Freshdesk to Snowflake: 2 Easy Methods

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Integrating your data from Freshdesk to Snowflake helps you unlock insights such as customer support performance, satisfaction, and retention signals, as well as agent and team efficiency. This integration provides numerous benefits, but it’s not easy to set up as lack of change data capture, API rate limits, and complex data structure often pose as hurdles for many users trying to set up this integration. Noticing these hurdles, we have created a step-by-step guide with 2 easy methods so you can set up this integration with ease.

Understanding the Need to Load Data from Freshdesk to Snowflake

Freshdesk helps you to know how many customers are upset, how many customers churned, whether the issue is addressed on time, etc. All this information may come from Freshdesk, emails, feedback, phone, social media, etc. So, different data silos are created per feedback per country. To remove this problem, you can integrate all your customer-related data and get a broader picture of it.

Methods to Move Data from Freshdesk to Snowflake

Now you are going to learn about 2 methods you can use to move your data from Freshdesk to Snowflake:

• Method 1: Move Data from Freshdesk to Snowflake Using Manually Build ETL Scripts
• Method 2: Move Data from Freshdesk to Snowflake Using Hevo Data

Method 1: Move Data from Freshdesk to Snowflake Using Manually Build ETL Scripts

Step 1: Extracting the Data from Freshdesk

The data can be extracted from Freshdesk by making simple calls to its REST API. The data will be returned in JSON format as XML is no longer supported. A sample request may look like GET. ‘https://domain.freshdesk.com/api/v2/tickets/

Step 2: Preparing and Mapping the Data

• Identify and transform your data structures into a tabular format and set up supporting tables in Snowflake to store related or repeated data.
• Ensure your data types are accurately mapped to compatible Snowflake data types to prevent errors during loading and check all your field formats.

Step 3: Staging Process

• Each of these stages can be created using SQL code or via the Snowflake GUI. The SQL code has been included for the purposes of this blog
• 2 types of stages can be created in Snowflake: Internal and External stages. The following are high-level steps outlined for creating the stages mentioned:

The following are high-level steps outlined for creating the stages mentioned above:

Internal Stage

CREATE [ OR REPLACE ] [ TEMPORARY ] STAGE [ IF NOT EXISTS ] <internal_stage_name> [ FILE_FORMAT = ( { FORMAT_NAME = '<file_format_name>' | TYPE = { CSV | JSON | AVRO | ORC | PARQUET | XML } [ formatTypeOptions ] ) } ] [ COPY_OPTIONS = ( copyOptions ) ] [ COMMENT = '<string_literal>' ]

External Stage

• Amazon S3:

CREATE STAGE “[Database Name]”, “[Schema]”,”[Stage Name]” URL=’S3://<URL> CREDENTIALS= (AWS_KEY_ID=<your AWS key ID>, AWS_SECRET_KEY= <your AWS secret key>) ENCRYPTION= (MASTER_KEY=<Master key if required>) COMMENT= ‘[insert comment]’

• Microsoft Azure:

CREATE STAGE “[Database Name]”, “[Schema]”,”[Stage Name]” URL=’azure://<URL> CREDENTIALS= (AZURE_SAS_TOKEN=’< your token>‘) ENCRYPTION= (TYPE = “AZURE_CSE, MASTER_KEY=<Master key if required>) COMMENT= ‘[insert comment]’

There are other types of internal stages. However, these are generated automatically by Snowflake. The table stage is held within a table object and is best used for situations that require the staged data to be only used exclusively for a specific table. The user table is assigned to each user by the system. It cannot be altered or dropped and serves as a personal storage location.

Step 4: Loading the Data into Snowflake

To load the data into Snowflake, we use the COPY INTO DML statement through Snowflake’s SQL command-line interface SnowSQL. Note that using the FROM clause in your COPY INTO statement is optional as Snowflake will check for files in the stage automatically.

Internal Stages:

• User Stage Type:

COPY INTO TABLE1 FROM @~/staged file_format=(format_name=’json_format’)

• Table Stage Type:

COPY INTO TABLE1 FILE_FORMAT=(TYPE=‘JSON’ STRIP_OUTER_ARRAY=”TRUE”)

• Created Internal Stage:

COPY INTO TABLE1 FROM @Stage_name

External Stages:

• Amazon S3

While you can load data directly from an Amazon S3 bucket, the preferred method is that you first create an Amazon S3 external stage. The same applies to Microsoft Azure and GCP buckets too.

COPY INTO TABLE1 FROM s3://bucket CREDENTIALS= (AWS_KEY_ID='YOUR AWS ACCESS KEY' AWS_SECRET_KEY='YOUR AWS SECRET ACCESS KEY') ENCRYPTION= (MASTER_KEY = 'YOUR MASTER KEY') FILE_FORMAT = (FORMAT_NAME = JSON_FORMAT)

• Microsoft Azure

COPY INTO TABLE1 FROM azure://your account.blob.core.windows.net/container STORAGE_INTEGRATION=(Integration_name) ENCRYPTION= (MASTER_KEY = 'YOUR MASTER KEY') FILE_FORMAT = (FORMAT_NAME = JSON_FORMAT)

• GCS

COPY INTO TABLE1 FROM 'gcs://bucket’ STORAGE_INTEGRATION=(Integration_name) ENCRYPTION= (MASTER_KEY = 'YOUR MASTER KEY') FILE_FORMAT = (FORMAT_NAME = JSON_FORMAT)

Limitations of Using Manual Method

• Need Engineering Bandwidth: If you are a fast-growing organization with limited engineering resources, this can become a real bottleneck to get your Freshdesk data into Snowflake. The engineering team has its own priorities and targets and would need to realign its current priorities to accommodate these requests.
• No Access to Real-time Data: Additional code and cron jobs are needed to replicate data when records are updated. This can be a slow, brittle, and time-consuming process.
• Hard to Perform Data Transformations: Additional code will need to be written if you need to perform data transformations. This can make the process even more cumbersome.
• Maintenance: This method will stop working if there are any changes to Freshdesk’s API or if their server is down. This can result in data loss. Hence, you would need to station engineering resources that can provide on-demand support. 

Method 2: Move Data from Freshdesk to Snowflake Using Hevo Data

Step 1: Configure Freshdesk as your Source

Step 2 : Configure Snowflake as your destination

Conclusion

In this blog, we have provided 2 simple step-by-step guides on integrating your data from Freshdesk to Snowflake. This integration can help you achieve numerous benefits, such as advanced analytics and forecasting, automation alerts, and historical data retention, along with a few other features. This integration is highly recommended for customer-centric saas companies and global enterprises with multi-channel support.

Hevo is a No-code Data Pipeline that transfers data from Freshdesk to Snowflake for free. It will ensure that you get data that is both reliable and real-time. Sign up for a free trial to simplify your data transfer process from Freshdesk to Snowflake.

Sign up for a 14-day free trial to try Hevo for free. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

FAQ on Freshdesk to Snowflake

Share your experience of loading data from Freshdesk to Snowflake in the comment section below.