Sync data from
Google BigQuery to Salesforce
Connect your data from Google BigQuery to Salesforce with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.
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Integrate your data in 3 easy steps
Add your source and destination
Connect to 15+ data sources, like Google BigQuery, and 140+ destinations, like Salesforce.
Define your model
Use SQL or select an existing dbt or Looker model.
Sync your data
Define how fields from your model map to Salesforce, and start syncing.
Model your Google BigQuery data using any of these methods
dbt Model Selector
Sync directly with your dbt models saved in a git.
Query using looks. Hightouch turns your look into SQL and will pull from your source.
Create and Edit SQL from your browser. Hightouch supports SQL native to Google BigQuery.
Select available tables and sheets from Google BigQuery and sync using existing views without having to write SQL.
For less technical users, pass traits and audiences from Google BigQuery using our visual segmentation builder.
Where can you sync your Google BigQuery data in Salesforce
Represents an individual account, which is an organization or person involved with your business (such as customers, competitors, and partners). Use the
accountobject to query and manage accounts in your org.
Represents a contact, which is a person associated with an account. Use the
contactobject to manage to manage individual people who are associated with an account.
Represents a prospect or lead. Use the
leadobject to manage leads in your org.
Represents an opportunity, which is a sale or pending deal. Use the
opportunityobject to to manage information about a sale or pending deal.
Represents the association between a campaign and either a lead or a contact. Use the
campaignMemberobject to manage campaign members in your org.
Represents a business activity such as making a phone call. Use the
taskobject to manage to-do items for your org.
Represents a case, which is a customer issue or problem. Use the
caseobject to manage customer cases for your org.
Represents an item of commercial value, such as a product sold by your company or a competitor, that a customer has purchased. Use the
assetobject to manage assets for your org.
userin the organization. Use this object to query information about users and to provision and modify users in your organization. Unlike other objects, the records in the User table represent actual users—not data owned by users.
Custom Salesforce Objects
We support custom Salesforce objects to ensure our integration supports all your organization’s unique workstreams.
Does this integration support in-warehouse planning?
Yes, if you integerate Google BigQuery and Salesforce 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 Salesforce). 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 Google BigQuery data to Salesforce?
Salesforce 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 Salesforce data, in addition to the rich behavioral data captured through your app/website already, exists in Google BigQuery. 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 Salesforce, and that means enriching your CRM with data directly from your warehouse.
Why should you use reverse ETL to connect Google BigQuery and Salesforce data?
Conventionally moving data from Snowflake to Salesforce meant downloading ad hoc CSV files and uploading them manually or forcing your data team to integrate with the Salesforce 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 Salesforce. 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 Salesforce. 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.
How to integrate BigQuery and Salesforce
There are 5 main options on how to move data from BigQuery to Salesforce. Some of these options are more efficient and more secure while some are more manual and less secure. We've outlined your options below:
Option 1: Reverse ETL (Hightouch)
Reverse ETL is the easiest way to sync your data in BigQuery to Salesforce. With Reverse ETL tools like Hightouch, all you have to do is define your data in BigQuery using SQL or your own existing data models and then map that data to the appropriate columns in Salesforce. In addition to this, Hightouch never stores any of your data, so you can own your data pipelines from end to end.
Better yet, your marketing teams can even build custom audiences directly in the Hightouch UI using a simple drag and drop interface. On top of this, your data teams don’t have to worry about fulfilling ad-hoc requests, managing workflows, or fixing broken data pipelines.
Reverse ETL enables Operational Analytics and shifts the focus from simply understanding your data, to putting it to work in the tools that run business processes. Reverse ETL only takes a few minutes to implement and you can get started by following the steps below.
Step 1: Connect to Your Data Source
Step 2: Connect Hightouch to Your Destination
Step 3: Create a data model or leverage an existing one
You can use SQL to define your data directly in the Hightouch UI or even leverage your existing data models.
Step 4: Choose Your Primary Key
With Hightouch you can map on any unique key, not just users and accounts.
Step 5: Create Your Sync
Once you have created your sync, you simply have to choose the appropriate columns and map them to the fields in your end destination.
Step 6: Schedule Your Sync
Once your sync is created you can run it manually, define a set interval, or schedule it to run after a dbt job is completed.
The first integration with Hightouch is completely free so you can actually get started today. If you want to learn more about Reverse ETL, download our guide.
Option 2: Manual CSV Files
Downloading a specific BigQuery dataset as a CSV file and uploading that data directly to Salesforce is probably one of the easiest ways to move data, but it's not efficient. Data stored in this format gets stale really quickly and business teams can never truly rely on it. In an ideal world, you need an automatic daily flow of data from BigQuery to Salesforce.
When you are moving CSVs it requires your data team to fulfill ad-hoc data retrievals every time your sales or marketing team has a request. This is no minimal effort and time begins to add up pretty quickly when you do this over and over again on a daily basis. In addition to this, you also have to ensure that the columns in your file match those that exist in Salesforce, otherwise, you risk ruining all of your existing data because there is no undo button.
Option 2: Custom Integrations
Depending on the scale of your company, you could build your own data pipeline to move data from BigQuery to Salesforce. There are a couple of challenges with this approach. Firstly, building in-house is both time-consuming and resource-intensive and it pulls away from the time your engineers could be spending doing their actual jobs.
Secondly, the APIs for BigQuery and Salesforce are constantly changing, meaning that you have to check for upstream or downstream changes to ensure your pipeline continues working. Depending on your needs, building in-house might make sense, but if you are planning on sending data to additional SaaS applications in the future, it's simply not scalable
Option 3: iPaaS (Integration Platform as a Service)
Workflow tools like Tray and Workato can be a good option for moving data back and forth between SaaS applications. These tools let you build intuitive workflows that trigger actions when specific events happen.
However, when you are moving data from your warehouse to a destination like Hubspot, the workflows within these tools quickly become complex because they are not designed to combine data in the same way that a warehouse is. You will find yourself having to write custom code in order to get your workflows to function, and you will quickly notice that you have more if/then statements and dependencies within each step than you can possibly count.
Option 4: CDPs (Customer Data Platform)
CDPs provide a number of built-in features, but they essentially help you consolidate all of your customer data into a centralized 3rd-party platform so that it can be sent to other SaaS tools like Salesforce. However, CDPs create another source of truth in addition to your warehouse.
They also take your data out of your own cloud environment which can create problems around compliance depending on the industry that you work in. Additionally, from a time-to-value standpoint, CDPs are probably the slowest option, with the average implementation time taking anywhere from six months to a year.
Push lead info from your warehouse into Salesforce CRM to enable executives to go after the right accounts
Push product data to enable account managers to know what actions are being taken in the app
Reduce churn by syncing health scores and churn events to Salesforce CRM for account managers to track
Why sync data from
Google BigQuery to Salesforce?
Salesforce is the single source of truth for your sales team, but it only shows one view of your customer. In reality, all of your product usage data, event data, and custom audiences live in BigQuery. To truly have a 360-degree view of your customer, your sales team needs access to this information. With the proper data in their hands, your sales team can remove the guesswork and start targeting your highest value leads.
About Google BigQuery
BigQuery is a fully-managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence.Learn more about Google BigQuery
Salesforce is a cloud computing service as a software (SaaS) company that specializes in customer relationship management (CRM). Salesforce's services allow businesses to use cloud technology to better connect with customers, partners and potential customers.Learn more about Salesforce
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What is Operational Analytics & Why You Should Use It
Operational Analytics shifts the focus from simply understanding data to taking action on it in the tools that run business processes. Instead of using dashboards to make decisions, Operational Analytics is focused on turning insights into action – automatically.Read
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