redash, Re-imagine the Dash-board

source: https://github.com/getredash/redash

Visualisation has become the hottest trend in 2022 already and irrespective of the Data segment you live in, you will use Dashboard almost everyday.

Data Engineers, Data scientists, Data Analysts or Data Owners, all would need in common is the Dashboard.

Dashboard will help you in analysing your data. And take data driven decisions.

Data can come from a different sources as we different Data sources matching our use case.

For example we use Redshift as a Data warehouse, or we use Postgres for the backed up data. Or we have a Cassandra database to store real time.

But in the end it will all boil down to the presentation, on how we visualise the final output for the business to take the data driven decisions.

Enter, redash.

Introduction

Redash is designed to enable anyone, regardless of the level of technical sophistication, to harness the power of data big and small. SQL users leverage Redash to explore, query, visualize, and share data from any data sources. Their work in turn enables anybody in their organization to use the data. Every day, millions of users at thousands of organizations around the world use Redash to develop insights and make data-driven decisions.

Source: https://github.com/getredash/redash

Features

Web interface and UI based

redash is entirely the UI based and it requires 0 to very low level knowledge to build the dashboard.

Multi data source connections

We can connect the redash with different NOSQL or SQL or Bigdata warehousing tools to pull in the data.

Full list of connectors: https://redash.io/help/data-sources/querying/supported-data-sources

SQL Editor

User will write to queries to analyse the data with the business specific filter. This query can directly be run on top of the data sources.

Powerful visualisation

Building the Dashboard is very easy and using redash we can create powerful visualisation with different types of charts and with more control for the user to customise.

Creating and saving the queries for reuse

User can create the query and save them for later resue. and the same query can be an input for your dashbaord.

Build Dashboard on the go

For every query you write, we can create a visualisation by a click of the button.

Schedule the refresh of your dashboard

Once the queries are built and the visualisation are created we can schedule the queries easily using the UI.

API

Easy connectivity using the APIs.

Alerting upon the criteria

We can create an alert for any discrepancies in your data. It can alert the user by email, slack or webhook.

Setup

We will use Docker based setup

Install docker and docker compose

Install nodeJs

Install yarn and node.

npm install --global yarn@1.22.10

git clone https://github.com/getredash/redash.git
cd redash/
docker-compose up -d

Install node package

yarn --frozen-lockfile

Create database

# Create tables
docker-compose run --rm server create_db

# Create database for tests
docker-compose run --rm postgres psql -h postgres -U postgres -c "create database tests"

docker-compose up or docker-compose start

Redash is available at http://localhost:5000/

We still need to build the frontend assets at least once, as some of them used for static pages (login page and such):

yarn build
yarn start

Full setup: https://redash.io/help/open-source/dev-guide/docker

DEMO

Setup account

Home page

We shall use the Snowflake as the data source: https://signup.snowflake.com/

Once the data source is created now lets connect the redash with the snowflake.

Let’s run our first query.

Save and publish the query.

Let’s build the visualisation.

Add the visualisation in the dashboard.

Let’s create an alert.

If the count is greater than threshold 2 we need to be alerted.

Create the alert destination as EMAIL.

We can see all the data sources.

We can alter the saved query.

Let’s create a Location based dashboard.

We can define the refresh time as when we want our queries to be run.

Reference:

Ajith Shetty

Bigdata Engineer — Bigdata, Analytics, Cloud and Infrastructure.

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Ajith Shetty

Ajith Shetty

Bigdata Engineer — Love for BigData, Analytics, Cloud and Infrastructure. Want to talk more? Ping me in Linked In: https://www.linkedin.com/in/ajshetty28/