How to load data from Pipedrive to Google BigQuery

Blendo Team

This post helps you with loading your data from Pipedrive to BigQuery. If you are looking to get analytics-ready data without the manual hassle you can integrate Pipedrive to BigQuery with Blendo, so you can focus on what matters, getting value out of your customer and sales data.

How to Extract my Pipedrive data?

Pipedrive exposes its complete platform to developers through their 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 by using tools like CURL or Postman or by using HTTP clients for your favorite language or framework. A few suggestions:

You will need more time to read this post than integrating Pipedrive to BigQuery.

Effortlessly Sync All Your Pipedrive Data to BigQuery

Pipedrive API Authentication

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

Pipedrive rate limiting

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

In case the limit is exceeded for the time window, the Pipedrive API will return an error response with HTTP code 429 and Retry-After header that will indicate the amount 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 both execute commands like adding a new person to our contact list but also to pull data from it. A unique characteristic of the Pipedrive API is that for many of the resources a companion resource exists which manages the custom fields that you might have created for the resource. 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 which can be used to 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 which 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 that are being synced with Piperive using the 2-way sync or the Smart Email BCC feature.
  • MailThreads: MailThreads represent mail threads which 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 a number of Deals.
  • Users: Users are people with access to your own Pipedrive account.

For a detail 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 favourite client, to the Persons’ endpoint like this.

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:

{
    "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": "*"
}

 

{
    "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 are serialized in JSON.

After you have successfully pulled 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 complex ETL process including the majority of the resources that Pipedrive has. Alternatively, you can check how Blendo which can simplify the whole process and you can have your own Pipedrive data available for analysis in a matter of a few minutes.

What is Pipedrive?

Pipedrive is a sales management tool designed to help small sales teams manage intricate or lengthy sales processes successfully. Based on a philosophy of activity-based selling, it ensures teams focus on the actions that drive deals to close. Some of the differences of Pipedrive to other CRM and sales management platforms are the following:

  • Dedicated Sales Management Platform. CRM is more generic. Pipedrive instead focuses more on offering a great experience for sales teams specifically.
  • Pipeline focused. Instead of focusing on numbers and metrics, Pipedrive focuses on the sales pipeline which is the overall process that your company will be running for acquiring new customers.
  • Optimised data experience. While CRMs are all about user data, Pipedrive is built in such a way that the minimal time is spent on data entry, so your salesmen can focus on selling instead of maintaining data.

Pipedrive focuses more on smaller teams and companies and it tries to address the unique problems that these might have, although it can also be found inside bigger companies. Such a great product couldn’t be built without a solid back-end and an excellent API. This API will be also used in this article to show how we can pull out valuable data out of Pipedrive.

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, so for example, if the API you pull data from returns XML you have to first transform it into a serialization that BigQuery understands. Currently, two data formats are supported:

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.

What is Google BigQuery?

BigQuery is the data warehousing solution of Google. It’s part of the Google Cloud Platform and it also speaks SQL like Redshift does. Queries are executed against append-only tables using the processing power of Google’s infrastructure.

It is also fully managed and is offered as a service over the cloud. You can interact with it through its web UI, using a command line tool while a variety of client libraries exist so you can interact with it through your application.

Data load 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 it’s 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.

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:

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 Blendo.

After you have loaded your data in a 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:

 

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.

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