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What is ZingChart?
Value Boxes are an essential feature for smaller datasets, providing clear visibility of each node's exact values. When enabled, these boxes prominently showcase the value associated with each node, making it easy for users to comprehend the data. However, in situations involving slightly larger datasets, displaying value boxes for every single node may create visual confusion, prompting the use of tooltips that allow users to view a node's value on demand. For instances where tooltips fall short in delivering sufficient information, crosshairs present a simple yet effective way to analyze a spectrum of values. In the case of very large datasets, where users need to zoom in significantly to identify node values, the ability to magnify specific points helps reduce the difficulty of focusing on the data. Furthermore, the inclusion of scroll bars significantly improves the navigation experience, enabling users to move through the dataset more efficiently. The preview window plays a dual role by assisting users in monitoring their current zoom level while also facilitating quick adjustments to the scroll position. Collectively, these features empower users to engage with the data efficiently, regardless of the size of the dataset, ensuring that they have the tools necessary to derive meaningful insights. This comprehensive approach to data interaction enhances user experience across various dataset scales.
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
Galileo
Hadoop
IBM Cloud Object Storage
RadiantOne
Switch Automation
Integrations Supported
Acquia CDP
Apache Spark
Galileo
Hadoop
IBM Cloud Object Storage
RadiantOne
Switch Automation
API Availability
Has API
API Availability
Has API
Pricing Information
$249 one-time payment
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
ZingChart
Company Website
www.zingchart.com
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