Data has become one of the most valuable resources over time. Multiple companies are looking to unlock business insights from their data. The infrastructure of data has gone through an incredible transformation. Businesses have taken a huge leap in this field, from Extract, Transform, and Load (ETL, where the data is first extracted, transformed i.e. cleaned and integrated, and then loaded into the destination) to Extract, Load and Transform (ELT, where the data is extracted and loaded and is then transformed into a query-able form) into the Data Warehouse. However, businesses are now also focussing on Reverse ETL and trying to integrate their Data Warehouses with SaaS applications. This article talks about a similar integration called Google BigQuery HubSpot Integration.
Reverse ETL is where data is transferred from Data Warehouses to Software-as-a-Service (SaaS) systems for Operational Analytics. This could help the businesses in making sure that there is a consistent view of their customer’s journey across all systems. Such Operational Analytics allow decision-makers to ensure that every decision made is an excellent strategic choice and is backed up by real-time data.
This article will help you understand how you can easily set up Google BigQuery HubSpot Integration to implement Operational Analytics in your business and also addresses the key features and limitations of the two methods used for the integration.
Table of Contents
- Introduction to Google BigQuery
- Key Features of Google BigQuery
- Introduction to HubSpot
- Key Features of HubSpot
- Methods to Set Up Google BigQuery HubSpot Integration
The prerequisites for Google BigQuery HubSpot Integration are as follows:
- A Google BigQuery Account.
- Permissions to access the Google BigQuery table that contains the data, permissions to run an export job, and permissions to write the data to the Cloud Storage Bucket.
- A HubSpot Account.
- OAuth (Open Authorization) Access Token or API (Application Programming Interface) Key for authorization of the requests.
- If you are updating the records of an existing table then, the Table ID of the table you want to import the data into.
Introduction to Google BigQuery
As companies are rapidly boosting their business by using their data to get valuable insights, it becomes difficult to scalably ingest, store and analyze that data. This is where Google BigQuery comes in. Google BigQuery is a Cloud Platform enterprise Data Warehouse, designed by Google to make large-scale Data Analysis accessible to everyone. It houses support for exabyte-scale storage functionality, backed by petabyte-scale SQL-based querying support. Google BigQuery has a built-in query engine that is capable of running SQL queries on terabytes of data in a matter of seconds. More than 350 companies reportedly use Google BigQuery for BI (Business Intelligence), including some of the Fortune 500 companies like Spotify, The New York Times, etc.
Key Features of Google BigQuery
The key features that make Google BigQuery unique are as follows:
- Fully Managed and Serverless: Google BigQuery is Serverless and lets you run queries without thinking about managing infrastructure. Its dynamic and flexible nature allows you to work with any type of data easily. Unlike other Data Warehouses, in Google BigQuery, processes are automatically distributed over a large number of nodes working in parallel.
- Real-time Analytics on Streaming Data: Google BigQuery can also process, design, and visualize reports and dashboards for Real-time or Streaming data. It does not require consolidated data from multiple sources to be stored before using analytics or Business Intelligence tools.
- Built-in Geographic Information Systems: Geospatial Analysis is very common in Data Analytics. Built-in GIS in Google BigQuery makes analysis and visualization of Geospatial Data very easy and highly performant. Although Geospatial Analysis is a unique feature in Google BigQuery, it has a couple of limitations such as the geographical functions are only available in standard SQL and only the BigQuery client library for Python currently supports it.
- High-Speed, In-Memory BI Engine: Google BigQuery has an in-memory BI engine that allows you to analyze data stored in it. You can also integrate BI engines with Google tools like Google Data Studio and also with other popular Business Intelligence tools like Looker, Tableau, Power BI, etc.
For more information on Google BigQuery, click here.
Introduction to HubSpot
HubSpot is a Cloud-Based platform that helps you build a flywheel for growth by providing you with the tools you would need to market, sell or service your customers and that too, all in one place. All these tools are categorized into the following categories:
- Marketing Hub: With Marketing Hub, you can attract the right audience by creating and sharing useful content, running tailored ad campaigns, and engaging with leads through personalized messages.
- Sales Hub: Sales Hub gives your Sales Team everything that they need to sell in a more personalized and relevant way. This could help them pitch their customers more efficiently and henceforth generate more revenue through sales.
- Service Hub: Service Hub is more focused on customer service. It helps you engage with your customers, guide them to solutions to their problems and turn them into promoters who would help your company grow through word of mouth.
Key Features of HubSpot
The key features of HubSpot are as follows:
- Ad Tracking and Social Media Management: You can use HubSpot to manage your Facebook, Instagram, LinkedIn, and Google ads and can also track which ads are performing well and generating more leads. It also helps you to monitor and prioritize important interactions that sometimes go unnoticed.
- Automated Emails and Workflows: You can use this HubSpot feature to send automated emails whenever someone submits a form on your website. HubSpot makes sure that these automated emails do not go to spam mails. You can also set up email workflows to send a series of emails to your contacts.
- Marketing Analytics and Metrics: HubSpot provides all the metrics and reports you need to make business decisions in one place. You can also make dashboards to effectively gain valuable insights from your customer data.
- Customer Relationship Management (CRM): HubSpot hosts both your website and CRM and gives you valuable insights into how your customers engage with your product or service. Accordingly, you can make changes to boost up their interaction and eventually convert your visitors into customers.
For more information on HubSpot, click here.
Ways to Set up Google BigQuery HubSpot Integration
This method involves setting up Google BigQuery HubSpot Integration manually by extracting data from Google BigQuery as CSV files and then importing them into HubSpot. This is a tedious method and requires a lot of technical knowledge, and hands-on experience in programming. The step-wise implementation to integrate Google BigQuery and HubSpot has been covered later in this article.Get Started with Hevo for free
Hevo Activate provides a hassle-free solution and helps you directly set up Google BigQuery HubSpot Integration without any intervention in an effortless manner. Hevo Activate is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Hevo’s pre-built integration with various data sources such as Google BigQuery, HubSpot, Amazon Redshift, Snowflake, Salesforce, etc., will take full charge of the data transfer process, allowing you to focus on key business activities. It helps transfer data from HubSpot to a destination of your choice for free.Sign up here for a 14-day Free Trial!
Methods to Set up Google BigQuery HubSpot Integration
You can set up Google BigQuery HubSpot Integration by implementing one of the following methods:
- Method 1: Manual Google BigQuery HubSpot Integration using CSV Files
- Method 2: Google BigQuery HubSpot Integration using Hevo Activate
Method 1: Manual Google BigQuery HubSpot Integration using CSV File
You can manually export Google BigQuery data as a CSV file to set up Google BigQuery HubSpot Integration by following these steps:
- Step 1: Go to the BigQuery page in the Cloud Console and log in to your Google BigQuery account.
- Step 2: In the Explorer panel, expand your project and dataset and then select the table you want to extract.
- Step 3: In the details panel, select Export and then Export to Cloud Storage.
- Step 4: Browse for the bucket, folder, or file where you want to export the data.
- Step 5: Choose the format for your exported data. There are three options in this, CSV (recommended), JSON, or Avro.
- Step 6: Accept the default value for compression (None or GZIP).
- Step 7: Click Export to export the table.
- Step 8: Open your HubSpot account and prepare the CSV file that needs to be imported. Make sure the file has a header, has only one sheet, contains fewer than 250,000 rows and 1,000 columns, and is smaller than 150MB.
- Step 9: Click on Start an import.
- Step 10: Select File from the Computer -> One File -> Multiple Objects and then select the objects in your import file.
- Step 10: Browse the file from your system that you want to import. Select the checkbox if you are updating the records of a previously loaded file using the Object ID.
- Step 11: On the Map columns, HubSpot automatically matches the columns in your file to properties, and in case you want to change any properties, click the corresponding dropdown menu and select the appropriate property. Once all the columns are matched, click Next.
- Step 12: Enter the Import name on the details screen. Once everything is set, click Finish import.
Limitations of Manual Google BigQuery HubSpot Integration using CSV files
Although Manual Google BigQuery HubSpot Integration using CSV files is an efficient process, it does have the following limitations:
- Manual BigQuery HubSpot Integration requires a lot of technical knowledge and hands-on experience in programming.
- As the data loaded in HubSpot is static, the changes made in Google BigQuery will not be automatically reflected in HubSpot. Therefore, the Data Integration Process needs to be repeated every time the data is to be updated in HubSpot.
- As you need to integrate data periodically, there is a high possibility of Data Redundancy in HubSpot.
- Manual BigQuery HubSpot Integration is only effective for businesses that work with small datasets. As these are CSV files, they are not scalable when there is a huge volume of data, and also changing the data structure once uploaded is a complex task to perform.
Method 2: Google BigQuery HubSpot Integration using Hevo Activate
Hevo Activate helps you directly transfer data from Google BigQuery, Snowflake, Amazon Redshift, etc., and various other sources to CRMs such as HubSpot, Salesforce, various SaaS applications, and a lot more, in a completely hassle-free & automated manner. Hevo Activate is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. It helps transfer data from HubSpot to a destination of your choice for free. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Hevo Activate takes care of all your data preprocessing needs and lets you focus on key business activities, and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.
Check out what makes Hevo Activate amazing:
- Real-time Data Transfer: Hevo Activate, with its strong Integration with various sources, allows you to transfer data quickly & efficiently. This ensures efficient utilization of bandwidth on both ends.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Secure: Hevo Activate has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Tremendous Connector Availability: Hevo Activate houses a large variety of connectors and lets you bring in data from numerous Data Warehouses and load it into Marketing & SaaS applications, such as HubSpot, Salesforce, Zendesk, Intercom, etc. in an integrated and analysis-ready form.
- Simplicity: Using Hevo Activate is easy and intuitive, ensuring that your data is exported in just a few clicks.
- Completely Managed Platform: Hevo Activate is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes.
- Live Support: The Hevo Activate team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
The article gives you detailed information on setting up Google BigQuery HubSpot Integration manually and automatically using Hevo Activate. It also highlights the key features and limitations of these methods.Visit our Website to Explore Hevo
With the limitations involved in manual integration, businesses are leaning more towards automated integration. It is not just hassle-free but easy to operate and does not require high-end expertise in this field. If this is the case for you, then you can explore more on Hevo Activate and see how it makes it so much easier for data migration code-free. It helps you directly transfer data from a source of your choice, such as Google BigQuery, Snowflake, Amazon Redshift, etc., to any SaaS application, CRMs, etc., in a fully automated and secure manner without having to write the code repeatedly. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure.
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