Accelerated Hadoop based Analytics

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Meet Jumbune

A Hadoop based solutions acceleration tool that helps enterprises to effortlessly solve every day's cluster, data & logic challenges, correctly build and efficiently run analytical applications.

Key Features


Optimize Job

Iteration based model to recommend most optimal configurations for custom analytics jobs considering actual workload on Hadoop cluster, resource utilization limits of user, observes the cluster for the recommendations.

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Analyze Cluster

Analysis and monitoring of Hadoop cluster, resource analysis of jobs, metered queue metrics, configuration recommendations & alerting, giving consumable analysis of the cluster which is much beyond cluster monitors.

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Analyze Data

Data quality check & cleansing may take 60% of data scientist time. Data quality suite to effectively monitor the quality of data on the data lake. It also provides the ability to profile the data to gain insights after Data load or regular ETL.

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Analyze Job

Production bugs, errors and failures results in significant loss in revenue and time. It computes execution flow analysis of analytical MapReduce jobs and distributed cluster wide execution profiling for quick logic faults detection.

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Unified Platform for Analytics

It integrates with the Hadoop technologies to provide a platform to correctly build complex analytical application, efficiently run and manage data & applications in the cloud hosted or on-premise Hadoop clusters.


Success for Everyone

Jumbune Benefits


Faster time to realize solutions

Rapidly build analytical solution providing correct business insights.Tear off time to detect Bad logic, Bad data.


Reduced total analytical cost

Assists IT team to quickly realize Enterprise Big Data based analytics strategy correctly & efficiently.


Supports all major distributions of Hadoop

Jumbune can be deployed on YARN and non-YARN versions of almost all major distributions of Hadoop.


Support for High Availability

Provides automatic fail-over to active agent and active namenode in the cluster in case of failures.


On premise and cloud support

Works well with on premise clusters as well as Azure, EC2 cloud based Hadoop based clusters.