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Qlik VS Jupyter

Compare Qlik VS Jupyter and see what are their differences

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Qlik logo Qlik

Qlik offers an Active Intelligence platform, delivering end-to-end, real-time data integration and analytics cloud solutions to close the gaps between data, insights, and action.

Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
  • Qlik Landing page
    Landing page //
    2023-06-28
  • Jupyter Landing page
    Landing page //
    2023-06-22

Qlik features and specs

  • Data Integration
    Qlik offers powerful data integration capabilities, allowing users to pull in data from multiple sources, clean it, and prepare it for analysis. This is particularly useful for organizations dealing with diverse datasets.
  • Associative Data Engine
    Qlik's unique associative data engine enables users to explore data freely, without the limitations of traditional hierarchical or query-based models. This feature ensures that all data relationships are maintained and accessible.
  • Interactive Visualizations
    Qlik provides highly interactive and customizable visualizations, making it easier for users to derive insights and share findings. The visualizations are intuitive and can be tailored to meet specific business needs.
  • AI Capabilities
    The platform includes AI-driven features like Insight Advisor, which helps users uncover insights and generate analytics automatically. This reduces the learning curve and makes advanced analytics more accessible.
  • Scalability
    Qlik is designed to scale from small teams to large enterprises. It supports both on-premises and cloud deployments, making it flexible to meet various business sizes and infrastructure preferences.

Possible disadvantages of Qlik

  • Complexity in Initial Setup
    The initial setup and configuration of Qlik can be complex and time-consuming, often requiring specialized knowledge or professional services to get started effectively.
  • Cost
    Qlik can be expensive, especially for smaller businesses. The cost includes not just licensing fees but also potential expenditures on training, deployment, and maintenance.
  • Learning Curve
    Although Qlik offers a powerful feature set, there is a steep learning curve for new users. Mastering the platform's full capabilities can take significant time and effort.
  • Performance Issues
    In some instances, users have reported performance issues, particularly when dealing with extremely large datasets or complex queries, which can hinder real-time analysis.
  • Limited Third-Party Integration
    While Qlik does support integration with various third-party tools, it may not be as extensive as some other analytics platforms. This can limit its usefulness in a highly diversified technology stack.

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

Analysis of Qlik

Overall verdict

  • Qlik is generally considered a good choice for data visualization and business intelligence needs.

Why this product is good

  • ["flexibility", "Qlik's platform allows for self-service data discovery, guided analytics, and embedded analytics."]
  • ["integration", "Qlik integrates well with various data sources, making it versatile for diverse data environments."]
  • ["user_friendly", "Qlik offers an intuitive interface that caters both to advanced users and beginners."]
  • ["active_community", "There is a strong community of Qlik users and developers who contribute to forums and share solutions."]
  • ["powerful_analytics", "It provides robust analytics capabilities with associative data indexing, which lets users easily explore data."]

Recommended for

  • Businesses seeking a comprehensive business intelligence tool.
  • Users who require a highly flexible, self-service analytics environment.
  • Organizations that need to integrate a wide array of data sources.
  • Companies looking for strong visual analytics capabilities.

Qlik videos

+ Add

A Day in the life of a Qlik Cloud User

More videos:

  • Demo - Qlik Sense Product Tour

Jupyter videos

+ Add

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

Category Popularity

0-100% (relative to Qlik and Jupyter)

Qlik

Jupyter

Data Dashboard
70 70%
Data Dashboard
30% 30
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Business Intelligence
100 100%
Business Intelligence
0% 0
Data Visualization
100 100%
Data Visualization
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Qlik and Jupyter

Qlik Reviews

10 Best Alternatives to Looker in 2024
Qlik: Qlik sets itself apart with its associative analytics engine, enabling users to uncover trends and patterns through intuitive exploration without predefined queries. This offers a more flexible and dynamic analytical process.
Source: www.sigmacomputing.com
Top 11 Fivetran Alternatives for 2024
Qlik provides three data integration products - Stitch (covered under Stitch) Talend Data Fabric (covered under Talend) and Qlik Replicate, which was originally Attunity. Qlik Replicate has both on-premises and cloud replication deployment options for streaming real-time data.
Source: estuary.dev
The 7 Best Embedded iPaaS Solutions to Consider for 2024
Description: Qlik offers a range of integration capabilities that span four product lines. The flagship product is Qlik Replicate, a tool that replicates, synchronizes, distributes, consolidates, and ingests data across major databases, data warehouses, and Hadoop. The portfolio is buoyed by Qlik Compose for data lake and data warehouse automation and Qlik Catalog for...
Source: solutionsreview.com
25 Best Reporting Tools for 2022
QlikView is a classic Reporting Tool that provides analytical solutions and allows you to develop appealing visualization from the data. It is an Enterprise Tool that converts raw data into a meaningful format. Some features of QlikView are as follows:
Source: hevodata.com
Top 10 Visual Analytics Provider For 2021
With some of the most sophisticated array of visualisations, Qlik is a pioneer in visualisation analytics software. With Qlik Sense and QlikView, it helps with a wide range and unorthodox ways of presenting data. Its ‘associative analytics engine’ in Qlik Sense moves away from a query-based approach and lets you explore data without any limitations. The engine lets you...
Source: analyticsindiamag.com

Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Source: searchbusinessanalytics.techtarget.com
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.
Source: towardsdatascience.com

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Qlik. While we know about 216 links to Jupyter, we've tracked only 1 mention of Qlik. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Qlik mentions (1)

  • GME FTD - Moving Daily Avg.
    All files was pulled into a program called : QLIK, qlik.com is the company and my company uses it for our reporting and our customer's reporting needs. Source: about 4 years ago

Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 5 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 12 months ago
View more

What are some alternatives?

When comparing Qlik and Jupyter, you can also consider the following products

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

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