Overview

Looker

Stacks632

Followers656

Votes9

Shiny

Stacks208

Followers228

Votes13

Looker vs Shiny: What are the differences?

Introduction

Here we will discuss the key differences between Looker and Shiny. Looker is a data exploration and business intelligence platform, while Shiny is a web application framework for creating interactive web applications using R. Let's explore the key differences below.

Data Visualization and Exploration: Looker is primarily focused on providing powerful data visualization and exploration capabilities. It offers various visualizations, including charts, dashboards, and reports, to analyze and present data in an intuitive way. On the other hand, Shiny allows developers to build custom interactive web applications using R, enabling data scientists to create unique and specialized visualizations.
User Interface Design: Looker offers a user-friendly and intuitive interface, designed to make it easy for non-technical users to explore and analyze data. It provides a drag-and-drop interface for creating and modifying visualizations. In contrast, Shiny offers a more customizable user interface, allowing developers to design and implement unique layouts and interactions tailored to specific needs.
Data Source Connectivity: Looker can connect to various data sources, including databases, cloud storage, and file systems. It offers native connectors to popular data sources like Snowflake, Redshift, and BigQuery. Shiny can also connect to multiple data sources but typically relies on R packages and libraries to establish these connections. It provides flexibility with a wide range of data source options available in R ecosystem.
Collaboration and Sharing: Looker provides features for collaboration and sharing of insights within an organization. Users can create and share reports, dashboards, and other visualizations with colleagues, enabling collaborative data analysis. Shiny, on the other hand, allows developers to create web applications that can be deployed and shared with others, providing a platform for sharing customized data-driven applications.
Coding and Customization: Looker primarily uses a visual interface and SQL-like syntax for data modeling and analysis. It offers a query language called LookML, which abstracts the complexity of SQL and allows users to define reusable data models. Shiny, being a web application framework, allows developers to write R code to create customized data workflows and interactivity, providing extensive flexibility for data scientists.
Integration with R Ecosystem: Shiny has seamless integration with the vast R ecosystem, allowing developers to leverage the extensive libraries and packages available in R for data analysis, machine learning, and statistical modeling. Looker, while offering some level of integration with R, is more focused on providing a self-contained data exploration and visualization platform.

In summary, Looker and Shiny differ in terms of their primary focus, user interface design, data source connectivity, collaboration and sharing capabilities, coding and customization options, and integration with the R ecosystem. While Looker is more focused on data exploration and visualization, Shiny allows for more customization and flexibility through code-based application development.

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Advice on Looker, Shiny

Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

353k views353k

Comments

Wei

CTO at Flux Work

Jan 8, 2020

Decided

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

230k views230k

Comments

Detailed Comparison

Looker	Shiny
We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.	It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.
Zero-lag access to data;No limits;Personalized setup and support;No uploading, warehousing, or indexing;Deploy anywhere;Works in any browser, anywhere;Personalized access points	-
Statistics
Stacks 632	Stacks 208
Followers 656	Followers 228
Votes 9	Votes 13
Pros & Cons
Pros 4 GitHub integration 4 Real time in app customer chat support 1 Reduces the barrier of entry to utilizing data Cons 3 Price	Pros 8 R Compatibility 3 Free 2 Highly customizable and extensible

What are some alternatives to Looker, Shiny?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

Mode

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Redash

Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.