Amazon Redshift

In order to allow Skypoint AI access to your Amazon Redshift account, you'll need:

1. Host Name
2. Database Name
3. Port Number
4. Username
5. Password

Steps to Achieve all the necessary details for your Redshift account

1. Go to the Redshift Cluster and view properties.
2. The publicly accessible option allowing instances and devices outside the VPC to connect to the database through the cluster endpoint should be Yes. The default value is No.

3. Click on the security group and go to inbound rules and click on edit inbound rules and there is a list of IPs and fill in all the detailed IPs with their details.

Below is the list of IPs required to be inside the inbound rules.

• 13.91.229.38
• 40.112.243.7
• 40.118.229.231
• 13.64.89.216
• 40.78.16.175
• 52.160.111.150
• 13.64.91.216
• 13.64.94.144
• 13.91.124.136
• 13.91.125.204
• 40.118.239.192

4. The hostname required for connecting to our platform can be found under the heading endpoint under connection details.

note

The hostname includes everything from the beginning of the endpoint up to the ".com".

5. The database name, port and username can be extracted under the database configuration section.

6. The password is the same that was set during the creation of the database.

Steps to Import Data Using Amazon Redshift

1. Navigate to Data > Dataflows and click on Add Dataflow button present on top right side of the platform.
2. Fill in the name of the connector and click on Next.
3. You will see all the different types of connectors present in front of you. In the search bar present on top right, type Amazon Redshift and select the Amazon Redshift connector.

4. Next, fill in the display name and type in a brief description about the connector in the general section and then go to Connector Section to configure the connector with our platform.

5. Enter all the necessary details and click on Connect. It might take some time to finish up, depending upon the data size.

6. Once its completed, you will be able to see the imported entities below the Connect button.

7. Follow this to get a better understanding of how to input data. This will be beneficial for data processing as the data will be stored in a convenient manner.

8. As the data gets integrated, we will be able to see the number of entities present in that dataflow. On successful completion of the process, the status of the dataflow will be a green check denoting that the data has been integrated and can now be taken into further usages.

9. In entities, we will be able to see the names of the different entities present in the data, the number of records present in each entity, entity type (default: Custom) and many more. There are filters present on top of every column which gives user the ability to search a particular entity based on some criteria. By clicking on a particular entity, we can get its details like the attributes defining all the different attributes present in the data with their datatypes. To know more about the entities, visit here

10. Next, based on the requirements, the data can be processed using the Stitch operation offered on our Platform. The data that has been collected in the previous processes is now ready for some mapping. For that, we provide the Stitch option that helps the user to map different entities and find correlation between entities and create a cumulative entity with maximum information on the data. The Stich involves three different types of actions which helps in refining the data. They are:

    • Map: Mapping involves correlating different entities in a dataflow using specific feature attributes to produce a more extractable data-form.
    • Match: After successful completion of Map, we are ready to match the entities. The match phase specifies how to combine your datasets into a cumulative customer profile dataset.
    • Merge: The final phase of Stitch is Merge where we choose and exclude attributes to merge within or mapped and matched data. Some of the attributes are automatically merged.

To know more about Stitch, click here