How to load data from Lever Hiring to Teradata

Learn how to use Airbyte to synchronize your Lever Hiring data into Teradata 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 Lever Hiring connector in Airbyte

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

Set up Teradata for your extracted Lever Hiring data

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

Configure the Lever Hiring to Teradata 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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How to Sync Lever Hiring to Teradata Manually

How to Sync Lever Hiring to Teradata 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.

The Lever Hire and Lever Nurture features allow leaders to scale and grow their people pipeline and build authentic and long-lasting relationships. The lever is a leading Talent Acquisition Suite that makes it easy for talent teams to reach their hiring goals and to connect companies with top talent. Lever hire is a complete talent acquisition suite that provides all the tools needed for businesses to discover and hire the best talents.

Lever Hiring's API provides access to a wide range of data related to the hiring process. The following are the categories of data that can be accessed through the API:

1. Candidates: Information about candidates who have applied for a job, including their name, contact details, resume, and application status.  
2. Jobs: Details about the job openings, including the job title, location, description, and requirements.  
3. Interviews: Information about the interviews scheduled for the candidates, including the date, time, location, and interviewer details.  
4. Offers: Details about the job offers made to the candidates, including the salary, benefits, and start date.  
5. Users: Information about the users who have access to the Lever Hiring platform, including their name, email address, and role.  
6. Teams: Details about the teams within the organization, including the team name, members, and roles.  
7. Stages: Information about the different stages of the hiring process, including the names and descriptions of each stage.  
8. Sources: Details about the sources from which the candidates have applied, including job boards, social media, and referrals.  

Overall, Lever Hiring's API provides a comprehensive set of data that can be used to streamline the hiring process and improve the overall efficiency of the recruitment process.

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 Lever Hiring to Teradata 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 Lever Hiring to Teradata 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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