Company Insights
Overview
Complete, worldwide company database of over 30 million businesses. Includes basic company information such as name, location, category, and industry, along with a number of aggregated trends such as employee count and average tenure.
| Data Information | Value |
|---|---|
| Refresh Cadence | Adhoc |
| Historical Coverage | 2010 - Present |
| Geographic Coverage | Global |
Schema
People and Resume Data is a Multi-Table dataset which allows you to access just the data you need. PDL.COMPAY.COMPANY is the primary table. You can join additional tables using the COMPANY_ID identifier. The schema below is for the PDL.COMPANY.COMPANY table. For more information on how to access Multi-Table datasets via API, review our docs page.
| Name | Description |
|---|---|
| LATEST_FUNDING_STAGE | The stage of the company’s most recent funding event |
| MIC_EXCHANGE | The MIC code for the company's ticker exchange |
| ULTIMATE_PARENT | The ID of the ultimate owning company |
| DATASET_VERSION | DATASET_VERSION |
| SIZE | Number of employees at the company (range) |
| LINKEDIN_ID | Main LinkedIn profile ID for the company |
| FOUNDED | The founding year of the company |
| TICKER | The company ticker for public companies |
| INDUSTRY | The self-reported industry of the company |
| COUNT_LINKEDIN_FOLLOWER | Count of LinkedIn followers |
| LINKEDIN_SLUG | LinkedIn slug for the company |
| AVERAGE_EMPLOYEE_TENURE | Average years of employee tenure |
| COUNT_FUNDING_ROUNDS | Number of funding rounds announced |
| GICS_SECTOR | GICS sector classification for public companies |
| COMPANY_ID | Unique identifier for the company |
| ULTIMATE_PARENT_TICKER | Ultimate parent's stock symbol (if public) |
| LINKEDIN_URL | Main LinkedIn profile URL for the company |
| NAME | Company's main common name |
| DISPLAY_NAME | Company's displayed name |
| TOTAL_FUNDING_RAISED | Total amount of funding raised in USD |
| EMPLOYEE_COUNT | Current number of employees |
| LAST_FUNDING_DATE | Date of the most recent funding event |
| TYPE | Company type |
| FACEBOOK_URL | Main Facebook profile URL for the company |
| WEBSITE | Primary company website |
| IMMEDIATE_PARENT | Direct owner of the company |
| ULTIMATE_PARENT_MIC_EXCHANGE | MIC exchange of the ultimate parent (if public) |
| TWITTER_URL | Main Twitter profile URL for the company |
| SUMMARY | Company description |
| INFERRED_REVENUE | Estimated annual revenue in USD |
| HEADLINE | Company's headline summary |
Key Concepts
Reading in PySpark
People Data Labs Company data has a slightly tricky encoding with multiple lines and non-escaped quotes inside quotes that really trip PySpark up (Pandas handles it fine).
You can use this way of reading the data:
df = spark.read.option("header", "true")\
.option("multiLine", "true")
.option("escape", """)
.option("quote", '"')
.csv(path-do-company-data)Updated about 1 month ago