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What is Leonovus Data Discovery Tool?

The Leonovus Data Discovery Tool conducts a thorough examination of Server Message Block's network-affiliated file storage, effectively categorizing all data present. This versatile tool can be utilized across various environments and is not tied to any specific vendor. By enabling users to visualize their file servers, it aids in identifying the types of files stored within them. The Data Discovery Tool equips you with crucial insights necessary for developing a file storage strategy that can effectively manage the challenges posed by rapid data growth, including the tiering of seldom accessed, or "cold," data. Furthermore, the Leonovus Smart Filer facilitates the seamless transition of this infrequently used data to more economical cloud or secondary storage solutions. In addition, the Data Discovery Tool constructs a data analytics framework for the file-based data service landscape, offering a comprehensive overview of the volume, age, activity level, and makeup of the data being served. Essentially, it provides a detailed profile of all data stored, which can significantly enhance data management practices. Consequently, this tool not only optimizes storage efficiency but also empowers organizations to make informed decisions regarding their data assets.

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.

Media

Media

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

Pricing not provided.
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

Leonovus

Date Founded

2008

Company Location

Canada

Company Website

www.leonovus.com/products/data-discovery-tool

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/cloud/analytics-engine

Categories and Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

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

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