The rising complexity of Big Data was preventing organizations from leveraging information for Data Analytics. Today, modern Data Analytics tools have become much more sophisticated to work with due to numerous data management controls. Although it is crucial for organizations to have complete control, it hinders the workflow of analysts in some specific use cases. Especially when working internally with a dedicated team that requires unlimited access to information, simple Data Analytics tools would expedite the insights delivery process by eliminating unnecessary clusters.
This is where Redash, a Data Analytics tool, assists organizations in simplifying the creation of dashboards with visualizations and sharing them among the team members. This tool has gained traction among thousands of organizations, including Cloudflare and SoundCloud, by allowing them to seamlessly write SQL queries and create dashboards to share with decision-makers.
This article will help you understand what Redash is and why it is uniquely positioned in the market to help organizations with data analysis.
Table of Contents
- Introduction to Redash
- Getting Started with Redash
- Redash vs Popular Data Analytics Tools
- Visualizations Supported by Redash
- Redash Pricing
- Limitations of Redash
Introduction to Redash
Redash is a Business Intelligence tool that houses many robust integration capabilities compared to existing Data Analytics platforms, making it a favorite for companies that have deployed numerous applications for managing their business processes. You can quickly integrate with Data Warehouses, write SQL queries to pull subsets of data for visualizations, and share dashboards with greater ease. It is designed in a way that it can be used by anyone regardless of the level of their technical expertise. The team’s main objective was to create a tool that makes it easy for data practitioners to collaborate and democratize access to insights for all teams.
Although Redash is a Web-based Business Intelligence tool, it offers an open-source version, which can be deployed by hosting it on your private Servers. For creating an instance, you can use AWS EC2 AMI, DigitalOcean, Docker, etc., with a minimum of 2 GB of RAM. The key features of Redash are as follows:
- There is no need for the users to set up an environment since this is a Web-based tool.
- This tool houses a Query Editor that enables users to compose database queries by leveraging the Schema Browser and autocomplete functionality.
- It gives users the ability to create visualizations using the drag-and-drop functionality and combine them into a single dashboard.
- Allows users to share visualizations and their associated queries easily and enable peer review of reports and queries.
- Allows automatic updation of charts and dashboards at custom time intervals.
More information about Redash can be found here.
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Getting Started with Redash
With Redash, users do not have to learn a new syntax while extracting information from data sources. For instance, if you are running PostgreSQL with Redash, you can use the syntax in accordance with the database engine. However, it has similar principles, i.e., connect, create, and share, followed by modern-day Data Analytics tools.
Adding a Data Source
- Go to Settings and navigate to the Data Sources Management page.
- Select your desired data source from numerous ready-to-connect sources from the list.
- Provide necessary details such as key or string based on the selected data source for integration.
Writing a Query
- Once connected, click on the Create button in the Navigation Bar.
- Now, click on Query and write the queries in the Query Editor to perform the necessary actions.
- Query results will appear in a simple table by default.
- Click the New Visualization button just above the results to select a visualization of your choice.
- Click the Edit Visualization option beneath each visualization to change the settings of the X-axis, Y-axis, etc.
Creating a Dashboard
- Click Create in the Navigation Bar to create a new dashboard, and then select Dashboard.
- You’ll now be asked to enter a name for the dashboard.
- Once you have provided a suitable name for your dashboard, you can add widgets from existing visualizations.
Sharing a Dashboard
- Share published dashboards with external users by clicking the Share icon in the upper-right corner.
Redash vs Popular Data Analytics Tools
Since Redash is a popular open-source application, it offers a wider range of benefits like self-hosting for free and allows businesses to customize the application according to their requirements. But, a lack of proper Data Management features makes it challenging for administrators to control the flow of data to ensure data privacy. Consequently, organizations that leverage Redash prioritize ease of use more than data governance. Other Data Analytics and Business Intelligence tools like Tableau, Microsoft Power BI, etc., allow administrators to apply sensitivity labels to data for classifying reports and visualizations. In addition, these tools are equipped with Machine Learning capabilities to simplify insights delivery with only a few clicks.
Visualizations Supported by Redash
Some of the visualizations supported by Redash are as follows:
Box or Whisker Plot is used for Exploratory Data Analysis to summarize data, especially a column of a dataset. With Boxplots, you can get a five-number summary that includes minimum, first quartile, median, third quartile, and maximum values of the data allowing you to better understand it.
Charts are widely used visualization formats that allow users to tell a story in the easiest way possible. In Redash, you can create numerous Charts such as Line, Bar, Area, Pie, Scatter, etc., to showcase trends to decision-makers.
A Cohort is created to analyze predetermined groups in different time intervals. These are common with E-Commerce websites for understanding the return of users over weeks or days.
It is one of the most straightforward visualization formats that display a number. A Counter is used in dashboards to display some of the important variables such as the Average Recurring Revenue (ARR) of a company or traffic of a website.
Funnel describes the Sales-related statistics like Conversion Rate, the performance of every stage of the Sales to identify potential problems in the Sales process.
To understand the geographical representation of collected information, Maps are created by almost every organization to understand their users and reachability. In Redash, you can create several types of Maps like Point, Line, Regional Maps, etc.
7) Pivot Table
With Pivot Tables, analyses of Big Data become a lot easier as it is often used to summarise rows and columns for calculating the sum, average, and other granularity metrics.
Sankey represents flow and relationships between two different elements. For instance, companies can visualize the amount of revenue generated by different products and services with Sankey diagrams/visualizations.
Used with Hierarchical Data, Sunburst Rings represent each level of the hierarchy. It showcases how the metric represented by the inner ring relates to the metric represented by the outer ring.
10) Word Cloud
Word Cloud is an eye-catching visualization format of different texts, where unique words are clustered together. In a Word Cloud, the most repeated words are bolder and more prominent and is used to represent the central idea of a document or discussion.
Redash has three pricing options:
- The Starter pack costs $49/month but only allows you to connect with three data sources.
- The Professional version costs $99/month and allows 10 data sources.
- The Business version is priced at $450/month and has unlimited data sources and dashboards.
Before opting for a premium plan, you can get a 30-day free trial, which includes every feature of the Business plan. Post the trial period, you have to choose from the three plans, according to your business and data requirements. Unlike other Data Analytics tools, Redash is not priced based on the number of users. Unlimited users empower organizations to enable self-service analytics across the departments.
Limitations of Redash
In spite of its seamless integration with Databricks, Redash does come with limitations. It lacks speed while working on Big Data because it parses every record before displaying the data. Nevertheless, it works flawlessly when you are not querying a colossal amount of data for analysis.
Apart from Big Data issues, you need to have an in-depth knowledge of SQL to use this tool correctly. This makes it less familiar when compared with other Data Analytics tools that support drag-and-drop for generating visualizations and creating dashboards. The need for SQL expertise limits its user bases, thereby making it difficult to really democratize data in every department of organizations.
Redash faces tough competition from other open-source Business Intelligence tools such as Apache Superset, Metabase, etc. Both Metabase and Superset support data sources similar to Redash. Metabase, especially, integrates with almost all data sources that Redash supports. However, with the backing of Databricks, Redash will witness a rapid adoption among organizations that already use Databricks for Data Science workflows.
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