How to Extract my Pipedrive data?

Pipedrive exposes its complete platform to developers through its API. As a Web API following the RESTful architecture principles, it can be accessed through HTTP.

As a RESTful API, interacting with it can be achieved using tools like CURL or Postman or by using HTTP clients for your favorite language or framework. A few suggestions:

• Apache HttpClient for Java
• Spray-client for Scala
• Hyper for Rust
• Ruby rest-client
• Python http-client

Pipedrive API Authentication

Pipedrive API Authentication is API-Key based. You acquire an API Key from the platform and you can use it to authenticate to the API securely. All the calls are executed over secure HTTPS.

Pipedrive Rate Limiting

Rate limiting is considered per API token. API allows performing 100 requests per 10 seconds.

Every API response includes the following headers:

1. X-RateLimit-Limit: the number of requests the current API token can perform for the 10 seconds window.
2. X-RateLimit-Remaining: the number of requests left for the 10 seconds window.
3. X-RateLimit-Reset: the amount of seconds before the limit resets.

Suppose the limit is exceeded for the time window. In that case, the Pipedrive API will return an error response with HTTP code 429 and Retry-After header that will indicate the number of seconds before the limit resets.

Endpoints and Available Resources

Pipedrive exposes a large number of endpoints from which we can interact with the platform. These endpoints can be used to execute commands like adding a new person to our contact list and pulling data from it. A unique characteristic of the Pipedrive API is that a companion resource manages the custom fields that you might have created for the resource for many of the resources. In this way, maximum flexibility is offered to the users of the platform. The list of available resources follows:

• Activities: Activities are appointments, tasks and events in general that can be associated with a deal and your sales pipeline.
• Activity Fields: custom fields created for your activities.
• Activity Types: user-defined types for your activities
• Authorization: Authorization objects can be fetched without an API token but using an email and password.
• Currencies: Supported currencies that can represent the monetary value of a Deal or a value of any monetary type custom field.
• Deals: Deals represent ongoing, lost, or won sales to an organization or to a Person.
• Deal Fields: DealFields represent the near-complete schema for a Deal in the context of the company of the authorized user.
• Email Messages: EmailMessages represent e-mail messages sent or received through Pipedrive designated e-mail account.
• Email Threads: EmailThreads represent e-mail message threads that contain individual e-mail messages.
• Files: Files are documents of any kind (images, spreadsheets, text files, etc.) that are uploaded to Pipedrive
• Filters: Each filter is essentially a set of data validation conditions.
• Goals: Goals help your team meet your sales targets.
• Mail Messages: MailMessages represent mail messages synced with Pipedrive using the 2-way sync or the Smart Email BCC feature.
• MailThreads: MailThreads represent mail threads that contain individual mail messages.
• Notes: Notes are pieces of textual (HTML-formatted) information that can be attached to Deals, Persons, and Organizations.
• Note Fields: Custom fields for Notes.
• Organizations: Organizations are companies and other kinds of organizations you are making Deals with.
• Organization Fields: OrganizationFields represent the near-complete schema for an Organization in the context of the company of the authorized user.
• Persons: Persons are your contacts, the customers you are doing Deals with
• Person Fields: Custom fields for persons.
• Pipelines: Pipelines are essentially ordered collections of Stages.
• Products: Products are the goods or services you are dealing with.
• Product fields: ProductFields represent the near-complete schema for a Product.
• Stages: Stage is a logical component of a Pipeline and essentially a bucket that can hold many Deals.
• Users: Users are people with access to your own Pipedrive account.

For a detailed list of all endpoints together with a way to make requests to them without a client to see every data they return, if you have a Pipedrive account. Please check here.

It is clear that with such a rich platform and API all data that can be pulled out of Pipedrive are both valuable and come in large quantities. So, let’s assume that we want to pull all the persons out of Pipedrive to use the associated data for further analysis. To do so we need to make a GET request with your favorite client to the Persons’ endpoint like this.

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GET https://api.pipedrive.com/v1/persons?start=0&api_token=YOUR_KEY

The response headers and the actual response will look like the following:

Response Header

JSON
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{
    "server": "nginx",
    "date": "Tue, 06 Sep 2016 15:46:38 GMT",
    "content-type": "application/json",
    "transfer-encoding": "chunked",
    "connection": "keep-alive",
    "x-frame-options": "SAMEORIGIN",
    "x-xss-protection": "1; mode=block",
    "x-ratelimit-limit": "100",
    "x-ratelimit-remaining": "99",
    "x-ratelimit-reset": "10",
    "access-control-allow-origin": "*"
}

Response

JSON
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{
    "success": true,
    "data": [
        {
            "id": 1,
            "company_id": 1180166,
            "owner_id": {
                "id": 1682699,
                "name": "Kostas",
                "email": "costas.pardalis@gmail.com",
                "has_pic": true,
                "pic_hash": "39bf355364aacbde4fdfed3cef8a4589",
                "active_flag": true,
                "value": 1682699
            },
            "org_id": null,
            "name": "Fotiz",
            "first_name": null,
            "last_name": "Fotiz",
            "open_deals_count": 0,
            "closed_deals_count": 0,
            "participant_open_deals_count": 0,
            "participant_closed_deals_count": 0,
            "email_messages_count": 0,
            "activities_count": 0,
            "done_activities_count": 0,
            "undone_activities_count": 0,
            "reference_activities_count": 0,
            "files_count": 0,
            "notes_count": 0,
            "followers_count": 1,
            "won_deals_count": 0,
            "lost_deals_count": 0,
            "active_flag": true,
……

Inside the response, there will be an array of objects with each one representing one Person as it is represented in Pipedrive. Please note that all data is serialized in JSON.

After successfully pulling your data using the Pipedrive API, you are ready to extract and prepare them for BigQuery. Of course, the above process is only for one of the available resources, if you would like to have a complete view of all the available data, then you will have to create a much more complex ETL process including the majority of the resources that Pipedrive has. Alternatively, you can check RudderStack, which can simplify the whole process and you can have your own Pipedrive data available for analysis in a few minutes.

How can I prepare my data to be sent from Pipedrive to Google BigQuery?

Before you load your data into BigQuery, you should make sure that it is presented in a format supported by it. For example, if the API you pull data from returns XML, you must first transform it into a serialization BigQuery understands. Currently, two data formats are supported:

• CSV, and
• JSON

You also need to make sure that any data types you are using are the ones supported by BigQuery, which are the following:

• STRING
• INTEGER
• FLOAT
• BOOLEAN
• RECORD
• TIMESTAMP

for more information, please check the Preparing Data for BigQuery page on the documentation.

Load Data from Pipedrive to Google BigQuery

If you want to load data from Pipedrive to BigQuery, you have to use one of the following supported data sources.

1. Google Cloud Storage
2. Sent data directly to BigQuery with a POST request
3. Google Cloud Datastore Backup
4. Streaming insert
5. App Engine log files
6. Cloud Storage logs

From the above list of sources, 5 and 6 are not applicable in our case.

For Google Cloud Storage, you first must load your data into it, there are a few options on how to do this, for example, you can use the console directly as it is described here and do not forget to follow the best practices. Another option is to post your data through the JSON API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse.. In its simplest case, it’s just a matter of one HTTP POST request using a tool like CURL or Postman. It should look like the following example.

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POST /upload/storage/v1/b/myBucket/o?uploadType=media&name=myObject 
HTTP/1.1 Host: www.googleapis.com 
Content-Type: application/text 
Content-Length: number_of_bytes_in_file 
Authorization: Bearer your_auth_token your Pipedrive data

and if everything went ok, you should get something like the following as a response from the server:

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HTTP/1.1 200 Content-Type: application/json { "name": "myObject" }

Working with Curl or Postman is good only for testing. If you would like to automate the process of loading your data to Bigquery, you should write some code to send your data to Google Storage in the Cloud. In case you are developing on the Google App Engine you can use the library that is available for the languages that are supported by it:

1. Python
2. Java
3. PHP
4. Go

If you are using one of the above languages and you are not coding for the Google App Engine, you can use it to access the Storage in the Cloud from your environment. Interacting such a feature-rich product like Google Cloud Storage can become quite complicated depending on your use case, for more details on the different options that exist you can check Cloud Storage documentation from Google. If you are looking for a less engaged and more neutral way of using Storage in the Cloud, you can consider a solution like RudderStack.

After you have loaded your data in Google Cloud Storage, you have to create a Load Job for BigQuery to actually load any data into it, this Job should point to the source data in Cloud Storage that have to be imported, this happens by providing source URIs that point to the appropriate objects.

The previous method described, used a POST request to the Google Storage API in Cloud for storing data there and then load it into BigQuery. Another way to go is to do a direct HTTP POST request to BigQuery with the data you would like to query. This approach is similar to how we loaded data to Cloud Storage through the JSON API, but it uses the appropriate end-points of BigQuery for loading the data there directly. The way to interact with it is quite similar, for more information can be found on the BigQuery API Reference and on the page that describes how you can load data into BigQuery using POST. You can interact with it using the HTTP client library of the language or framework of your choice, a few options are:

• Apache HttpClient for Java
• Spray-client for Scala
• Hyper for Rust
• Ruby rest-client
• Python http-client

The best way to load data from Pipedrive to Google BigQuery

So far we just scraped the surface of what you can do with BigQuery and how you can load data you own into it. Things can get even more complicated if you want to integrate data coming from different sources.

Are you striving to achieve results right now?

Instead of writing, hosting, and maintaining a flexible data infrastructure use RudderStack that can handle everything automatically for you.

RudderStack with one click integrates with sources or services, creates analytics-ready data, and syncs your Pipedrive to BigQuery right away.