The document discusses the limitations of traditional monitoring frameworks and highlights the emergence of 'franken-monitors' due to the integration of best-of-breed tools, which lack cohesion and can't keep up with rapid technological changes. It introduces Opsdatastore as a solution for real-time data-driven IT operations, emphasizing its capabilities for analytics, monitoring, and troubleshooting across various layers of infrastructure. The document concludes with a call to action for feedback and offers a chance to win prizes for survey participation.

1. Transaction (Click) to Disk Troubleshooting
with AppDynamics and OpsDataStore
Bernd Harzog
Founder and CEO

2. My History with AppDynamics – Confidant and Fan
Strategy Consultant Product Selection Consultant

3. Questions for You
Does everything work as well as it is supposed to all of
the time?
Is your understanding of your infrastructure
performance as robust as is your understanding of
your application performance?
Would you be interested in understanding
infrastructure performance in the context of
transaction performance?

4. What Has Not Worked?

5. Blind Dinosaurs!
=
Frameworks made a promise
that could not be kept – that
one product can monitor
everything
Frameworks are out of date
with respect to current
application and infrastructure
environments
Frameworks stand no chance
of keeping up with the
accelerating pace of
innovation
Best of breed tools are
replacing the frameworks
Blind Dinosaur

6. -2.5%
-4.5%
-2.0%
-5.0%
Flat
+3.0%
11% share (of a $20B market) lost
by the Big 4 went to new best of
breed vendors (creating the
problem OpsDataStore solves)
-14.0%

7. Best of Breed Results in Franken-Monitors!
Franken-Monitors are redundant siloed management
tools that do not integrate with each other and that each
store their data in their own proprietary datastores
Franken-Monitors happened because customers
bought best of breed point tools to address gaps left by
IBM, BMC, HP and CA
Franken-Monitors cannot cope with the rapid pace of
change driven by Agile, DevOps, and hybrid/private
clouds
Franken-Monitors make it impossible for management
to keep up with the pace of innovation
With Franken-Monitors there is no architecture for
management and monitoring
How many monitoring tools do you own that produce
valuable metrics about an aspect of
your environment?
Network
Monitor
Storage
Monitor
Application
Monitor
Server
Monitor
Virtualization
Monitor
OS
Monitor
IT
Operations
Log
Monitor

8. This problem is becoming more important to solve and
more challenging to solve over time
Diversity and rapid change at the application layer
Pressure to respond to the business
DevOps and Agile
More languages and run times
More Layers of Abstraction
The JVM (the first one)
Server virtualization
Network virtualization
Storage virtualization
Docker
The Cloud
Dynamic and scaled out infrastructure
Constant change in the virtualized server,
network and storage layers
Automation
Your Cloud Public CloudHybrid Cloud

9. Data Driven IT Operations
to the Rescue

10. The Architecture for Data Driven IT Operations
Physical and
Virtual Storage
Instrumentation
Physical and
Virtual Server
Instrumentation
Physical and
Virtual Network
Instrumentation
Operating
System
Instrumentation
Application and
Transaction
Instrumentation
Data
Sources
Live Custom Dashboards – Specific to use cases, domains, and user requirements – Choice of BI ToolsVisualization
End-to-End
Monitoring
Capacity
Analytics
Automated
Root Cause
Automated
Remediation
IT Operations
Analytics
Cost
Management
Use
Cases
Data Driven IT
Operations
OpsDataStore
Data
Platform
Data
Types
Data from
Management
API’s
Agent Data Wire Data Synthetic Data
Manual and
Configuration
Data (CMDB)

11. The New Metric Collection Requirements
Continuous – Near Real Time – Streaming – Coarse grained
intervals (minutes) no longer frequent enough
Consistent – Gaps in metric collection are no longer acceptable
Deterministic – Rolled up averages are dangerously misleading
Accurate – measurement bias (i.e., time shifting) no longer
tolerable
Efficient – not influenced by the metric collection process –
“outside-in” observation preferable

12. Instrument for Response time (Latency),
Throughput and Congestion at Every Layer
Storage Array
Router/WAN
Network
Server OS
Hypervisor
Virtualization
Data Sources
Transaction and application response time and throughput
CPU, memory, network and disk contention (queuing)
CPU, memory, network, and disk contention (queuing)
Network performance and behavior of network users (apps)
WAN performance and End User Experience
Latency, throughput and contention
Application
OpsDataStore
• IT Operations Analytics
• Performance Management
• Capacity Management
• Service Troubleshooting
• Live Custom IT Dashboards

13. Evaluate Metrics based upon their Value,
Breadth of Applicability and Cost to Implement
LowValueoftheMetricHigh
Narrow Breadth of Applicability Ubiquitous
Open
Source/Free
$ $$
Application
Server
Datastore
Dynatrace AppDynamics
Coda Hale, StatsD
Corvil
Intel DCM
Network
SNMP
VMware VASA
Datastore HW
Netflow
VMware vCenter
CollectD
ExtraHop (STD)
ExtraHop (ITR)
CollectD (PL)

14. The Solution
OpsDataStore
The Real-Time Platform for Data Driven IT Operations

15. OpsDataStore – Solution Overview
Live Custom IT
Dashboards
Cross-Stack & End-To-
End Troubleshooting
Real-Time Fabric for Data
Driven IT Operations
Continuous Capacity
Optimization

16. OpsDataStore Ecosystem Strategy
OpsDataStore
Cirba
NPM Cloud Auto AutomationOps Mgmt. App Perf. Mgmt.Platforms
Sevone VMware ApptioRiverbed ServiceNowVMware
Riverbed Dell/EMC Puppet
VMware
New Relic
Virtual Inst.
Linux
AppDynamics
Moogsoft
Arista Dell
Cisco UCS
Nutanix
GigaMon Red Hat Chef
VMTurbo Dynatrace
Prelert
GigaMonWindows GigaMon
INETCO
CSCNagios
AppNeta
AppEnsure Big Panda
CSCEMC
Mirantis
Ansible
Apteligent
NetuitiveExtraHop
NetApp Kentik
Analytics Cost Mgmt. Service Mgmt.IPM
Zenoss
Zabbix
Corvil
ExtraHop

17. Key OpsDataStore Features
Open Ingest – collect data from any platform, management tool or API
Dynamic Object Model – all data is related at ingest time, stored in a graph
database and shown in a topology map
Highly Scalable Real Time Back End – designed for infinite horizontal scalability,
high ingest rate and low latency queries of metrics and relationships
Continuous Data Transformation – into a form usable by BI tools
Open Query Architecture – all data and topologies are accessible through
ODBC/JDBC
Easily Extensible User Interface - user interface (dashboards) built in the
leading BI tool (Tableau)

18. OpsDataStore Object Model – Organizing Streams
of IT Operations Data
Transaction
Application
Tier
Virtual
Server
ClusterData
Store
Data
Center
Tenant
Transaction Transaction
Physical
Server
Objects, Metrics, Configuration Items, and Relationships through Time

19. Real-Time QueryContinuous
Processing
Continuous
Ingest
OpsDataStore Product Architecture
Graph Database & Low Latency Scale Out Time Series Database
Streaming Writes
Object Model
Data Collector SDK
Errors
Capacity
UtilizationThroughput
Response TimeConfiguration
ExtraHop
AnyDataSource
IntelDCM
Dynatrace
AppDynamics
Query
VMware
ODBC API
RAM DB
Data Transformation
Aggregation
Statistical Computation
Baselining
Anomaly Detection JDBC API
Query

20. End-to-End Topology Map for Every Component

21. Metrics from the Transaction to the Datastore

22. Find Infrastructure Issues that Impact Applications

23. Your Data Your Way in Tableau
Build live
dashboards
in minutes
in your
choice of BI
tool
Empower
requesters
of IT data
with self-
service
Improve IT
decisions

24. Scalable and Automated Capacity Analytics

25. Use Cases for OpsDataStore
• Platform for IT Operations Analytics
• Platform for end-to-end (transaction to hardware)
monitoring and troubleshooting
• Platform for real-time and continuous capacity analysis
• Platform for live Custom IT Dashboards

26. Questions?
www.opsdatastore.com
bernd@opsdatastore.com
Send me an email for
a copy of the slides

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