How to load data from Lever Hiring to Redshift

Learn how to use Airbyte to synchronize your Lever Hiring data into Redshift within minutes.

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

Set up Redshift for your extracted Lever Hiring data

Select where you want to import data from your 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 Redshift 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 to Manually

Step 1: Extract Data from Lever Hiring

Begin by accessing the Lever Hiring API. You'll need to authenticate using API keys or tokens provided by Lever. Make API calls to extract the necessary data, such as candidate information, job postings, and interview feedback. Ensure that you respect API rate limits and handle paginated responses if the data set is large.

Once you have the raw data from Lever, convert it into a format suitable for Redshift. This typically involves transforming JSON or XML data into CSV format, which is easily ingested by Redshift. During this transformation, clean the data by handling null values, ensuring consistent data types, and removing any unnecessary fields.

Before loading the data into Redshift, set up an Amazon S3 bucket to temporarily store the transformed CSV files. This step is crucial, as Redshift can directly copy data from S3. Ensure your S3 bucket is in the same AWS region as your Redshift cluster for optimal performance and avoid unnecessary data transfer costs.

Use AWS CLI, SDK, or web interface to upload your transformed CSV files to the S3 bucket. Organize the files in a structured manner, perhaps by date or data type, to make them easy to manage and retrieve later. Ensure that the appropriate permissions are set on the S3 bucket to allow Redshift to access it.

Ensure that your Amazon Redshift cluster is set up and accessible. Create the necessary database and tables in Redshift to match the structure of your transformed data. Define the schema precisely, considering data types and constraints to ensure data integrity upon loading.

Use the Redshift `COPY` command to load data from S3 into your Redshift tables. The `COPY` command is designed for high-performance data ingestion and supports various options that you can use to handle different data formats and compression types. Monitor the loading process for any errors and adjust your data transformation process if necessary.

After loading the data, perform thorough checks to ensure data integrity and completeness. Run queries to verify that all data has been transferred correctly and that no records are missing. Compare the data against the original data in Lever to confirm accuracy. Address any discrepancies by adjusting your extraction or transformation processes.