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Description
IBM Analytics Engine offers a unique architecture for Hadoop clusters by separating the compute and storage components. Rather than relying on a fixed cluster with nodes that serve both purposes, this engine enables users to utilize an object storage layer, such as IBM Cloud Object Storage, and to dynamically create computing clusters as needed. This decoupling enhances the flexibility, scalability, and ease of maintenance of big data analytics platforms. Built on a stack that complies with ODPi and equipped with cutting-edge data science tools, it integrates seamlessly with the larger Apache Hadoop and Apache Spark ecosystems. Users can define clusters tailored to their specific application needs, selecting the suitable software package, version, and cluster size. They have the option to utilize the clusters for as long as necessary and terminate them immediately after job completion. Additionally, users can configure these clusters with third-party analytics libraries and packages, and leverage IBM Cloud services, including machine learning, to deploy their workloads effectively. This approach allows for a more responsive and efficient handling of data processing tasks.
Description
Failover Clustering in Windows Server (and Azure Local) allows a collection of independent servers to collaborate, enhancing both availability and scalability for clustered roles, which were previously referred to as clustered applications and services. These interconnected nodes utilize a combination of hardware and software solutions, ensuring that if one node encounters a failure, another node seamlessly takes over its responsibilities through an automated failover mechanism. Continuous monitoring of clustered roles ensures that if they cease to function properly, they can be restarted or migrated to uphold uninterrupted service. Additionally, this feature includes support for Cluster Shared Volumes (CSVs), which create a cohesive, distributed namespace and enable reliable shared storage access across all nodes, thereby minimizing potential service interruptions. Common applications of Failover Clustering encompass high‑availability file shares, SQL Server instances, and Hyper‑V virtual machines. This functionality is available on Windows Server versions 2016, 2019, 2022, and 2025, as well as within Azure Local environments, making it a versatile choice for organizations looking to enhance their system resilience. By leveraging Failover Clustering, organizations can ensure their critical applications remain available even in the event of hardware failures.
API Access
Has API
API Access
Has API
Integrations
Acquia CDP
Active Directory
Apache Spark
Galileo
Hadoop
IBM Cloud Object Storage
MINT
Microsoft 365
Microsoft Azure
Microsoft Hyper-V
Integrations
Acquia CDP
Active Directory
Apache Spark
Galileo
Hadoop
IBM Cloud Object Storage
MINT
Microsoft 365
Microsoft Azure
Microsoft Hyper-V
Pricing Details
$0.014 per hour
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
IBM
Founded
1911
Country
United States
Website
www.ibm.com/cloud/analytics-engine
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
learn.microsoft.com/en-us/windows-server/failover-clustering/failover-clustering-overview
Product 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
