IBM Analytics Engine

Separate compute from storage to flexibly build, scale and maintain your analytics applications

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Overview

Analytics unleashed on demand

IBM Analytics Engine delivers scalable Apache Spark environments by separating compute and storage to help control costs. Store data in object storage and spin up compute clusters only when needed. Usage-based consumption adds flexibility and predictable spending for large-scale analytics.

Features

Scale, customize, and accelerate open-source analytics

Build on an ODPi-compliant stack with pioneering data science tools with the broader Apache Spark ecosystem.

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Define clusters based on your application’s requirement. Choose the appropriate software pack, version, and size of the cluster. Use as long as required and delete as soon as application finishes jobs.

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Configure clusters with third-party analytics libraries and packages as well as IBM’s own enhancements. Deploy workloads from IBM Cloud services, such as machine learning.

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Use cases

Discover ways to scale analytics smarter

Enable scalable analytics with flexible compute, elastic clusters, and efficient resource usage. Adapt quickly to changing workload demands, strengthen security, reduce infrastructure complexity, and optimize data processing environments while maintaining cost control and operational flexibility.
Flexible compute scaling for analytics

Run analytics workloads with compute-only clusters that scale on demand without storing data locally. Eliminate the need for cluster upgrades and quickly provision resources as workloads evolve, enabling more agile and efficient data processing at scale.
Optimize storage-heavy analytics workloads

Support I/O-intensive analytics by scaling object storage independently from compute resources. Provision storage as needed without paying for unused compute cycles, helping teams improve cost efficiency while managing growing volumes of enterprise data.
Scale clusters dynamically with live demand

Adapt to changing analytics workloads by adding or removing data nodes through REST APIs. Maintain flexibility and efficiency with elastic clusters that respond to real-time demand while reducing operational overhead through stateless compute environments.
Simplify secure access across environments

Strengthen security with a multilayered approach that simplifies cluster protection and access management. Enable more granular control across environments while reducing the complexity and operational costs associated with securing analytics infrastructure.
Avoid lock-in with flexible cluster setups

Support diverse analytics requirements by creating clusters tailored to each workload. Run multiple software versions independently without forcing all jobs into a single configuration, improving flexibility and reducing dependency on fixed environments or architectures.
Control Spark costs with serverless usage

Optimize Apache Spark spending with serverless environments that consume compute resources only when applications run. Scale usage based on workload demand and pay only for the resources used, improving cost predictability and operational efficiency.

Pricing

Explore pricing for scalable analytics

Review pricing options for Apache Spark environments built to scale with your analytics workloads. Explore flexible consumption models, serverless configurations, and deployment choices that help manage costs while supporting performance, elasticity, and enterprise data processing needs.

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