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First, log in to your Workable account and navigate to the data or reports section. Use the built-in export functionality to download your desired data as a CSV or Excel file. This file will serve as the base data for migration to Databricks Lakehouse.
Ensure that you have Python and any necessary libraries installed on your local machine. Python is commonly used for data manipulation tasks, and you'll likely need libraries such as pandas for handling your CSV or Excel files. Install any required packages using pip, e.g., `pip install pandas`.
Load the exported data file into a pandas DataFrame. Perform any necessary data cleaning or transformations locally. This might include handling missing values, renaming columns, or converting data types to match the schema you plan to use in Databricks Lakehouse.
Access your Databricks Lakehouse environment through your Databricks account. Create a new cluster if necessary, ensuring it is running and ready to accept data. Set up any required configurations for data import, such as defining the expected schema and creating any necessary database tables.
Use the Databricks web interface to upload your cleaned data file to the Databricks File System. Navigate to the "Data" tab, select "Add Data," and choose the option to upload your file directly from your local system to DBFS.
Once your data file is on DBFS, use a Databricks notebook to load the data into a DataFrame. Utilize Spark’s `read` functions to import the data from the CSV or Excel file stored in DBFS. For example, use `spark.read.csv("/dbfs/path/to/your/file.csv")` to read the data into a Spark DataFrame.
Verify the integrity and accuracy of the imported data by performing exploratory data analysis in a Databricks notebook. Once verified, write the DataFrame to a table in your Databricks Lakehouse using the `write` method. For instance, use `dataframe.write.format("delta").saveAsTable("your_table_name")` to save your data in the Delta Lake format, ensuring it is stored and accessible for analytics and further processing.
By following these steps, you can manually transfer data from Workable to your Databricks Lakehouse environment, ensuring clean and accurate data migration without relying on third-party tools.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
