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JMP VS Databricks

Compare JMP VS Databricks and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

JMP logo JMP

JMP is a data representation tool that empowers the engineers, mathematicians and scientists to explore the any of data visually.

Databricks logo Databricks

Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?
  • JMP Landing page
    Landing page //
    2023-04-12
  • Databricks Landing page
    Landing page //
    2023-09-14

JMP features and specs

  • User-friendly Interface
    JMP offers a drag-and-drop interface that is intuitive and easy to navigate, making it accessible for both beginners and advanced users.
  • Comprehensive Data Visualization
    The software provides robust tools for data visualization, enabling users to create a wide variety of charts, graphs, and plots that can help in understanding complex data sets.
  • Advanced Statistical Analysis
    JMP includes a wide range of advanced statistical techniques, such as regression analysis, ANOVA, and multivariate methods, which are suitable for rigorous data analysis.
  • Integration with R and Python
    The software supports integration with R and Python, allowing users to leverage additional functionalities not available in JMP alone.
  • Interactive Data Exploration
    JMP enables interactive data exploration, allowing users to dynamically manipulate data sets and instantly see the results of their changes.
  • Quality Control Features
    The software includes numerous quality control tools, making it ideal for industries where maintaining high standards is critical.

Possible disadvantages of JMP

  • Cost
    JMP is a commercial software with a relatively high price point, which may be a barrier for small businesses or individual users.
  • Learning Curve
    Despite its user-friendly interface, JMP has a steep learning curve for those unfamiliar with statistical analysis and data visualization techniques.
  • Resource Intensive
    The software can be resource-intensive, requiring significant computational power and memory, especially when handling large datasets.
  • Limited Collaboration Features
    JMP lacks extensive features for real-time collaboration compared to some of the more modern data science platforms.
  • Package Ecosystem
    While JMP is powerful, its ecosystem of add-ons and packages is not as extensive as that of R or Python, which might limit its utility for some specialized tasks.
  • OS Compatibility
    JMP is primarily designed for Windows and MacOS. Users on other operating systems might face compatibility issues or may need to use workarounds.

Databricks features and specs

  • Unified Data Analytics Platform
    Databricks integrates various data processing and analytics tools, offering a unified environment for data engineering, machine learning, and business analytics. This integration can streamline workflows and reduce the complexity of data management.
  • Scalability
    Databricks leverages Apache Spark and other scalable technologies to handle large datasets and high computational workloads efficiently. This makes it suitable for enterprises with significant data processing needs.
  • Collaborative Environment
    The platform offers collaborative notebooks that allow data scientists, engineers, and analysts to work together in real-time. This enhances productivity and fosters better communication within teams.
  • Performance Optimization
    Databricks includes various performance optimization features such as caching, indexing, and query optimization, which can significantly speed up data processing tasks.
  • Support for Various Data Formats
    The platform supports a wide range of data formats and sources, including structured, semi-structured, and unstructured data, making it versatile and adaptable to different use cases.
  • Integration with Cloud Providers
    Databricks is designed to work seamlessly with major cloud providers like AWS, Azure, and Google Cloud, allowing users to easily integrate it into their existing cloud infrastructure.

Possible disadvantages of Databricks

  • Cost
    Databricks can be expensive, especially for large-scale deployments or high-frequency usage. It may not be the most cost-effective solution for smaller organizations or projects with limited budgets.
  • Complexity
    While powerful, Databricks can be complex to set up and manage, requiring specialized knowledge in Apache Spark and cloud infrastructure. This might lead to a steeper learning curve for new users.
  • Dependency on Cloud Providers
    Being heavily integrated with cloud providers, Databricks might face issues like vendor lock-in, where switching providers becomes difficult or costly.
  • Limited Offline Capabilities
    Databricks is primarily designed for cloud environments, which means offline or on-premise capabilities are limited, posing challenges for organizations with strict data governance policies.
  • Resource Management
    Efficiently managing and allocating resources can be challenging in Databricks, especially in large multi-user environments. Mismanagement of resources could lead to increased costs and reduced performance.

Analysis of JMP

Overall verdict

  • Overall, JMP is a highly regarded software package, especially among users in academic, engineering, and scientific research fields. It is considered excellent for visual data exploration and is often praised for its ability to handle complex statistical tasks with relative ease. However, some users may find it expensive, and it may not be the best option for those seeking free or open-source alternatives.

Why this product is good

  • JMP (jmp.com) is considered a strong choice for statistical analysis due to its comprehensive suite of tools for data visualization, exploratory data analysis, and analytic modeling. It is particularly known for its interactivity and user-friendly interface which helps make complex data more understandable. JMP supports a wide range of data analysis techniques and provides robust support for design of experiments (DOE), which is highly valued in research and development settings.

Recommended for

  • Researchers and analysts who require advanced statistical capabilities
  • Engineers and quality professionals involved in Six Sigma and other quality improvement initiatives
  • Academics and students looking for an educational tool offering rich functionality for data analysis
  • Organizations with a focus on design of experiments and predictive modeling

JMP videos

+ Add

Review Of The UAD Marshall JMP 2203 Plug-in From Universal Audio

More videos:

  • Demo - Marshall JMP-1 - In Depth Demo by Leon Todd
  • Review - Marshall JMP 1 Watt Combo - Blues Harmonica Amp Review

Databricks videos

+ Add

Introduction to Databricks

More videos:

  • Tutorial - Azure Databricks Tutorial | Data transformations at scale
  • Review - Databricks - Data Movement and Query

Category Popularity

0-100% (relative to JMP and Databricks)

JMP

Databricks

Technical Computing
100 100%
Technical Computing
0% 0
Data Dashboard
16 16%
Data Dashboard
84% 84
Database Tools
0 0%
Database Tools
100% 100
Numerical Computation
100 100%
Numerical Computation
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 JMP and Databricks

JMP Reviews

25 Best Statistical Analysis Software
JMP is a top-rated tool for data exploration and analysis, delivering dynamic data visualization and an extensive selection of statistical tools to empower users to make well-informed decisions based on their findings.
Source: www.scijournal.org
Top 10 Free Statistical Analysis Software 2023
5. JMP Scripting Language (JSL) scripting is supported for automation, customisation, and expanding functionality.
Source: fellowshipbard.com
9 Best Analysis Software for PC 2023
JMP is a software analysis software that can perform data manipulations and mining. It is a perfect alternative to MS Excel, which is famous for visualization. JMP is available on a free-trial and premium-based plan. The trial plan allows the user to interact with the software before deciding whether to purchase it.
Source: pdf.wps.com

Databricks Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Databricks notebooks are a popular tool for developing code and presenting findings in data science and machine learning. Databricks Notebooks support real-time multilingual coauthoring, automatic versioning, and built-in data visualizations.
Source: lakefs.io
7 best Colab alternatives in 2023
Databricks is a platform built around Apache Spark, an open-source, distributed computing system. The Databricks Community Edition offers a collaborative workspace where users can create Jupyter notebooks. Although it doesn't offer free GPU resources, it's an excellent tool for distributed data processing and big data analytics.
Source: deepnote.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Source: www.shipyardapp.com
Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Databricks is a simple, fast, and collaborative analytics platform based on Apache Spark with ETL capabilities. It accelerates innovation by bringing together data science and data science businesses. It is a fully managed open-source version of Apache Spark analytics with optimized connectors to storage platforms for the fastest data access.
Source: visual-flow.com
Top Big Data Tools For 2021
Now Azure Databricks achieves 50 times better performance thanks to a highly optimized version of Spark. Databricks also enables real-time co-authoring and automates versioning. Besides, it features runtimes optimized for machine learning that include many popular libraries, such as PyTorch, TensorFlow, Keras, etc.
Source: blog.bismart.com

Social recommendations and mentions

Based on our record, Databricks seems to be more popular. It has been mentiond 18 times since March 2021. 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.

JMP mentions (0)

We have not tracked any mentions of JMP yet. Tracking of JMP recommendations started around Mar 2021.

Databricks mentions (18)

  • Platform Engineering Abstraction: How to Scale IaC for Enterprise
    Vendors like Confluent, Snowflake, Databricks, and dbt are improving the developer experience with more automation and integrations, but they often operate independently. This fragmentation makes standardizing multi-directional integrations across identity and access management, data governance, security, and cost control even more challenging. Developing a standardized, secure, and scalable solution for... - Source: dev.to / 8 months ago
  • dolly-v2-12b
    Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: about 2 years ago
  • Clickstream data analysis with Databricks and Redpanda
    Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / almost 3 years ago
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / about 3 years ago
  • A Quick Start to Databricks on AWS
    Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / about 3 years ago
View more

What are some alternatives?

When comparing JMP and Databricks, you can also consider the following products

GraphPad Prism - Overview. GraphPad Prism, available for both Windows and Mac computers, combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization.

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

Stata - Stata is a software that combines hundreds of different statistical tools into one user interface. Everything from data management to statistical analysis to publication-quality graphics is supported by Stata. Read more about Stata.

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.

IBM SPSS Statistics - IBM SPSS Statistics is software that provides detailed analysis of statistical data. The company behind the product practically needs no introduction, as it's been a staple of the technology industry for over 100 years.

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.

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