How to load data from Zenloop to BigQuery

Learn how to use Airbyte to synchronize your Zenloop data into BigQuery within minutes.

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Zenloop connector in Airbyte

Connect to Zenloop or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Zenloop data

Select BigQuery where you want to import data from your Zenloop source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Zenloop to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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How to Sync Zenloop to BigQuery Manually

How to Sync Zenloop to BigQuery Manually - Method 2:

FAQs

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.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Zenloop to BigQuery as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Zenloop to BigQuery and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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.

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