Sync data from
Greenplum Database to Freshsales

Model your Greenplum Database data using any of these methods

• dbt Model Selector

  

  Sync directly with your dbt models saved in a git.

• Looker

  

  Query using looks. Hightouch turns your look into SQL and will pull from your source.

• SQL Editor

  

  Create and Edit SQL from your browser. Hightouch supports SQL native to Greenplum Database.

• Table Selector

  

  Select available tables and sheets from Greenplum Database and sync using existing views without having to write SQL.

• Customer Studio

  

  For less technical users, pass traits and audiences from Greenplum Database using our visual segmentation builder.

Does this integration support in-warehouse planning?

Yes, if you integerate Greenplum Database and Freshsales using Hightouch, in-warehouse planning is supported.

Great, but what is in-warehouse planning?

Between every sync, Hightouch notices any and all changes in your data model. This allows you to only send updated results to your destination (in this case Freshsales). With the baseline setup, Hightouch picks out only the rows that need to be synced by querying every row in your data model before diffing using Hightouch’s infrastructure.

The issue here is this can be slow for large models.

Warehouse Planning allows Hightouch to do this diff directly in your warehouse. Read more on how this works here.

Why is it valuable to sync Greenplum Database data to Freshsales?

Freshsales is the backbone of your sales org. It's where you manage all of your contacts, accounts, and deals. The problem is that most of your CRM data has to be manually input by individual sales reps, so it doesn't show you an accurate 360-degree view of your customer. You only have access to basic information and historical interactions.

All of your Freshsales data, in addition to the rich behavioral data captured through your app/website already, exists in Greenplum Database. Most of the time, this includes core metrics your data team has defined around lifetime value, workspaces, subscriptions, playlists, average order value, last login date, etc. There's a high chance you're probably even consuming this data in a dashboard, and your sales reps can't answer key questions like:

• Which accounts have the highest lifetime value?
• Which users have the highest utilization in our product?
• When did user X last log in to the app?
• Which leads/accounts should I be prioritizing?
• Which accounts are at risk of churning?
• What is the average order value of account X?

Your sales team doesn't want to hop back and forth between your various SaaS tools to answer these questions. They want to take action in Freshsales, and that means enriching your CRM with data directly from your warehouse.

Why should you use reverse ETL to connect Greenplum Database and Freshsales data?

Conventionally moving data from Snowflake to Freshsales meant downloading ad hoc CSV files and uploading them manually or forcing your data team to integrate with the Freshsales API and build and maintain custom pipelines. In reality, CSVs are not scalable, and in-house data pipelines and custom scripts break constantly.

Other point-to-point solutions create a weave complex web of pipelines to and from various SaaS applications and customer data platforms (CDPs), forcing you to pay for another layer of storage in addition to your data warehouses.

Reverse ETL solutions like Hightouch query against Snowflake and sync that data directly to Freshsales. You don't have to worry about CSVs or APIs. You can leverage your existing tables, data models, and audience segments. All you have to do is define your data and map it to the appropriate columns in Freshsales. You can schedule your syncs to run manually or even trigger them to run sequentially based on criteria that you define.

Hightouch automatically diffs data between syncs to ensure your only ever syncing the freshest data, and if any rows fail, Hightouch will automatically retry them later. A live debugger lets you analyze your API payload requests/responses and failed runs in real-time.