Jumbune on HDInsight helps enterprises harness more power from HDInsight-based Big Data solutions. Enterprises leverage Jumbune to get multifold business benefits, including:

• Faster analytics at reduced cost
• Maturing operational efficiency of Azure Data Lakes
• Reducing time to analytics with ML-assisted recommendations
• Target-meeting, aggressive application SLAs
• Empowering teams to operate on clean data

• Proactive monitoring of HDInsight 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

At a high level, Jumbune assists DevOps, DataOps and Analytics teams in following ways,

1. ML based Cluster Analysis: Improve productivity and efficiency of Infrastructure, Engineering teams by proactively showcasing deep insights and recommending optimizations for individual applications, Execution engines, Cluster, 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 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 restructuring recommendations

3. Manage Data Quality: Jumbune provides most comprehensive set of automated data quality management tools which enables Dataops team to easily profile and identify quality of data. 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

4. Analyze Job: This feature correlates the data with execution flow inside the MapReduce code to build execution data flow graph