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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.

What is Azure Queue Storage?

Utilize Azure Queue Storage to build flexible applications that decouple functionalities, promoting improved resilience during high-demand periods. By designing your applications with scalability as a core principle, you enable various components to function independently, which opens up specific scaling strategies tailored to your needs. The asynchronous messaging capabilities of queue storage support seamless communication between different parts of your application, whether they are situated in the cloud, on local devices, in on-premises setups, or across mobile environments. Implementing Queue Storage can significantly enhance the efficiency of your service deployments, enabling your applications to manage unexpected surges in traffic and preventing servers from becoming overwhelmed by a sudden influx of requests. Moreover, keeping an eye on the queue length allows you to infuse agility into your application, enabling the dynamic addition or reduction of worker nodes based on fluctuating customer demands. This strategy not only boosts performance but also ensures that resource allocation is finely tuned to meet real-time requirements, leading to a more responsive and effective application overall. By leveraging these capabilities, you can create a robust ecosystem that adapts seamlessly to changing conditions in user traffic.

Media

Media

Integrations Supported

Acquia CDP
Apache Spark
Beats
Enterprise Recon
Galileo
Hadoop
IBM Cloud Object Storage
RadiantOne
SSIS Integration Toolkit
Switch Automation

Integrations Supported

Acquia CDP
Apache Spark
Beats
Enterprise Recon
Galileo
Hadoop
IBM Cloud Object Storage
RadiantOne
SSIS Integration Toolkit
Switch Automation

API Availability

Has API

API Availability

Has API

Pricing Information

$0.014 per hour
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/cloud/analytics-engine

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/services/storage/queues/

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

Categories and Features

Message Queue

Asynchronous Communications Protocol
Data Error Reduction
Message Encryption
On-Premise Installation
Roles / Permissions
Storage / Retrieval / Deletion
System Decoupling

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