Segment to BigQuery: 2 Easy Methods

on Tutorials • October 19th, 2021 • Write for Hevo

Does your organization have huge amounts of customer data that has been unified with Segment? Do you want to transfer this data to your BigQuery data warehouse to facilitate deep analysis? This blog will present three methods of transferring the data from Segment to BigQuery. Thus, enabling you to choose the method that best suits your needs.  

Table of Contents

Introduction to Segment

Segment to BigQuery: Segment
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Segment is a data platform that enables you to track and unify the customer data that is flowing through your organization. Segment does this by providing infrastructure that simplifies the process by enabling you to store and send your data. It also provides access to all of this data from a single API. Once Segment is installed, it tracks and sends messages to its tracking API in JSON format. Segment provides a standard structure for basic API calls and also a recommended JSON structure/schema that helps maintain data consistency.

A few key features of Segment are as follows:

  • Provides access to all customer data that is tracked by your organization from a single API.
  • Simplifies data integration tasks by minimizing the setup procedure.
  • Allows you to enrich your collected customer data by providing connections to other tools. This helps you further enhance your decision-making process.

Introduction to Google BigQuery

Segment to BigQuery: BigQuery
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Google BigQuery is a cloud-based data warehouse that is offered as part of the Google Cloud Products stack. BigQuery enjoys a good reputation on the market due to its high performance. This can be attributed to its tree architecture and its columnar data storage. BigQuery works by providing fast and scalable analysis of Big Data with SQL code. BigQuery is also offered as a managed service. This makes it easier to use when compared with many other data warehouses on the market. 

A few key features of Google BigQuery are as follows:

  • Easy to Use: Using BigQuery only requires you to load your data and then pay for what you use. 
  • Architecture: BigQuery has a distributed architecture, so you do not have to manage to compute clusters manually as Google manages these resources dynamically. 
  • Fast Insights: BigQuery can integrate seamlessly with many popular front-end analytics tools like Tableau and Data Studio. This makes it very easy to generate insights from your data.
  • Managed Service: Google handles backend configuration and performance tuning. This makes it easier to use than other data warehouses where you may be required to perform these tasks.

Loading Data from Segment to BigQuery:

This blog covers two methods for migrating data from Segment to BigQuery:

Method 1: Using Manual ETL Scripts to Connect Segment to BigQuery

Migrate your data from Segment to BigQuery by making calls to REST APIs and loading it to BigQuery.

Method 2: Using Hevo Data to Connect Segment to BigQuery

Hevo, an official Snowflake Partner for Data Integration, simplifies the process of data transfer from FTP to Snowflake for free with its robust architecture and intuitive UI. You can achieve data integration without any coding experience and absolutely no manual interventions would be required during the whole process after set up. Hevo’s pre-built integration with FTP along with 100+ Sources (including 30+ free Data Sources) will take full charge of the data transfer process, allowing you to set up FTP to Snowflake migration seamlessly and focus solely on key business activities.

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Understanding the Methods to Connect Segment to BigQuery

You can use the following methods to establish a connection from Segment to BigQuery in a seamless fashion:

Method 1: Using Manual ETL Scripts to Connect Segment to BigQuery

The broad steps to be undertaken in this approach of connecting Segment to BigQuery are as follows:

Step 1: Extracting Data from Segment

You can extract your Segment data by making calls to its REST API. For example:

GET https://platform.Segmentapis.com/v1beta/workspaces. The response will be  {   "workspaces": [     {       "name": "workspaces/myworkspace",       "display_name": "My Space",       "id": "e5bdb0902b",       "create_time": "2018-08-08T13:24:02.651Z"     }   ],   "next_page_token": "" }

If your Segment data incorporates data from third-party sources, then you may need their respective reporting APIs. For example, if your data includes Google Analytics data, then you can also make a call to its API using the GET method. Using these third-party APIs is not super flexible and you may have to manually combine the data should the need arise. It should also be noted that the data can be extracted with the Segment GUI. Further information can be found here.

Step 2: Preparing Your Data

You may have to create a schema for the tables to receive your Segment data. You will also have to ensure that the data types in the Segment data match with the data types in BigQuery. BigQuery provides support for a lot of popular data types.

More information on BigQuery data types can be found here. Information on Segment data types can be found here.

Step 3: Loading Your Data to BigQuery

The data can be loaded by:

  • Using gsutil to load the data file into Google Cloud Storage.
  • Accessing the BigQuery command line and use the bq load command to write code to create tables to store your data.
  • Load the data into your tables. More information on using the BigQuery command line to load data can be found here.

Limitations of using Manual ETL Scripts to Connect Segment to BigQuery

  • Difficulty with Data Transformations: It is very difficult to perform fast standardizations like currency and time conversions under this method.
  • Time-Consuming: This method requires a lot of manual code and builds a heavy dependency on the engineering team. This means it may not be the best option in situations when work has to be done quickly to meet tight deadlines.
  • Requires Constant Maintenance: Problems with the Segment API will result in inaccurate data. Thus, constant maintenance is required.
  • Difficulties with Real-Time Data: In case you are looking to get data in real-time you will have to write a lot of code and cron jobs to achieve this.

Method 2: Using Hevo Data to Connect Segment to BigQuery

Segment to BigQuery: Hevo Logo
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Hevo, an automated data pipeline, makes it very simple to move your data from Segment to BigQuery. Hevo 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. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

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Since Hevo is completely managed, your data projects can take off in just a few mins. The following are the steps:

Step 1: Authenticate and connect Segment to Hevo Data through Webhooks. To add the generated Webhook URL to your Segment account, just copy the URL and add it to your Segment account as a destination.

Segment to BigQuery: Source
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Step 2: Connect to your BigQuery account and start moving your data from Segment to BigQuery by providing the project ID, dataset ID, destination name, GCS bucket, Enable Streaming Inserts, and Sanitize Table/Column names.

Segment to BigQuery: Destination
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Hevo also ensures that it maps your data automatically to its relevant tables in BigQuery and gives you real-time access to your data. Sign up for a zero-risk, free 14-day trial with Hevo for hassle-free data migration to BigQuery. 

Furthermore, Hevo enables you to clean, transform, and enrich your data both before and after you move it in to the warehouse, ensuring that your data is analysis-ready at any point in the data warehouse. 

More reasons to try Hevo are listed below:

  • Scalability: Hevo is capable of handling data from a wide variety of sources like marketing applications, advertising platforms, analytics applications, etc. at any scale. Thus, Hevo enables you to scale your data infrastructure as your needs expand. You can explore the complete list of integrations here.
  • Simplicity: Hevo is intuitive and easy to use. Hevo ensures that your data is transferred in just a few clicks.
  • Real-time: Using Hevo enables you to gain real-time insights. Hevo has a real-time streaming architecture that allows you to instantly move your data without delay.
  • Reliable Data Load: Hevo’s fault-tolerant architecture ensures that data loads are reliable and consistent.
  • Fully Automated: Hevo is fully managed and automated, so it requires minimal effort from your end when setting it up

Conclusion

This blog talks about the two methods you can use to set up a connection from Segment to BigQuery: using custom ETL scripts and with the help of a third-party tool, Hevo. It also gives a brief overview of Segment and BigQuery highlighting their key features and benefits before diving into the setup process.

Visit our Website to Explore Hevo

Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications such as Segment into your Data Warehouse like BigQuery to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.

Do you want to transfer your data from Segment to BigQuery without the hassle of having to do it manually? Enter Hevo Data. Sign Up for a free trial today to vastly simplify your data load process. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

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