Delivery Formats

We seek to provide multiple ways to access our data, ensuring that your team can get information in the right format and at the right time.

Delivery options

We generally offer two options: flat file datasets and access data via API. Depending on the project scope and size, you can choose the option that best suits your needs.

Delivery option	Sources	Description
Flat files: download the dataset using a web link	All sources	We provide you with the link and login credentials for you to retrieve the data
Flat files: uploaded data file to your cloud server (S3, Azure, Google Cloud, etc.)	All sources	Provide your storage credentials, and we will send the data to you
APIs: get data using available APIs	Company, Employee, Jobs data	Access data by sending API requests

Get a flat-file dataset

We offer nine different flat-file datasets for businesses. Datasets are available in JSON, JSONL, CSV, or Parquet formats:

Dataset	Delivery format
Base Company	JSONL; CSV; JSON
Base Employee	JSONL; Parquet; CSV
Employee Posts	JSONL; Parquet
Base Jobs	JSONL; Parquet; CSV
Clean Company	JSONL; Parquet; CSV
Clean Employee	JSONL; Parquet; CSV
Multi-source Company	JSONL; Parquet
Multi-source Employee	JSONL; Parquet
Multi-source Jobs	JSONL; Parquet

We are constantly improving our delivery capabilities. If you do not find a preferred method or format, contact us.

Access the data via API

Data access via our API provides a freshly collected dataset in JSON format that can be analyzed using Python, Ruby, PHP, or any other preferred scripting language.

Try out the data for free

Our data speaks for itself, and we are excited to show it to you.

If you are unsure which dataset works best for you, try it out.

Start now

Set up a free account and get 400 search and 200 collect credits to check more than one billion company, employee, and job posting records.

Once you create your account, you will have 14 days to explore the data.

Recommended tools

We can only offer general solutions since it depends on the tech stack you use or what you prefer using.

Ingesting large datasets can be efficiently managed using a combination of tools and technologies tailored to handle big data workloads.

Tool category	Tool example
Database systems	Mongo DB Couchbase PostgreSQL Apache Cassandra Amazon Redshift Amazon S3 + Athena Elasticsearch
Data processing frameworks	Apache Spark Apache Hadoop
Data ingestion tools	Apache NiFi Google BigQuery
Data ETL (Extract, Transform, Load) tools	AWS Glue Talend
Data transformation	dbt Pandas