How To Load Data from Freshdesk to Postgresql
Access your data on Freshdesk
The first step in loading your Freshdesk data to any kind of data warehouse solution is to access your data and start extracting it.
Freshdesk offers a rich and well-defined API that belongs to the Representational State Transfer (REST) category. Using it you can perform RESTful operations such as reading, modifying, adding, and deleting your helpdesk data, thus allowing you to programmatically interact with your account.
Among the 18 provided resources, you can find information about Tickets and Conversations, Agents, Companies, Surveys and Satisfaction Ratings, and many more.
In addition to the above, the things that you have to keep in mind when dealing with the Freshdesk API, are:
- Rate limits. Depending on the chosen plan and API version that is being used, Freshdesk allows a number of API calls per hour.
- Authentication. You authenticate on Freshdesk using an API key.
- Paging and dealing with a big amount of data. Platforms like Freshdesk that are dealing with clickstream data tend to generate a lot of data, like events on your web properties.
Freshdesk is a SaaS customer support platform released by Freshworks that integrates traditional support channels such as email, phone, and LiveChat with social channels like Facebook or Twitter.
While using Freshdesk as your ticketing platform you can easily keep track of all ongoing tickets as well as manage all the support-related communication across all channels. You can also produce various helpdesk reports in order to better understand your team’s performance, gauge your customers’ level of satisfaction and gain important insight regarding possible improvements.
Transform and prepare your Freshdesk Data for PostgreSQL
After you have accessed your data on Freshdesk, you will have to transform it based on two main factors,
- The limitations of the database that the data will be loaded onto
- The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly.
Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database. Freshdesk has a very limited set of available data types which means that your work to do these mappings is much easier and straightforward, but nonetheless equally important with any other case of a data source.
Due to the rich and complex data model that Freshdesk follows, some of the provided resources might have to be flattened out and be pushed in more that one table.
Each table is a collection of columns with a predefined data type like an integer or VARCHAR. PostgreSQL, like any other SQL database, supports a wide range of different data types.
A typical strategy for loading data from Freshdesk to a Postgres database is to create a schema where you will map each API endpoint to a table. Each key inside the Freshdesk API endpoint response should be mapped to a column of that table and you should ensure the right conversion to a Postgres compatible data type.
Load data from Freshdesk to PostgreSQL
For example, if an endpoint from Freshdesk returns a value as String, you should convert it into a VARCHAR with a predefined max size or TEXT data type. tables can then be created on your database using the CREATE SQL statement.
Once you have defined your schema and you have created your tables with the proper data types, you can start loading data into your database.
The preferred way of adding larger datasets into a PostgreSQL database is by using the COPY command. COPY is copying data from a file on a file system that is accessible by the Postgres instance, in this way much larger datasets can be inserted into the database in less time. COPY requires physical access to a file system in order to load data.
Nowadays, with cloud-based, fully managed databases, getting direct access to a file system is not always possible. If this is the case and you cannot use a COPY statement, then another option is to use PREPARE together with INSERT, to end up with optimized and more performant INSERT queries.
Updating your Freshdesk data on PostgreSQL
As you will be generating more data on Freshdesk, you will need to update your older data on PostgreSQL. This includes new records together with updates to older records that for any reason have been updated on Freshdesk.
You will need to periodically check Freshdesk for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a PostgreSQL table is achieved by creating UPDATE statements.
Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Freshdesk does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.
In general, ensuring the quality of the data that is inserted in your database is a big and difficult issue and PostgreSQL features like TRANSACTIONS can help tremendously, although they do not solve the problem in the general case.
The best way to load data from Freshdesk to PostgreSQL
So far we just scraped the surface of what you can do with PostgreSQL and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.
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