How to load data from NetSuite to PostgreSQL

Access your data on NetSuite

The first step in loading your NetSuite data to any data warehouse solution is to access your data and start extracting it.

NetSuite’s data model comes into to three following versions:

  1. SuiteScript Model: Suitable if working with Script or the Workflow Tool.
  2. Web Service Model: Suitable when external applications need to connect to NetSuite.
  3. ODBC/JDBC Model: Available when SuiteAnalytics module is purchased. This allows you to connect to a read-only version of NetSuite using ODBC or JDBC drivers.

Although differences do exist among the models mentioned above, the main types of information are always present and, among others, include the following:

  1. Accounts
  2. Transactions
  3. Campaigns
  4. Revenue Recognition

When working with the SuiteScript Model, you can extend the SuiteScript API using RESTlets.This way, you can deploy server-side scripts that interact with NetSuite data following RESTful principles. RESTlets offer ease of adoption for developers familiar with SuiteScript and support more behaviors than NetSuite’s SOAP-based web services, which are limited to those defines as SuiteTalk operations.

On the other hand, if you choose the Web Service model you will have to work with the NetSuite’s web services API which is implemented using the SOAP protocol. This will add some complexity to your development as you will have to manage SOAP and XML responses.

Also, keep in mind that you will have to respect the API usage limits as in every other API and make sure that you handle errors correctly.

Transform and prepare your NetSuite data PostgreSQL

After you have accessed your data on NetSuite, you will have to transform it based on two main factors,

  1. The limitations of the database that the data will be loaded onto
  2. 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. Keep in mind that in the case you are working with the web services SOAP API you will get XML responses. 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.

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 it 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.

With the NetSuite web services API, you also have two main additional sources of complexity. When it comes to data types, you have to keep in mind that SOAP is using XML to describe the service and the data. This means the data types you have to map are coming from XML and might have automatically been transformed into the primitive data types of the language you are using.

Each table is a collection of columns with a predefined data type as 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 NetSuite to a Postgres database is to create a schema where you will map each API endpoint to a table. Each key inside the NetSuite 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 NetSuite to PostgreSQL

For example, if an endpoint from NetSuite 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 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 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 and INSERT to end up with optimized and more performant INSERT queries.

Updating your NetSuite data on PostgreSQL

As you will be generating more data on NetSuite, you will need to update your older data on PostgreSQL. This includes new records and updates to older records that have been updated on NetSuite for any reason.

You will need to periodically check NetSuite for new data and repeat the process 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 identifying and removing any duplicate records on your database. Either because NetSuite 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 inserted in your database is a big and difficult issue, and PostgreSQL features like TRANSACTIONS can help tremendously. However, they do not solve the problem in the general case.

The best way to load data from NetSuite to PostgreSQL

So far, we just scraped the surface of what you can do with PostgreSQL and how you can load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

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