customer data: This includes information about customers such as their name, email address, phone number, and other contact details.
3. Survey data: This includes data related to surveys conducted by the company to gather feedback from customers.
4. Net Promoter Score (NPS) data: This includes data related to the NPS score of the company, which is a measure of customer satisfaction and loyalty.
5. Sentiment analysis data: This includes data related to the sentiment of customer feedback, which can help companies understand the overall sentiment of their customers towards their products or services.
6. Analytics data: This includes data related to customer behavior, such as the number of visits to the company's website, the time spent on the website, and the pages visited.
Overall, Zenloop's API provides access to a wide range of data that can help companies gain insights into customer feedback and satisfaction, and make data-driven decisions to improve their products and services.
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:
