Connect Twilio to BigQuery: 2 Easy Methods

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Twilio allows businesses to communicate with their customers by using several APIs. It consists of all the activity logs of its customers from various channels such as audio, messages, text, WhatsApp, calls, and more. Even though Twilio keeps all the customer information safely in one place, transferring that information to a data warehouse like Google BigQuery can help you further analyze and make data-driven decisions. You can connect Twilio data to Google BigQuery using standard APIs and third-party ETL (Extract, Load, and Transform) tools.

In this article, you will learn to connect Twilio to BigQuery using APIs and manual processes.

What is Twilio?

Developed in 2008, Twilio is a customer engagement platform that enables communication and data management tools for organizations. It helps organizations provide unique and personalized experiences for their customers using several APIs. Twilio is very well known for democratizing channels such as voice, text, chat, video, and emails through APIs, which enables every organization to have meaningful interactions with their customers on their preferred channels.

Twilio provides applications such as Twilio Engage, Twilio Frontline, and Twilio Flex that allow organizations to span the entire customer lifecycle, from marketing to sales and customer service.

Key Features of Twilio

• MessagingX: Twilio MessagingX allows organizations to engage customers by sending and receiving SMS, MMS, and OTT messages on a large scale with built-in APIs like Programmable Messaging and Conversations that use phone numbers in more than 180 countries. 
• Cost-effective: Twilio offers the pay-as-you-go pricing scheme for different communication APIs. As a result, Twilio is a cost-effective platform that allows organizations to control their communication costs.

What is BigQuery?

Developed in 2010, Google BigQuery is a fully managed data warehouse with several cloud functions and services to run businesses seamlessly. Google BigQuery allows businesses to handle terabytes of data and use standard SQL language queries to analyze and get answers from data faster.

Since Google BigQuery uses Dremel technology and columnar storage structure, it can provide fast query performance and high data compression capabilities. Besides high performance, Google BigQuery also enables businesses to reduce the additional costs by scaling up and down storage and computation power independently according to their business needs.

BigQuery enables developers and data scientists to work with several programming languages like Python, Java, JavaScript, and Go. They can also use BigQuery’s REST API to transform and manage data effectively.

Key Features of BigQuery

• BigQuery engine: BigQuery consists of the BigQuery engine, allowing users to process large datasets with sub-second query response time and high concurrency. This BI engine can integrate with BI tools such as Tableau, Google Data Studio, Power BI, and more to speed up data analysis.
• Machine learning: With BigQuery, users can create machine learning models on structured and unstructured data with SQL queries. Machine learning models such as Logistic Regression, Multi-class Regression, Binary Logistic Regression, K-means clustering, and more are supported with BigQuery.

Methods to Connect Twilio to BigQuery

Method 1: Connect Twilio to BigQuery using Hevo

The following steps can be implemented to connect Twilio to BigQuery using Hevo:

• Configure Source: Connect Hevo Data with Twilio providing a unique name for your Pipeline, along with details such as  API SID, API Secret and historical sync duration.

• Configure Destination: Establish a connection to Google BigQuery by providing information about its credentials such as Destination Name, Authorized Account, Project Id and Dataset Id. 

Here are more reasons to try Hevo:

• Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
• Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
• Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making. 
• Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
• Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
• Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Method 2: Connect Twilio to BigQuery Manually 

You can connect Twilio to BigQuery by using standard APIs, Twilio Studio. In this section, you will learn to connect Twilio to BigQuery by exporting and importing data from Twilio to Google BigQuery. 

Step 1: Exporting Twilio data

You can export Twilio data in two ways: Exporting Twilio data using APIs and Twilio Studio.

A) Exporting Twilio data using APIs

BulkExport is a new feature in Twilio that allows you to access and download files containing records of all the incoming and outgoing messaging.

BulkExport allows you to:

• Connect a data warehouse with the state of all your messages such that you can query and aggregate them.
• Check the status of your messages without going back to the Twilio API.

With the BulkExport file, you can get the final state of all your messages. The BulkExport enables you to get a single zipped JSON file containing records for each message you have sent or received on a given day.

When you enable BulkExport, you can download a file daily that includes the previous day’s messages.

BulkExport is useful when you want to:

• Check the delivery status of your messages.
• Load message data into the data store.
• Check how many messages are sent and received.
• Archive your activity.

When you get the BulkExport file, you can view and load the messages to another system. You must fetch the resulting file from the Twilio API to use these messages.

The code below refers to BulkExport JSON block.

{
  "date_updated": "2017-08-03T03:57:34Z",
  "date_sent": "2017-08-03T03:57:33Z",
  "date_created": "2017-08-03T03:57:32Z",
  "body": "Sent from your Twilio trial account - woot woot!!!!",
  "num_segments": 1,
  "sid": "SMandtherestofthemessagesid",
  "num_media": 0,
  "messaging_service_sid": "MGandtherestofthemessagesid",
  "account_sid": "ACandtherestoftheaccountsid",
  "from": "+14155551212",
  "error_code": null,
  "to": "+14155552389",
  "status": "delivered",
  "direction": "outbound-api"
}
// a lot of other messages

You can read more about using BulkExport.

B) Export Twilio data using Twilio Studio

To export data, initially create a Flow, which represents the workflow you want to build for your project.

• Step A: Create a Flow
  • Step 1: Log into your Twilio account using the Twilio Console.
  • Step 2: Go to the Studio Flows section.
  • Step 3: Click on the Create new Flow. You will see the window below if you have already created a Flow. Click on ‘+’ to create a new Flow.
  • Step 4: Name your Flow and click on Next. After naming your Flow, you can see the list of possible templates to use. You can also start with the empty template by clicking on the Start from scratch option. Then click on Next.

• Step B: After creating the Flow, you should make the Flow’s Canvas, where you can build the rest of the logic for your project. You can follow the video tutorial to understand Flow’s Canvas.
• Step C: After managing Canvas, you can use the Widgets, known as the Twilio Studio’s building blocks. Widgets enable you to handle incoming actions and respond immediately by performing tasks like sending a message, making a phone call, capturing the information, and more. You can read and implement working Widgets using a video tutorial.

Step 2: Importing data to BigQuery

You can load any data file in Google BigQuery, but you will learn to import a csv file in this article.

When you load csv file from Cloud Storage, you can load that file into a new table or partition. You can also append your csv file to overwrite an existing table or position. When your csv file is loaded into Google BigQuery, it is converted into the columnar format for Capacitor.

Before loading the csv file into BigQuery, you need the IAM permissions to load data into BigQuery or partitions. 

1) Permissions to load data into BigQuery

You need the below IAM permissions to load data or append or overwrite an existing table or partition in BigQuery.

bigquery.tables.create
bigquery.tables.updateData
bigquery.tables.update
bigquery.jobs.create

Each of the below predefined IAM roles includes the permissions that you need to load data into BigQuery table or partition:

roles/bigquery.dataEditor
roles/bigquery.dataOwner
roles/bigquery.admin (includes the bigquery.jobs.create permission)
bigquery.user (includes the bigquery.jobs.create permission)
bigquery.jobUser (includes the bigquery.jobs.create permission)

2) Permissions to load data from Cloud Storage

To load the data from Cloud Storage, you need the below IAM permissions.

storage.objects.get
Storage.objects.list

The predefined IAM role roles/storage.objectViewer consists of all the permissions you need to load data from Cloud Storage.

3) Loading csv file into a BigQuery table

You can load the csv file into a BigQuery table with the following:

• The Cloud Console
• The bq command-line tool’s bq load command
• Calling the jobs.insert API method
• Client libraries

Follow the below steps to load the csv file into a BigQuery table.

• In the Cloud Console, navigate to the BigQuery page.
• Expand your project in the Explorer pane and then select a dataset.
• Click on the Create table in the Dataset info section.
• Specify the following details in the Create table panel. In the Source section, select Google Cloud Storage in the Create table from the list and then follow the steps below.
  • Select the file from the Cloud Storage bucket or enter the Cloud Storage URI. You cannot include multiple URLs in the Cloud console, but you can include wildcards. The Cloud Storage bucket must be in the same location as the dataset, containing the table you want to create, append or overwrite.
  • Select csv for the file format.

Specify the below details in the Destination section.

• Select the dataset in which you want to create the table.
• Enter the name of the table that you want to create in the Table field.
• Verify that the Table type field is set to Native table.

Enter the schema definition in the Schema section. To enable the auto-detection of the Schema, select Auto-detect. You can enter the Schema information using any of the below options.

• Click on Edit as Text and paste the Schema in the form of a JSON array. While using a JSON array, you can generate the Schema using the same process as creating a JSON schema file. You can use the below command to view the Schema of the existing table in JSON format.

bq show --format=prettyjson dataset.table

• Click on the Add field and enter the table schema. Specify each field’s Name, Type, and Mode.

Click on the Advanced Options and follow the next instructions to create a table in BigQuery.

You have successfully transferred your data from Twilio to BigQuery.

Limitations of connecting Twilio to BigQuery Manually

• Although exporting Twilio to Google BigQuery using APIs might seem easy, it is more time-consuming and requires technical experts. 
• You can also export Twilio data using Twilio Studio, it is an easy task, but users can not process real-time data with Twilio Studio. As a result, to overcome such challenges, you can use third-party ETL tools that allow automated and seamless data transfer from Twilio to BigQuery.

Conclusion

In this article, you learned to connect Twilio to BigQuery. Twilio enables businesses to communicate with their customers by leveraging several APIs that provide interactive and personalized communication. Twilio also allows organizations to export their customer data to a centralized repository like Google BigQuery, which can be further used for analysis and decision making.You got a glimpse of how to connect Twilio to BigQuery using APIs and CSVs. The process can be a bit difficult for beginners. Moreover, you will have to update the data each and every time it is updated and this is where Hevo saves the day!

Frequently Asked Questions

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