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BrandMap® 10Researchers globally opt for this software due to its intuitive interface that facilitates rapid analysis and the creation of visually appealing biplots, correspondence maps, and MCA layouts. This 64-bit application is compatible with both MAC and PC platforms. The Brand Projector I functionally displays and computes essential characteristics for brand repositioning on a visual map. Meanwhile, Brand Projector II offers an interactive experience where researchers can adjust attributes and observe how the brand dynamically shifts in relation to the changes made. This combination of features makes the program an invaluable tool for those in the research community.
What is Matplotlib?
Matplotlib is a flexible library that facilitates the creation of static, animated, and interactive graphs in Python. It not only makes it easy to generate simple plots but also supports the development of intricate visualizations. A wide range of third-party extensions further amplifies Matplotlib's functionality, offering sophisticated plotting interfaces like Seaborn, HoloViews, and ggplot, as well as mapping and projection tools such as Cartopy. This rich ecosystem empowers users to customize their visual outputs according to individual requirements and tastes. Additionally, the continuous growth of the community around Matplotlib ensures that innovative features and improvements are regularly introduced, enhancing the overall user experience.
What is IBM Analytics Engine?
IBM Analytics Engine presents an innovative structure for Hadoop clusters by distinctively separating the compute and storage functionalities. Instead of depending on a static cluster where nodes perform both roles, this engine allows users to tap into an object storage layer, like IBM Cloud Object Storage, while also enabling the on-demand creation of computing clusters. This separation significantly improves the flexibility, scalability, and maintenance of platforms designed for big data analytics. Built upon a framework that adheres to ODPi standards and featuring advanced data science tools, it effortlessly integrates with the broader Apache Hadoop and Apache Spark ecosystems. Users can customize clusters to meet their specific application requirements, choosing the appropriate software package, its version, and the size of the cluster. They also have the flexibility to use the clusters for the duration necessary and can shut them down right after completing their tasks. Furthermore, users can enhance these clusters with third-party analytics libraries and packages, and utilize IBM Cloud services, including machine learning capabilities, to optimize their workload deployment. This method not only fosters a more agile approach to data processing but also ensures that resources are allocated efficiently, allowing for rapid adjustments in response to changing analytical needs.
Integrations Supported
Acquia CDP
Apache Spark
Comet LLM
Dash
Galileo
Hadoop
IBM Cloud Object Storage
Kedro
MLJAR Studio
PaizaCloud
Integrations Supported
Acquia CDP
Apache Spark
Comet LLM
Dash
Galileo
Hadoop
IBM Cloud Object Storage
Kedro
MLJAR Studio
PaizaCloud
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Free Trial Offered?
Free Version
Pricing Information
$0.014 per hour
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Matplotlib
Company Website
matplotlib.org
Company Facts
Organization Name
IBM
Date Founded
1911
Company Location
United States
Company Website
www.ibm.com/cloud/analytics-engine
Categories and Features
Data Visualization
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery
Categories and Features
Data Discovery
Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics
Data Visualization
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery