This post helps you with loading your data from BaseCRMto Redshift. If you are looking to get analytics-ready data without the manual hassle you can integrate BaseCRM to Redshift with Blendo, so you can focus on what matters, getting value out of your customer support.
Access your data on BaseCRM
The first step in loading BaseCRM’s data to any kind of data warehouse solution, is to access them and start extracting it.
As previously mentioned, using BaseCRM’s rich Core API you can get access to data from 25 resources including, among others, the following:
- Account: The Account API provides read-only access to your account details.
- Calls: The Calls API provides a simple interface to manage calls.
- Contacts: The Contacts API provides a simple interface to manage your contacts. A contact represents an individual or an organization.
- Deals: The Deals API provides a simple interface to manage deals.
- Leads: The Leads API provides a simple interface to manage leads. A lead represents an individual or an organization that expresses interest in your goods or services.
- Orders: Through the Orders API you can manage your orders.
- Pipelines: The Pipelines API provides a read only interface to your sales pipeline definition.
- Products:The Products API offers an interface for managing the Product Catalog. The catalog lists products that are available in your account.
- Users: Using the User’s API you can interact with your account’s users. You can retrieve a single user as well as list of all users associated with your account.
In addition to the above, the things that you have to keep in mind when dealing with the BaseCRM API, are:
- Rate limits. According to the documentation you can make up to 36,000 requests per hour (10 requests/ip/second).
- Authentication. You can authenticate on BaseCRM using OAuth.
- 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.
BaseCRM, is a web based next-generation customer relationship management (CRM) platform that offers customer management, sales tracker, sales pipeline monitoring, email tracking, forecasting, reporting and many more. Founded in 2009, BaseCRM offers to more any sales team the necessary tools to consolidate workflows, increase their efficiency and overall performance and scale easily.
BaseCRM currently offers three different APIs for programmatically interact with the platform: Core, Sync and Firehose. BaseCRM’s Sync API is suitable for syncing the latest BaseCRM data while Firehose for receiving near real-time stream of changes in BaseCRM data. However, both Sync and Firehose are available only certain upgraded plans.
On the other hand, the Core API is very convenient for creating, reading, updating and deleting data from BaseCRM RESTfully and is also available for all subscription plans.
Transform and prepare data from BaseCRM for Amazon Redshift
After you have accessed data on BaseCRM, you will have to transform it based on two main factors,
- The limitations of the database that data will be loaded onto
- The type of analysis that you plan to perform
Each system has specific limitations on data types and structures that it supports. If for example you want to push into Google BigQuery, then you can send nested data like JSON directly.
Also, you have to choose the right data types. Again, depending on the system that you will send the BaseCRM 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.
Also, you have to consider that the reports you’ll get from BaseCRM are like CSV files in terms of their structure and you need to somehow identify what and how to map to a table into your database.
Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load data of BaseCRM to Redshift, you will have to follow its model which is a typical relational database model. The information you extract from your data source should be mapped into tables and columns. Where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.
Also, each attribute should adhere to data types that are supported by Redshift.
As your BaseCRM data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift and make sure that you have mapped your types into one of the datatypes that are supported by Redshift.
Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions that you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of the database you need to load data on one of the data sources that are supported as input by Redshift, these are the following:
Load your data from BaseCRM to Amazon Redshift
To upload your data from BaseCRM to Amazon S3 you will have to use the AWS REST API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the Amazon AWS REST API endpoints for S3.
You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation.
After you have created your bucket you can start sending your data to Amazon S3, using again the same AWS REST API but by using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.
Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Amazon Redshift instance with your client, using either a JDBC or ODBC connection and then you perform an INSERT command for your data.
The way you invoke the INSERT command is the same as you would do with any other SQL database, for more information you can check the INSERT examples page on the Amazon Redshift documentation.
Redshift is not designed for INSERT like operations, on the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command.
You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift is able to read multiple files in simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.
The best way to load data from BaseCRM to Amazon Redshift
So far we just scraped the surface of what you can do with BaseCRM to Redshift integration 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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