How to load data from ActiveCampaign to PostgreSQL
This post helps you with loading your data from ActiveCampaign to PostgreSQL. If you are looking to get analytics-ready data without the manual hassle, you can integrate ActiveCampaign to PostgreSQL with RudderStack, so you can focus on what matters, getting value out of your support data.
Access your data on ActiveCampaign
The first step in loading any ActiveCampaign data you own to any kind of data warehouse solution is to access your data and start extracting it.
ActiveCampaign has a well-designed API that is structured around REST, HTTP, and JSON, sufficient for interacting with the platform programmatically. API endpoint URLs are organized around resources, such as connections or deals. The available resources include the following:
- Deal Stages
- Deal Tasks
- Deal Task Types
- E-commerce Customers
- E-commerce Orders
In addition to the above, the things that you have to keep in mind when dealing with the ActiveCampaign API, are:
- Rate limits. There is no restriction regarding rate limits in v3 of the API, which is currently in beta. However, the older version describes the rate limits as 5 requests per second per account.
- Authentication. You can authenticate on ActiveCampaign with simple token authentication using the user’s API key in the request header.
- Pagination. API endpoints that return a collection of items are always paginated. The number of results to display can vary with a maximum value of 100.
ActiveCampaign is an email marketing easy-to-use solution that aims to help marketers achieve greater results with less effort. By combining more traditional email marketing capabilities with marketing automation and sales CRM features, it is widely used for the launch of intelligent, targeted campaigns.
By analyzing the data produced by ActiveCampaign you can conduct behavioral analysis by calculating the metrics that show how your current and potential customers react to the campaigns. You can even go beyond traditional behavior analysis and consider data as clickstreams, thus become able to reveal hidden sequential behavioral patterns in the data.
Furthermore, measuring the results that you have already generated is also important by answering questions like “What’s the average revenue generated per campaign sent?” or “How many people convert into leads?”
Transform and prepare your ActiveCampaign data for PostgreSQL
After you have accessed your data on ActiveCampaign, you will have to transform it based on two main factors,
- The limitations of the database that is going to be used
- The type of analysis that you plan to perform
Each system has specific limitations over the different 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 every 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 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.
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 out of ActiveCampaign to Postgres database is to create a schema where you will map each API endpoint to a table. Each key inside the ActiveCampaign 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 from ActiveCampaign to PostgreSQL
For example, if an endpoint from ActiveCampaign 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 coming 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 the 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 ActiveCampaign data on PostgreSQL
As you will be generating more data on ActiveCampaign, 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 ActiveCampaign.
You will need to periodically check ActiveCampaign 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 ActiveCampaign does not have a mechanism to identify new and updated records or because of errors on data pipelines, duplicate records might be introduced to your database.
In general, ensuring the quality of data that is 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 ActiveCampaign 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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