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  5. Denodo vs Looker

Denodo vs Looker

OverviewDecisionsComparisonAlternatives

Overview

Looker
Looker
Stacks632
Followers656
Votes9
Denodo
Denodo
Stacks40
Followers120
Votes0
GitHub Stars0
Forks0

Denodo vs Looker: What are the differences?

# Introduction

1. **Integration Capabilities**: Denodo is primarily a data virtualization tool that integrates data from various sources in real-time, while Looker is a business intelligence tool focused on creating data visualizations and reports.
2. **Data Processing**: Denodo provides a unified interface for data processing, enabling users to query and access disparate data sources seamlessly. In contrast, Looker relies on a modeling layer to define relationships between data sets, which can sometimes lead to slower query performance.
3. **Data Transformation**: Denodo supports data transformation at the source level, allowing for data cleansing and enrichment before being accessed by users. Looker, on the other hand, focuses on data transformation within the tool itself through its modeling layer.
4. **User Interface**: Looker provides a user-friendly interface for creating and customizing data visualizations, making it easier for non-technical users to analyze data. Denodo, while powerful in data integration, may require more technical expertise to leverage its capabilities fully.
5. **Deployment Options**: Denodo can be deployed on-premises or in the cloud, providing flexibility in where data sources are accessed and processed. Looker is a cloud-native platform, which offers scalability and easier maintenance for organizations looking to manage their data in the cloud.
6. **Collaboration Features**: Looker offers strong collaboration features, such as shared data sets and scheduled data delivery, making it easier for teams to work together on data analysis projects. Denodo, while customizable, may require additional tools for collaboration and sharing of data insights.

In Summary, Denodo and Looker offer distinct advantages in data integration and visualization, with Denodo excelling in data virtualization capabilities and Looker focusing on intuitive data visualization and collaboration features.

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Advice on Looker, Denodo

Vojtech
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
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
Looker
Denodo
Denodo

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 the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.

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
Data virtualization; Data query; Data views
Statistics
GitHub Stars
-
GitHub Stars
0
GitHub Forks
-
GitHub Forks
0
Stacks
632
Stacks
40
Followers
656
Followers
120
Votes
9
Votes
0
Pros & Cons
Pros
  • 4
    Real time in app customer chat support
  • 4
    GitHub integration
  • 1
    Reduces the barrier of entry to utilizing data
Cons
  • 3
    Price
No community feedback yet
Integrations
No integrations available
DataRobot
DataRobot
AtScale
AtScale
Vertica
Vertica
Trifacta
Trifacta
Dremio
Dremio
Apache Kylin
Apache Kylin
SAP HANA
SAP HANA

What are some alternatives to Looker, Denodo?

Metabase

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

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

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

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.

Pandas

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Mode

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

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.

NumPy

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

AskNed

AskNed

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

Shiny

Shiny

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.

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