Scenario 3 - Low code data analytics
Problem statement#
As a data scientist, I need to extract insights from data stored on a relational database in a low code/no code manner such that I can create reports quickly and with a low amount of effort.
Goals#
- Load the data
- Perform ETL operations on the data
- Provide a low code/no code solution to create data visualizations
Tools & Capabilities#
To meet the use case goals, the following tools from the portal will be leveraged:
| Tool | Description | Key capability |
|---|---|---|
| ElasticSearch | Elasticsearch is the distributed, RESTful search and analytics engine at the heart of the Elastic Stack. | - Data load - Provide a search engine tool - Search engine performances - Retrieve relevant data despite typos in the query keyword |
| Kibana | Kibana is your window into the Elastic Stack. Specifically, it is a browser-based analytics and search dashboard for Elasticsearch. | - Search dashboard for the search engine tool - Relevant visualization for the data retrieved |
Use case guide#
This document is meant to guide the user through Scenario 3 - Low code data analytics, by presenting a high level overview of the main steps. As discussed in the use case description, the goal is to provide a tool that performs data analytics in a low code fashion.
- Step 1: Initialize the resources. Launch three instances – PgAdmin, PostgreSQL and Knime - from the Service Catalog section of the Portal, and verify that their status is ACTIVE in the My Services section to double-check that the deployed instance is ready to be used.
- Step 2: Connect PgAdmin to PostgreSQL. Copy the PostgreSQL host address from My Services, log in to the pgAdmin instance using the provided credentials, and register the PostgreSQL database by completing the configuration modal with the hostname, port, maintenance database, username, and password as specified.
- Step 3: Load the data. In pgAdmin, create a new data table within the public schema, define its schema by adding columns using SQL queries, and import data from a source CSV file into the table, ensuring proper file upload settings and format specifications for successful data integration.
- Step 4: Perform low code data analytics. Launch the Knime instance from My Services, install the JFree extension, and create a Knime workflow. Build the workflow by connecting nodes, such as PostgreSQL Connector, DB Table Selector, DB Table Reader, GroupBy, and GroupBy Bar Chart (JFreeChart), to interact with the PostgreSQL database, select tables, perform aggregations, and generate visualizations for data analysis, leveraging low-code tools and techniques.