Amazon Redshift Analytics - AgenticBI Integration

AgenticBI enables data discovery, querying, visualization and reporting automation from Redshift along with other unstructured and structured datasources.

Overview

  1. Connect, extract and transform data from your Redshift through our UI to connect directly, if your Redshift servers are accessible from the cloud.

  2. Visualize and Automate your Reporting instantly.

UI Based Approach

Connecting

  1. Log in to AgenticBI and select Queries from the left sidebar.
  2. Click on New Datasource + button and select Amazon Redshift from the list of datasources.
  3. After navigating to the New Datasource page, either use the pre-configured settings into AgenticBI's own demo Amazon Redshift database or follow the prompts and configure the following details to set up connectivity to your own Amazon Redshift database: a. Datasource Name: Enter a name for your datasource
    b. Host Name: Enter the host name to connect to
    c. Port: Enter the database port
    d. Database Name: Enter the database name
    e. Schema Name: Enter the schema name
    f. User: Enter the User ID to connect
    g. Password: Enter the password to connect to the database
    h. Database Properties: Additional database connection properties/url parameters. For example, ssl=true&anotherProp=anotherVal.

  4. Establish Network connectivity and click on the Test Connection button.

    Note: The connection validity of the network can be tested only if it has been established via Direct Connectivity or an SSH tunnel. For more information on connectivity and datasource, please refer to the documentation on- Connectivity & Datasources.

  5. Click on Save and start Querying.

    adding-redshift

Query

Set up Query using a visual builder or query editor

Visual Builder

After connecting to the Amazon Redshift datasource, AgenticBI will pull out a list of tables along with field samples.

Step 1: After connecting to the Amazon Redshift datasource, AgenticBI will pull out a list of tables along with field samples. Using these tables, you can automatically generate queries through our visual builder in a no-code environment by either dragging and dropping fields or making your selections through the drop-down.

visual-builder

Step 2: Define data execution strategy by using any of the following two options:

  • Direct Execution: Directly execute the Query on the original Datasource, without any storage in between. In this case, when a widget is displayed, it will fetch the data in real time from the underlying Datasource.

  • Non-Direct Execution: For non-direct queries, results will be stored in AgenticBI's Elastic Store. Benefits include- long-running queries, reduced load on your database, and more.

Non-direct execution can be put into action if you choose to run the Query once or at scheduled intervals. For more information, feel free to check out this documentation- Defining Data Execution Strategy

data-strategy

Step 3: Click on the Preview button to analyze the results of your Query and fine-tune the desired output, if required.

preview

The result of your Query is called Dataset. After reviewing the results, name your dataset and then hit the Create & Run button.

create-and-run

Query Editor

A versatile text editor designed for editing code that comes with a number of language modes including Redshift Query Language (RQL) and add-ons like Cloud9QL.

create-and-run

Step 1: Write your query using Redshift SQL (RQL) in the Query Editor. Optionally, apply Cloud9QL on top for additional transformations.

Step 2: Define data execution strategy by using any of the following two options:

  • Direct Execution: Directly execute the Query on the original Datasource, without any storage in between. In this case, when a widget is displayed, it will fetch the data in real time from the underlying Datasource.

  • Non-Direct Execution: For non-direct queries, results will be stored in AgenticBI's Elastic Store. Benefits include- long-running queries, reduced load on your database, and more.

Non-direct execution can be put into action if you choose to run the Query once or at scheduled intervals. For more information, feel free to check out this documentation- Defining Data Execution Strategy

data-strategy

Step 3: Click on the Preview button to analyze the results of your Query and fine-tune the desired output, if required.

data-strategy