> For the complete documentation index, see [llms.txt](https://docs.coresignal.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.coresignal.com/introduction/delivery-formats.md).

# Delivery Formats

Access Coresignal's fresh B2B data via flat files (JSONL, Parquet, CSV) or APIs. Supports S3, Azure, GCS delivery.

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 **JSONL, Parquet or CSV formats:**

| Dataset        | Delivery format |
| -------------- | --------------- |
| Company Posts  | JSONL; Parquet  |
| Employee Posts | JSONL; Parquet  |
| Company        | JSONL; Parquet  |
| Employee       | JSONL; Parquet  |
| Jobs           | JSONL; Parquet  |

{% hint style="info" %}
We are constantly improving our delivery capabilities. If you do not find a preferred method or format, contact us.
{% endhint %}

## 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.

## Recommended tools

{% hint style="info" %}
We can only offer general solutions since it depends on the tech stack you use or what you prefer using.
{% endhint %}

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                          | <p><a href="https://www.mongodb.com/docs/manual/">Mongo DB</a></p><p><a href="https://docs.couchbase.com/home/index.html">Couchbase</a></p><p><a href="https://www.postgresql.org/docs/">PostgreSQL</a></p><p><a href="https://cassandra.apache.org/_/index.html">Apache Cassandra</a></p><p><a href="https://docs.aws.amazon.com/redshift/?icmpid=docs_homepage_analytics">Amazon Redshift</a></p><p><a href="https://docs.aws.amazon.com/s3/?icmpid=docs_homepage_featuredsvcs">Amazon S3</a> + <a href="https://docs.aws.amazon.com/athena/?icmpid=docs_homepage_analytics">Athena</a></p><p><a href="https://www.elastic.co/elasticsearch">Elasticsearch</a></p> |
| Data processing frameworks                | <p><a href="https://spark.apache.org/docs/latest/">Apache Spark</a></p><p><a href="https://hadoop.apache.org/docs/current/">Apache Hadoop</a></p>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
| Data ingestion tools                      | <p><a href="https://nifi.apache.org/documentation/">Apache NiFi</a></p><p><a href="https://cloud.google.com/bigquery/?utm_source=google&#x26;utm_medium=cpc&#x26;utm_campaign=emea-emea-all-en-dr-bkws-all-all-trial-e-gcp-1707574&#x26;utm_content=text-ad-none-any-DEV_c-CRE_683760970761-ADGP_Hybrid+%7C+BKWS+-+EXA+%7C+Txt+-+Data+Analytics+-+BigQuery+-+v1-KWID_43700078882901453-kwd-63326440124-userloc_9062284&#x26;utm_term=KW_google%20bigquery-NET_g-PLAC_&#x26;&#x26;gad_source=1&#x26;gclid=CjwKCAjwjqWzBhAqEiwAQmtgT_YDxbhoa9HU9m1P8VqZqtyO1esrm4j0F-dmDNxirswc4LeVn5aDtxoCYioQAvD_BwE&#x26;gclsrc=aw.ds#how-it-works">Google BigQuery</a></p>         |
| Data ETL (Extract, Transform, Load) tools | <p><a href="https://docs.aws.amazon.com/prescriptive-guidance/latest/serverless-etl-aws-glue/aws-glue-etl.html">AWS Glue</a></p><p><a href="https://www.talend.com/knowledge-center/">Talend</a></p>                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
| Data transformation                       | <p><a href="https://docs.getdbt.com/">dbt</a></p><p><a href="https://pandas.pydata.org/docs/">Pandas</a></p>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |


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