Jumbune
| Developer(s) | Impetus Technologies |
|---|---|
| Stable release | 2.0 [1]
|
| Written in | Java |
| Engine | |
| Operating system | Linux |
| Website | jumbune |
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Jumbune [juhm-b-yoon][1] is an AIOps-focused Big Data APM available in enterprise grade and open-source offerings. Jumbune leverages Machine Learning to achieve performance improvement of Big Data Clusters and workloads like Spark, Hive, Tez, MapReduce, and others.
[2]Enterprises leverage Jumbune to get manifold business benefits, including:
- Faster analytics at reduced cost
- Maturing operational efficiency of Data Lakes
- Reducing time to analytics with ML-assisted recommendations
- Target-meeting, aggressive application SLAs
- Empowering teams to operate on clean data
Exclusivity brought by Jumbune:
- Proactive monitoring of BigData Clusters
- ML-based, Cost-Based Optimization for Hive, Tez, Spark, MapReduce, and other Big Data workloads
- Hundreds of critical recommendations for Business Applications, Infrastructure, and Hadoop services
- Pinpointing inefficiencies in applications and clusters
Jumbune provides a complete view of the whole BigData cluster and jobs at one go. This framework comprises various modules [3]
1. ML based Cluster Analysis: Improve productivity and efficiency of Infrastructure and Engineering teams by proactively showcasing deep insights and recommending optimizations for individual applications, Execution engines, Clusters, Infrastructure, and operating Queues.
- Highlighting features:
- Uncovers Inefficiency at Cluster, Application, and Queue level
- ML based Recommendations
- Deep Queue Analysis
- Optimal Tuning of individual workloads
- Proactive & Customizable Alerts…and many more
2. Optimize Job: Jumbune recommends the most optimal configuration based on application execution logistics, resource consumption, and real cluster state. It employs best-in-class ML algorithms on built heuristics to deduce the most comprehensive set of recommendations, which achieves the fastest execution time of your workloads.
- Highlighting feature:
- Most Comprehensive set of Tuning Parameters for Spark, Hive, MapReduce, Tez
- Tuning Parameter based on execution history
- Query recommendations
3. Manage Data Quality: Jumbune provides the most comprehensive set of automated data quality management tools which enables Dataops teams to easily profile and identify data quality. We establish data quality metering to analyze Quality KRAs even over incremental ingestions.
- Highlighting feature:
- Comprehensive suite of Data Quality functions
- Automated Data Cleansing
- Scheduled Data Validation
- Establish Quality metering over multiple ingestions
- Data Completeness, Accuracy, and Correctness checks
Prominent Users
Data Lake Cluster Administrator and DevOps
Data Engineer, Big Data Developer, Architects, and Data Scientists.
See also
- Big data
- Data Intensive Computing
- Apache Hadoop – Distributive computation framework
References
- ↑ "Jumbune Releases". jumbune.com.
- ↑ "Jumbune - Big Data Application Performance Management | Hadoop APM". jumbune.com. Retrieved 2019-09-16.
- ↑ "Jumbune Overview" (PDF). jumbune.org.
External links
This article "Jumbune" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Jumbune. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.
- ↑ "Jumbune - Big Data Application Performance Management | Hadoop APM". jumbune.com. Retrieved 2019-09-16.
- ↑ "Jumbune - Optimize Hadoop Solutions". jumbune.org. Retrieved 2019-09-16.
- ↑ Jumbune is an open-source Proactive ML based BigData platform performance accelerator & automated data quality management platform. Commercial offering is available at http://jumbune.com. More .., Impetus, 2019-07-02, retrieved 2019-09-16
