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Begin by clearly identifying which data sets and fields need to be moved from Zenloop to Starburst Galaxy. Document the data structure, types, and volume to ensure you have a precise understanding of what needs to be migrated.
Use Zenloop's export functionality to download your data. Typically, you can export data in formats like CSV or Excel. Navigate to the data export section in Zenloop, select the required datasets, and choose the appropriate export format.
Once the data is exported, review it for any inconsistencies or errors. Clean the data by removing duplicates, correcting errors, and ensuring it matches the schema required by Starburst Galaxy. This step might involve standardizing formats, such as dates or numerical values.
Choose a secure method to transfer the data files to the environment where Starburst Galaxy is running. Secure File Transfer Protocol (SFTP) or Secure Copy Protocol (SCP) can be used for secure data transfer. Ensure you have the correct access credentials and permissions for the transfer.
Access the Starburst Galaxy environment and use its data import tools to upload the data files. Depending on your data source setup, you may need to use command-line tools, web interfaces, or APIs provided by Starburst Galaxy to ingest the data.
Once the data is uploaded, map it to the appropriate tables and fields in Starburst Galaxy. This step involves defining how the data fields from Zenloop correspond to the columns in your Starburst Galaxy tables. Use SQL or other data definition tools available in Starburst Galaxy for this task.
After the data is mapped and imported, conduct a thorough review to ensure all data is accurately transferred and properly formatted. Run queries to compare data points between Zenloop and Starburst Galaxy. Check for completeness and integrity to confirm that the migration was successful.
By following these steps carefully, you can manually move your data from Zenloop to Starburst Galaxy 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?
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