So, you’re a Pipedrive user, right? It’s nice to talk to someone that knows that the customer plays the most pivotal role in the success of any business. 

At times, there would be a need to move your sales & marketing data from Pipedrive to a data warehouse. That’s where you come in. You take the responsibility of replicating data from Pipedrive to a centralized repository. By doing this, the analysts and key stakeholders can make super-fast business-critical decisions.

Give a high-five! We’ve prepared a simple and straightforward guide without beating around the bush. Leap forward and read the 2 simple methods for performing the data replication from Pipedrive to Redshift.

How to Replicate Data From Pipedrive to Redshift?

To replicate data from Pipedrive to Redshift, you can do either of the following:

  • Either use CSV files or 
  • A no-code automated solution. 

We’ll cover replication via CSV files next.

Replicate Data from Pipedrive to Redshift Using CSV Files

Pipedrive, being a CRM platform, stores data about deals, organizations, and people. You can combine and customize different types of data before exporting.

Follow along to replicate data from Pipedrive to Redshift in CSV format:

Step 1: Export CSV Files from Pipedrive 

Exporting from the detail view

You can only export data about individual deals from the detail view.

  • Click on the detail about which you want to export data.
  • Go to the deal’s detail view.
  • On the top left corner, click on the “…” icon. From the drop-down, select the “Export as XLS option.
  • A spreadsheet with information about the details of the deal along with any information about linked people or organizations will be downloaded.
Export as XLS for exporting data from Pipedrive
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  • Open the file, and go to the “Save As” dialog box. Select the location where you want to save the file. And from the file type drop-down menu, select “CSV.” Click on “Save.”

Exporting from the list view

From the list view, you can easily export custom reports about your deals, organizations or any other items in Pipedrive.

  • Go to the list view of any item.
  • Then, apply a filter that best fits the type the data you want to export. For example, if you want to export information about the won deals only, you need to select the filter “All won deals” to only pull up deals that fit that criteria.
Apllying Filters while exporting data from Pipedrive to Redshift
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  • Now, select the columns for which you want to export data.
Selecting columns before exporting data
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  • Now, click on the “…” icon in the top-right corner. From the drop-down list, select the “Export filter results” option. 

Now, Pipedrive will export all the information that fits the filter and columns that you have set.

Select the export filter results option
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Exporting from the “Export data” option

In this case, you can export data about a whole module or apply filters on any module and then export only specific data.

  • Navigate to the “Settings” option. Then, click on the “Export data” option.
  • The export from settings will allow you to export data related to deals, organizations, people, activities, or notes individually.
Click on the Export data option
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  • Select the type of data you want to export. Then click on the “CSV” option. 
  • Your export file will then appear in the “Exports available for download” list, with a green Download button. Then click on the ”Download” button.
Downloading the data ready for export
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Step 2: Import CSV Files into Redshift

  • Create a manifest file that contains the CSV data to be loaded. Upload this to S3 and preferably gzip the files.
  • Once loaded onto S3, run the COPY command to pull the file from S3 and load it to the desired table. If you have used gzip, your code will be of the following structure:
COPY <schema-name>.<table-name> (<ordered-list-of-columns>) FROM '<manifest-file-s3-url>' 

CREDENTIALS'aws_access_key_id=<key>;aws_secret_access_key=<secret-key>' GZIP MANIFEST;
  • You also need to specify any column arrangements or row headers to be dismissed, as shown below:
COPY table_name (col1, col2, col3, col4)
FROM 's3://<your-bucket-name>/load/file_name.csv'
credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'

-- Ignore the first line
COPY table_name (col1, col2, col3, col4)
FROM 's3://<your-bucket-name>/load/file_name.csv'
credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'

This process will successfully load your desired CSV datasets to Amazon Redshift in a pretty straightforward way.

The above 2-step guide replicates data from Pipedrive to Redshift effectively. It is optimal for the following scenarios:

  • Less Amount of Data: This method is appropriate for you when the number of reports is less. Even the number of rows in each report is not huge.
  • One-Time Data Replication: This method suits your requirements if your business teams need the data only once in a while.
  • Limited Data Transformation Options: Manually transforming data in CSV files is difficult & time-consuming. Hence, it is ideal if the data in your spreadsheets is clean, standardized, and present in an analysis-ready form. 
  • Dedicated Personnel: If your organization has dedicated people who have to perform the manual downloading and uploading of CSV files, then accomplishing this task is not much of a headache.
  • Coding Knowledge: For doing this replication, you need to have some knowledge of writing COPY commands in Redshift.

However, when the frequency of replicating data from Pipedrive increases, this process becomes highly monotonous. It adds to your misery when there is a need to transform the raw data every single time. With the increase in data sources, you would have to spend a significant portion of your engineering bandwidth creating new data connectors. Just imagine — building custom connectors for each source, transforming & processing the data, tracking the data flow individually, and fixing issues. Doesn’t it sound exhausting?

Rather, how about you focus on more productive tasks than repeatedly writing custom ETL scripts, downloading, cleaning, and uploading CSV files? This sounds better, right?

In that case, you can… 

Replicate Data from Pipedrive to Redshift Using an Automated ETL Tool

An automated tool is an efficient and economical choice that takes away a massive chunk of repetitive work. It has the following benefits:

  • It allows you to focus on core engineering objectives. By doing so, your business teams can jump on to reporting without any delays or data dependency on you.
  • Your support team can effortlessly filter, aggregate, and segment data from Pipedrive.
  • Without technical knowledge, your analysts can seamlessly standardize timezones, convert currencies, or simply aggregate campaign data for faster analysis. 
  • An automated solution provides you with a list of native in-built connectors. No need to build custom ETL connectors for every source you require data from.

For instance, here’s how Hevo Data, a cloud-based ETL solution makes the data replication from Pipedrive to Redshift ridiculously easy: 

Step 1: Configure Pipedrive as your Source

  • Fill in the required credentials required for configuring Pipedrive as your source.

Step 2: Configure Redshift as your Destination

Now, you need to configure Redshift as the destination.

Configuring your Amazon Redshift destination in Hevo Data
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After implementing the 2 simple steps, Hevo will take care of building the pipeline for replicating data from Pipedrive to Redshift based on the inputs given by you while configuring the source and the destination.

You don’t need to worry about security and data loss. Hevo’s fault-tolerant architecture will stand as a solution to numerous problems. It will enrich your data and transform it into an analysis-ready form without having to write a single line of code.

Here’s what makes Hevo stands out from the rest:

  • Fully Managed: You don’t need to dedicate time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
  • Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
  • Faster Insight Generation: Hevo offers near real-time data replication, giving you access to real-time insight generation and faster decision-making. 
  • Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
  • Scalable Infrastructure: With the increased number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
  • Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website, along with all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
  • Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

You can take our 14-day free trial to experience a better way to manage data pipelines.

Get started for Free with Hevo!

What Can You Achieve by Replicating Your Data from Pipedrive to Redshift?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Pipedrive to Redshift. Does your use case make the list?

  • What is the Marketing Behavioural profile of the Product’s Top Users?
  • What message would take a customer from one lifecycle stage to another?
  • How likely is the lead to purchase a product?
  • What was the survey’s Engagement Rate of App Users by channel?

Summing It Up 

Exporting & uploading CSV files is the go-to solution for you when your data analysts require fresh data from Pipedrive only once in a while. But with an increase in frequency, redundancy will also increase. However, to focus your time on doing productive tasks, you can prefer an automated solution. This automated solution would be able to accommodate your frequent data replication needs. This would be genuinely helpful to sales & marketing teams as they would need regular updates about marketing & sales expenses, effects of custom messaging, and how to close their sales deals, etc.

Even better, your sales teams would now get immediate access to data from multiple channels and thus deep-dive to explore better market opportunities.

Now, you don’t need to bite the bullet and spend months developing & maintaining custom data pipelines. You can make all hassle go away in minutes by taking a ride with Hevo Data’s automated no-code data pipeline.

Hevo’s 150+ plug-and-play native integrations will help you replicate data smoothly from multiple tools to a destination of your choice. Its intuitive UI will help you smoothly navigate through its interface. And with its pre-load transformation capabilities, you don’t even need to worry about manually finding errors and cleaning & standardizing them.

With a no-code data pipeline solution at your service, companies will spend less time calling APIs, referencing data, building pipelines, and more time gaining insights from their data.

Skeptical? Why not try Hevo for free and take the decision all by yourself? Using Hevo’s 14-day free trial feature, you can build a data pipeline from Pipedrive to Redshift and try out the experience.

Here’s a short video that will guide you through the process of building a data pipeline with Hevo.

We’ll see you again the next time you want to replicate data from yet another connector to your destination. That is if you haven’t switched to a no-code automated ETL tool already.

We hope you have found the appropriate answer to the query you were searching for. Happy to help!

Former Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has written more than 100 articles on diverse topics related to data industry.

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