Connect Twilio to Snowflake: 2 Easy Methods

Struggling to analyze Twilio data in Snowflake?

The real challenge lies in extraction, staging, and reliable loading.

Twilio generates a constant stream of operational data, SMS events, call logs, delivery statuses, and usage metrics. As this data grows, querying it directly from APIs becomes inefficient and unreliable for analytics.

Snowflake, on the other hand, is built for large-scale analytics, performance tracking, and cost analysis. But to unlock its full potential, you need a reliable way to load Twilio data into it.

In this guide, you’ll learn how to load data from Twilio to Snowflake using two proven approaches. By the end, you’ll know which approach fits your technical requirements, data volume, and operational complexity.

Prerequisites to Integrate Twilio to Snowflake

Before integrating Twilio with Snowflake, ensure you have:

• An active Twilio account with access to the Twilio Console
• Twilio API credentials (Account SID and Auth Token or API Key & Secret)
• An active Snowflake account with a configured warehouse, database, and schema
• A Snowflake role with permissions to create tables and load data
• Clear understanding of the Twilio data to be synced (messages, calls, usage, etc.)
• Secure storage for credentials (environment variables or secret manager)
• Internet access to call Twilio APIs
  

Additional requirements:

• Hevo method: Active Hevo account with Twilio and Snowflake connectors
• Manual method: Basic knowledge of REST APIs and scripting (Python recommended)

How to integrate Twilio to Snowflake?

You can integrate Twilio to Snowflake using the following two methods:

• Method 1: Hevo’s fully automated, no-code pipeline
• Method 2: Manual API-based method

Method 1: Using Hevo’s Automated Data Pipelines to Connect Twilio to Snowflake

Hevo lets you move data from Twilio to Snowflake using a fully managed, no-code ELT approach. With easy integrations, built-in transformations, and automated schema handling, this method eliminates manual API handling and maintenance overhead.

Step 1: Configure Twilio as a source in Hevo

Log in to the Hevo platform and configure Twilio as your data source.

Follow these steps:

1. In the Asset Palette, click PIPELINES.
2. On the Pipelines List View, click CREATE.
3. On the Select Source Type page, choose Twilio from Hevo’s 150+ connectors.
4. In the Configure your Twilio Source page, specify the following:
   • Pipeline Name: A unique name for your Pipeline, not exceeding 255 characters.
   • API SID: The string identifier (SID) for your API key.
   • API Secret: The secret for your API key, retrieved from your Twilio account.
   • Historical Sync Duration: The duration for which the existing data in the Source must be ingested. Default duration: 6 Months.
5. Click Test & Continue.

Step 2: Configure Snowflake as a Destination

1. In the Asset Palette, click on Pipelines.
2. On the page displayed, Create Pipeline.
3. On the Add Destination page, select Snowflake as the Destination type.
4. In the Configure your Snowflake Warehouse page, specify the following:
   • Destination Name: A unique name for your Destination.
   • Database Password: Password of the database user.
   • Database Name: Name of the Destination database where the data is to be loaded.
   • Database Schema: Name of the schema in the Destination database where the data is to be loaded. Note: Schema name is case-sensitive.
   • Warehouse: The Snowflake warehouse is associated with your database, where the SQL queries and DML operations are performed.
5. Click Test Connection.
6. Click Save & Continue.

Step 3: Final Settings (Optional)

This step also allows viewing field mapping from source to destination using the Schema Mapper.

This step allows you to set up transformations that can be applied to source data to clean or enrich it.

Below is a real-life example of how Hevo centralized data ingestion into Snowflake with near real-time sync:

Company: ThoughtSpot is an AI-powered business intelligence platform that enables users to query and analyze data using intuitive interfaces for self-service analytics.

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Problem: ThoughtSpot had legacy on-premise data pipelines, manual ETL tooling, and high infrastructure costs, creating inefficiencies and limiting data usability for analytics teams.

Hevo’s solution: ThoughtSpot implemented Hevo as its cloud-native ELT platform to replace legacy pipelines and centralize data ingestion into Snowflake, leveraging Hevo’s robust connectors, near-real-time updates, and intuitive interface.

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Result: ThoughtSpot reduced infrastructure costs by 85% and cut ELT tooling expenses by 50%. Data usage across analytics teams increased by 30–35% with uninterrupted pipeline reliability.
Design scalable Twilio to Snowflake pipelines with Hevo

Method 2: Manually Connect Twilio to Snowflake Using APIs

Method 2: Manually Connect Twilio to Snowflake Using APIs

You can manually connect Twilio to Snowflake by extracting data through Twilio’s Bulk Export APIs, storing it in cloud storage, and loading it into Snowflake using native ingestion commands. Here, we have explained the method with a standard example:

Step 1: Prepare Twilio and Snowflake for integration

• Log in to your Twilio Console and copy the Account SID and Auth Token.
• Store these credentials securely using environment variables or a secrets manager.
• Next, ensure your Snowflake environment is ready:

• A running warehouse
• A target database and schema
• Permissions to create stages and load data
  

This preparation avoids authentication and permission errors during data ingestion.

Step 2: Create a Bulk Export Job in Twilio

Use Twilio’s BulkExport API to request data for a defined date range.

curl -X POST https://bulkexports.twilio.com/v1/Exports/Messages/Jobs \

-u “$TWILIO_ACCOUNT_SID:$TWILIO_AUTH_TOKEN” \

-d “StartDay=2025-01-01” \

-d “EndDay=2025-01-07”

Workflow:

• Defines a clear date range for exporting historical Twilio messaging data.
• Initiates an asynchronous job instead of pulling paginated API responses.
• Generates one compressed data file per UTC calendar day.
• Allows efficient backfilling of large historical communication datasets.
• Reduces API throttling and operational complexity.

Note: The BulkExport files are automatically removed after seven days.

Step 3: Identify ready-to-download export files

Check which daily export files are ready for download.

curl https://bulkexports.twilio.com/v1/Exports/Messages/Days \

-u “$TWILIO_ACCOUNT_SID:$TWILIO_AUTH_TOKEN”

Workflow:

• Returns only the days where export jobs have completed successfully.
• Provides visibility into export readiness at a daily granularity.
• Supports controlled, incremental ingestion into the data warehouse.
• Acts as a checkpoint before starting downstream loading steps.

Step 4: Download the exported data files

Download the data file for a specific exported day.

curl -o messages_2025_01_01.json.gz \

https://bulkexports.twilio.com/v1/Exports/Messages/Days/2025-01-01 \

-u “$TWILIO_ACCOUNT_SID:$TWILIO_AUTH_TOKEN”

Workflow:

• Downloads compressed JSON files containing Twilio message activity records.
• Preserves daily data boundaries for easier reprocessing and troubleshooting.
• Transfers data securely using Twilio-authenticated API requests.
• Prepares files for upload into cloud-based object storage.
• Enables offline or scheduled batch processing workflows.

Step 5: Upload files to cloud storage

Upload the exported files to the cloud storage supported by Snowflake.

aws s3 cp messages_2025_01_01.json.gz s3://twilio-exports/messages/

Workflow:

• Moves exported data into centralized and durable cloud object storage.
• Makes files accessible to Snowflake without local system dependencies.
• Separates data extraction from warehouse ingestion responsibilities.
• Simplifies retries, replays, and historical reload operations.

Step 6: Load data into Snowflake

Use a Snowflake stage and the COPY INTO command to load data into your Snowflake database.

COPY INTO twilio_messages

FROM @twilio_stage

FILE_FORMAT = (TYPE = ‘JSON’ COMPRESSION = ‘GZIP’);

Workflow:

• Reads compressed JSON files directly from external cloud storage.
• Parses semi-structured data into Snowflake’s VARIANT column format.
• Uses Snowflake compute resources for parallelized data loading.
• Centralizes raw Twilio data for downstream analytics and modeling.

Step 7: Validate the loaded data

Run the following code:

SELECT COUNT(*) FROM twilio_messages;

Workflow:

• Detects missing, duplicate, or incomplete data loads early.
• Validates overall pipeline health before analytics consumption.
• Ensures reliability of downstream reporting and dashboards.
• Signals readiness for transformation or enrichment workflows.

Experience faster Twilio to Snowflake integration today! Try Hevo for free.

Limitations of Using the Manual Method for Connecting Twilio to Snowflake

1. Operational complexity

Manually extracting Twilio data requires writing scripts to handle API calls, pagination, and retries. Maintaining code scripts over time increases operational overhead, especially when handling large volumes of messages or call logs.

2. Schema management

Twilio data often includes nested JSON structures and evolving fields. Manual pipelines require constant monitoring and updates to accommodate schema changes. Missing new fields or incorrect mappings lead to incomplete or inconsistent data in Snowflake.

3. Limited automation and monitoring

Manual pipelines lack built-in monitoring, alerting, and error recovery. Engineers must track failures, reprocess missing data, and handle retries themselves.

4. Scalability

As Twilio generates larger volumes of messages and call logs, manual extraction scripts can struggle to handle high throughput efficiently. Scaling these scripts to process daily or historical backfills can become resource-intensive and slow.

5. Time-consuming

Manual pipelines require ongoing maintenance, including updating API versions, handling rate limits, and adjusting scripts to accommodate changes in Snowflake or cloud storage. 

Understanding Twilio to Snowflake Integration

What is Twilio?

Developed in 2008, Twilio is a communication platform used by developers for making and receiving calls and exchanging text messages. Twilio is a CPaaS (Cloud Platform as a Service), which allows organizations to add real-time communication like audio, video, and messaging to business applications by using APIs. With Twilio, organizations can send SMS, voice messages, videos, calls, emails, chats, and more and interact with their customers. Organizations need only to integrate the Twilio API with their software, allowing direct communication with customers through apps and websites. Many companies like Twitter, Shopify, Netflix, and more use Twilio for their business applications.

Key Features of Twilio

Cost-effective

Twilio is a cost-effective platform that allows organizations to control their communication budget. It provides a pay-as-you-go pricing model for various communications through APIs.

Reliable Connections

Twilio enables organizations to provide a seamless communication experience with customers, partners, employees, and more. It offers a 99.95% uptime SLA with zero maintenance windows.

What is Snowflake?

Developed in 2012, Snowflake is a popular, fully managed cloud data warehouse that can be hosted on any cloud service ,such as Amazon Cloud Service, Google Cloud Storage, or Microsoft Azure. Snowflake consists of services like data engineering, data lakes, data warehouses, analytics, and more.

With Snowflake, users can organize their data into an optimized, compressed, and columnar format whenever data is loaded into the platform. Snowflake is a ready-to-use platform, as it uses SQL queries to perform data operations. You can start using Snowflake with a free trial of 30 days.

Since Snowflake is a fully managed SaaS platform, users do not need to select, manage, or configure hardware or software. As a result, it is ideal for many organizations that do not want to dedicate resources to setup, maintenance, configuration, etc.

Key Features of Snowflake

Connectors and Drivers

Snowflake enables users to use an extensive set of client connectors and drivers. It includes Python and Spark connectors and drivers like Node.js, Go Snowflake, .NET, JDBC, ODBC, PHP, and more.

Unique Architecture

One main feature that makes Snowflake different from other data warehousing services is its architecture. The architecture of Snowflake enables storage and compute units to scale independently. Therefore, organizations can use and pay for storage and computation separately.

Data Sharing

The data sharing feature in Snowflake allows users to share items from one database account to another without duplication. As a result, it guarantees more storage space with significantly less computation, which results in faster data accessibility.

Result Caching

Snowflake consists of a unique feature that caches results at different levels. The results can last for 24 hrs after the query is executed. As a result, if the same query is executed, the results are quickly delivered.

Harnessing Hevo for Seamless Twilio-Snowflake Integration

Integrating Twilio data into Snowflake can be challenging when you’re dealing with API extractions, JSON parsing, cloud staging, and manual loading commands.

Hevo streamlines this process with a no-code pipeline that automates extraction, transformation, and loading. It detects schema changes and maps new or nested fields so your Snowflake tables remain aligned as Twilio data evolves.

The platform also manages retries, API rate limits, and error recovery in the background. You can monitor pipeline performance, data freshness, and load health directly from Hevo’s dashboard, ensuring reliable ingestion without constant supervision.

Additionally, the platform adheres to industry-standard certifications, like SOC 2 Type II, GDPR, and HIPAA, ensuring secure data movement at every stage of the pipeline.
Sign up for Hevo’s 14-day free trial and experience seamless data migration.

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