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Begin by logging into your Workable account. Navigate to the section where your data is stored, such as candidate profiles or job postings. Use the export feature to download the data you need. Typically, this data can be exported in CSV or Excel format. Ensure that you export all relevant fields that you want to transfer to Weaviate.
Open the exported data file in a spreadsheet application like Microsoft Excel or Google Sheets. Clean and structure the data to match Weaviate's schema requirements. This includes ensuring that each column corresponds to a particular property or attribute in Weaviate. Validate data types (e.g., text, numbers, dates) and remove any unnecessary columns or rows.
If you haven’t already, set up a Weaviate instance. You can do this by deploying Weaviate on a server or using a cloud service. Follow the Weaviate documentation to install and configure your instance. Make sure it is accessible and ready to receive data.
In your Weaviate instance, define a schema that matches the structure of your data. This involves creating classes and properties that correspond to the columns in your prepared data file. Use the Weaviate RESTful API or the Weaviate Console to create and configure the schema.
Convert your cleaned and structured data into JSON format, as Weaviate accepts data in this format for import. Each row of your data should be converted into a JSON object, with keys corresponding to the property names defined in your Weaviate schema. You can write a script in Python or another language to automate this conversion.
Use the Weaviate API to upload your JSON data to your Weaviate instance. This can be done using HTTP POST requests to the Weaviate data endpoint. You may need to write a script to loop through your JSON objects and send them individually or in batches. Ensure that your API requests are correctly authenticated if required.
After importing, verify that the data in Weaviate matches your original dataset. Use Weaviate's search and query capabilities to check that all records are present and correctly structured. Validate that all relationships and attributes are properly set. If discrepancies are found, review your data conversion and import steps, and make necessary corrections.
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.
Workable is a cloud-based recruitment software that helps businesses streamline their hiring process. It offers a range of tools to help companies manage job postings, applicant tracking, candidate communication, and interview scheduling. Workable also provides features such as resume parsing, candidate scoring, and background checks to help businesses make informed hiring decisions. The platform integrates with popular job boards and social media sites, making it easy for companies to reach a wider pool of candidates. Workable is designed to be user-friendly and customizable, allowing businesses to tailor the software to their specific needs.
Workable's API provides access to a wide range of data related to recruitment and hiring processes. The following are the categories of data that can be accessed through Workable's API:
1. Candidates: Information about candidates who have applied for a job, including their name, contact details, resume, cover letter, and application status.
2. Jobs: Details about the job openings, including the job title, description, location, salary, and hiring manager.
3. Hiring pipeline: Information about the hiring process, including the stages of the pipeline, the number of candidates in each stage, and the time spent in each stage.
4. Interviews: Details about the interviews conducted with candidates, including the date, time, location, interviewer, and feedback.
5. Reports: Analytics and insights related to recruitment and hiring processes, including the number of applications, the time to hire, and the cost per hire.
6. Integrations: Information about the third-party tools and services integrated with Workable, including the ATS, HRIS, and job boards.
Overall, Workable's API provides a comprehensive set of data that can help organizations streamline their recruitment and hiring processes and make data-driven decisions.
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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