Appfluent Visibility for Hadoop provides detailed insight into the user activity related to HiveQL including the SQL statements and their performance and the data sets being used in Hadoop. The solution also shows how the SQL statements correlate to the performance of associated MapReduce jobs and system resource consumption and performance.

Operations and development teams can quickly get a deep understanding of application activity and the performance characteristics for both HiveQL and MapReduce jobs. The ability to drill down for detailed information provides the means to quickly diagnose problems in production and development environments and make changes to boost performance.

The product’s easy to use dashboard templates deliver reports with details on activity, performance, resource consumption and data usage. Armed with this actionable data, organizations can optimize relevant workloads, avoid system disruption plus reduce their time to test, fix and roll-out query intensive applications across their user base.

Data Sheet

Now that a fast-growing number of enterprise users are analyzing data on Hadoop, operational and development teams need to proactively discover and respond to end-user complaints and performance bottlenecks —all while maintaining acceptable service levels for the business.

Applications that use Hive or MapReduce jobs experience many of the perfor-mance issues and bottlenecks that occur in traditional distributed systems while the number and volume of performance metrics are much larger and harder to diagnose and comprehend. It is nearly impossible to gain actionable insight into HiveQL activity and impact on MapReduce job performance.

Appfluent Visibility for HadoopAppfluent Visibility for Hadoop provides unprecedented insight

into Hive SQL and its impact on the performance of Hadoop

jobs. Hadoop is a flexible and economical platform for

enterprise data analysis and reporting.

Features & Benefits

• Correlate activity across users,

applications, HiveQL statements,

MapReduce jobs, databases and

tables used via an intuitive

point-click user interface

• Detect problematic user activity,

HiveQL and MapReduce jobs

exceeding acceptable performance

metrics to proactively address

system bottlenecks

• Drill down on HiveQL queries

and statements to associated

MapReduce jobs, and the reverse

to diagnose performance issues

• Compare activity and performance

metrics of two or more MapReduce

jobs to isolate variances in resource

consumption and diagnose

bottlenecks

• Analyze application activity and

resource consumption by business

units and data sets accessed in

Hadoop to guide optimization and

capacity

• Report on activity, performance,

resource consumption and data

usage via dashboard templates

Appfluent Visibility for Hadoop provides detailed insight into the user activity related to HiveQL including the SQL statements and their performance and the data sets being used in Hadoop. The solution also shows how the SQL statements correlate to the performance of associated MapReduce jobs and system resource consumption and performance.

Operations and development teams can quickly get a deep understanding of application activity and the performance characteristics for both HiveQL and MapReduce jobs. The ability to drill down for detailed information provides the means to quickly diagnose problems in production and development environments and make changes to boost performance.

The product’s easy to use dashboard templates deliver reports with details on activity, performance, resource consumption and data usage. Armed with this actionable data, organizations can optimize relevant workloads, avoid system disruption plus reduce their time to test, fix and roll-out query intensive applications across their user base.

About AppfluentAppfluent provides IT organizations with unprecedented visibility into their Big Data

systems to reduce costs. Appfluent helps companies put the right workload on the

right system, across data warehouses, business intelligence, and Hadoop. Seeing every

analytic and Extract, Transform and Load (ETL) that hits your Big Data, Appfluent can see

where you are wasting expensive storage and compute. With Appfluent, enterprises

can address exploding data growth with confidence, proactively manage performance

of BI and data warehouse systems, and realize the tremendous economies of Hadoop.

6001 Montrose Road, Suite 1000

Rockville MD 20852 USA

301-770-2888

www.appfluent.com

Now that a fast-growing number of enterprise users are analyzing data on Hadoop, operational and development teams need to proactively discover and respond to end-user complaints and performance bottlenecks —all while maintaining acceptable service levels for the business.

Applications that use Hive or MapReduce jobs experience many of the perfor-mance issues and bottlenecks that occur in traditional distributed systems while the number and volume of performance metrics are much larger and harder to diagnose and comprehend. It is nearly impossible to gain actionable insight into HiveQL activity and impact on MapReduce job performance.

“The penetration of Hadoop distributions is growing rapidly, now greater than 20% of the addressable market.”

— Gartner’s “IT Market Clock for Database Management Systems, 2013”

by Donald Feinberg & Eric Thoo