Observability and Continuous Tuning
for the Big Data Analytics Stack

Real-time visibility for troubleshooting, debugging, and planning.
Automated tuning for optimal performance on-prem and in the cloud.

Run More Apps, Track Spend, and Manage Costs

50%

Automatic Throughput
Improvement

100%

Visibility for
Accurate Chargeback

$10M+

Infrastructure
Savings

On Premises and In the Cloud

Analytics Stack Performance Products

Platform
Spotlight

platform spotlight dashboard icon

Diagnose issues
and make
resource decisions.

Capacity
Optimizer

capacity optimizer dashboard icon

Run more
workloads with
continuous tuning.

Query
Spotlight

query spotlight dashboard icon

Understand query
execution and
DB performance.

Streaming
Spotlight

home streaming screen

Get near
real-time visibility
into Kafka clusters.

Application
Spotlight

application spotlight dashboard icon

Get a 360° view
of your applications.

Customer Success

customer_nbc

Pepperdata Big Data Performance Report 2020

big data report home icon

“Typically, with 95% of jobs, there is little wastage. Major wastage is often in only 5% to 10% of total jobs. This is why optimization is inherently such a needle-in-a-haystack challenge, and why machine learning can be such a help.” – Pepperdata Big Data Performance Report 2020

This report reveals valuable insights regarding the condition of enterprise workloads that lack the benefits of observability and continuous tuning. It also reveals the enormous potential to optimize workloads and cut that waste.

photo-content-image

Achieve Big Data Success

Pepperdata products provide a 360° degree view of your platform and applications with continuous tuning, recommendations, and alerting.

What’s New

resources 05

News

Pepperdata Releases Inaugural “Big Data Performance Report” 2020

Review of Big Data Workloads in the Cloud Exposes Enormous Waste, Opportunities for Optimization

resources 04

Webinars

Reduce the Runaway Waste and Cost of Autoscaling

Pepperdata Field Engineer, Kirk Lewis
Tuesday August, 11 2020 @ 10 am PT