Splunk provides analytics capabilities through data models and pivot reporting. Data models encapsulate domain knowledge about data sources and allow non-technical users to interact with and report on data. Pivot provides a query builder interface for creating reports based on data models without using the Splunk search language. Data models define objects that map to events, searches, or groups of events/searches with constraints and attributes. Pivot reports generate optimized search strings from the data model objects.
Splunk Ninjas: New features, pivot, and search dojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Splunk Ninjas: New features, pivot, and search dojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Splunk Ninjas: New Features, Pivot, and Search DojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Splunk Ninjas: New Features, Pivot and Search DojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Splunk is a powerful platform that can harness your machine data and turn it into valuable information thereby enabling your business to make informed decisions, taking your organization from reactive to proactive. Just like any other platform, Splunk is only as powerful as the data it has access to, therefore in this session we will be conducting a walk thru of how to successfully on-board data, with samples of data ranging from simple to complex. We will also be taking a look at how to use common TA’s to bring valuable data into Splunk. This session is designed to give you a better understanding of how to onboard data into Splunk enabling you to unlock the power of your data.
Splunk Ninjas: New Features, Pivot, and Search DojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Splunk Ninjas: New Features, Pivot and Search DojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Splunk is a powerful platform that can harness your machine data and turn it into valuable information thereby enabling your business to make informed decisions, taking your organization from reactive to proactive. Just like any other platform, Splunk is only as powerful as the data it has access to, therefore in this session we will be conducting a walk thru of how to successfully on-board data, with samples of data ranging from simple to complex. We will also be taking a look at how to use common TA’s to bring valuable data into Splunk. This session is designed to give you a better understanding of how to onboard data into Splunk enabling you to unlock the power of your data.
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SplunkLive! Frankfurt 2018 - Data Onboarding OverviewSplunk
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CMGT410 v19Project Charter TemplateCMGT410 v19Page 2 of 3P.docxmccormicknadine86
CMGT/410 v19
Project Charter Template
CMGT/410 v19
Page 2 of 3Project Charter Template
Instructions: With your team members, create a project charter using the template below. Some example information has been provided to assist you. Please replace all example information in the template with information specific to the project charter you and your fellow team members create.1.0 Project Identification
Name
Company Web interface for big data analytics
Description
Design, develop, and implement a data analytics application leveraging the millions of data records in company databases.
Sponsor
Matthew Sullivan, David Dosaima
Project Manager
Daniel Kathii
Project Team Resources
2.0 Business Reasons for Project
· Too produce company analytic assessments based on database records.
· To process and understand millions of data records to make more informed business decisions
· To allow for more efficient data record analysis
· To allow for more efficient trend analysis3.0 Project Objectives (Purpose)
· To develop a web interface and application leveraging existing data storage repositories
· To develop a web interface for complex data queries leveraging Boolean and elastic search capabilities
· To develop a nodal analysis tool to visualize data records and databases objects.4.0 Project Scope
· In scope:
· Initiate web server/infrastructure for online dealings
· Provide development and user customer services options
· Market place
· Develop enterprise object model ontology
· Develop object driven search and discovery based on simple, complex and temporal database queries
· Out of scope:
· 3rd party recruitment options 5.0 Requirements – User Stories
User Story
Description
Search and Discovery
“As <user role – data analyst > I want to <action – discover data records through complex search criteria> so that <goal reduce time of relevant data discovery>”
Data Visualization
“As <user role – data analyst > I want to <action – view data records and links in a nodal data chart> so that <goal – data relationships can be viewed>”
Account lookup
“As <user role – UI account > I want to <action – view account info> so that <goal – management can query user adoption>”
Temporal Search and Discovery
“As <user role – data analyst > I want to <action – query data based on temporal filters> so that <goal – data can be filtered by time and space>”
Data Organization
“As <user role – data analyst > I want to <action – save, organize and save persistent searches> so that <goal – users can be notified of new data that meets historical/saved searches>”
6.0 Milestone Dates
Item
Milestone
Date
I.
Stand up cloud test/development server
1st Dec 2019
II.
Develop UI prototype
15th Mar 2020
III.
Networking/Information event for all departments
15th Apr 2020
IV.
Develop department toolkit, templates, resources
15th May 2020
V.
Implementation and communication to stakeholder groups
15th Jul 2020
VI.
Website launch
12th Sep 2020
VII.
Evaluation, consultations, lessons learned
1st ...
Cognos Framework Manager is a metadata modeling tool.Cognos Framework Manager provides the metadata model development environment for Cognos 8.A model is a business presentation of the information from one or more data sources. The model provides a business presentation of the metadata.The model is packaged and published for report authors and query users
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Getting Started with Splunk Enterprise
What is Splunk? At the end of this session you’ll have a high-level understanding of the pieces that make up the Splunk Platform, how it works, and how it fits in the landscape of Big Data. You’ll see practical examples that differentiate Splunk while demonstrating how to gain quick time to value.
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In the fast-paced, high demand environment of manufacturing, it’s almost impossible to find the time to gather large amounts of data and organize it into a common context. This session presents best practices for using Discoverant hierarchies and Direct Connects to provide your end-users with on-demand and scheduled self-service access to their contextualized data, freeing their time for the more important trending and analysis activities that enable improved process performance and predictability.
Splunk Ninjas: New Features, Pivot, and Search DojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
SplunkLive! Frankfurt 2018 - Get More From Your Machine Data with Splunk AISplunk
Presented at SpluknLive! Frankfurt 2018:
Why AI & Machine Learning?
What is Machine Learning?
Splunk's Machine Learning Tour
Use Cases & Customer Stories
Wrap Up
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Visit http://www.gtri.com/ for more information.
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4. Analytics Big Picture
Pivot
Build complex reports without the
search language
Data
Model
Provides more meaningful representation
of underlying raw machine data
Analytics
Store
Acceleration technology delivers up to
1000x faster analytics over Splunk 5
4
5. Operational Intelligence Across the Enterprise
[10/11/12
18:57:04
000000b0
UTC]
Raw
Data
IT professional
Create and share data models
Accelerate data models and custom
searches with the analytics store
Create reports with pivot
Analytics
Store
Developer
Leverage data models to
abstract data
Leverage pivot in custom apps
Data
Model
Pivot
Analyst
Create reports using pivot based on
data models created by IT
13. Splunk Search Language
search and filter | munge | report | clean-up
sourcetype=access_combined source = "/home/ssorkin/banner_access.log.2013.6.gz"
| eval unique=(uid + useragent) | stats dc(unique) by os_name
| rename dc(unique) as "Unique Visitors" os_name as "Operating System"
14. Hurdles
index=main source=*/banner_access* uri_path=/js/*/*/login/* guid=* useragent!=*KTXN* useragent!=*GomezAgent* clientip!=206.80.3.67
clientip!=198.144.207.62 clientip!=97.65.63.66 clientip!=175.45.37.78 clientip!=209.119.210.194 clientip!=212.36.37.138 clientip!=204.156.84.0/24
clientip!=216.221.226.0/24 clientip!=207.87.200.162 | rex field=uri_path "/js/(?<t>[^/]*)/(?<v>[^/]*)/login/(?<l>[^/]*)” | eval license = case(l LIKE "prod%" AND
t="pro", "enterprise", l LIKE "trial%" AND t="pro", "trial", t="free", "free”) | rex field=v "^(?<vers>d.d)” | bin span=1d _time as day | stats values(vers) as vers
min(day) as min_day min(eval(if(vers=="5.0", _time, null()))) as min_day_50 dc(day) as days values(license) as license by guid | eval type =
if(match(vers,"4.*"), "upgrade", "not upgrade") + "/" + if(days > 1, "repeat", "not repeat")| search license=enterprise | eval _time = min_day_50| timechart
count by type| streamstats sum(*) as *
•
Simple searches easy… Multi-stage munging/reporting is hard!
•
Need to understand data’s structure to construct search
•
Non-technical users may not have data source domain knowledge
•
Splunk admins do not have end-user search context
15. Data Model Goals
•
Make it easy to share/reuse domain knowledge
•
Admins/power users build data models
•
Non-technical users interact with data via pivot UI
17. What is a Data Model?
A data model is a search-time mapping of data onto a hierarchical structure
Encapsulate the knowledge
needed to build a search
Pivot reports are build on top
of data models
Data-independent
Screenshot here
18. A Data Model is a Collection of Objects
Screenshot here
26. Object Attributes
Auto-extracted – default and predefined fields
Eval expression – a new field based
on an expression that you define
Lookup – leverage an existing lookup
table
Regular expression – extract a new
field based on regex
Geo IP – add geolocation fields such
as latitude, longitude, country, etc.
27. Object Attributes
Set field types
Configure various flags
Note: Child object configuration can differ from parent
28. Best Practices
Use event objects as often as possible
– Benefit from data model acceleration
Resist the urge to use search objects instead of event objects!!
– Event based searches can be optimized better
Minimize object hierarchy depth when possible
– Constraint based filtering is less efficient deeper down the tree
Event object with deepest tree (and most matching results) first
– Model-wide acceleration only for first event object and its
descendants
29. Warnings!
Object constraints and attributes cannot contain pipes or subsearches
A transaction object requires at least one event or search object in the data model
Lookups used in attributes must be globally visible (or at least visible to the app
using the data model)
No versioning on data models (and objects)!
36. Using the Splunk Search Language
Object Search String
| datamodel <modelname> <objectID> search
Example:
| datamodel WebIntelligence HTTP_Request search
Behind the scenes:
sourcetype=access_* OR sourcetype=iis* uri=* uri_path=* status=* clientip=* referer=*
useragent=*
37. Under the hood: Pivot Search String Generation
Pivot search = object search + filters + reporting + formatting
Example:
(sourcetype=access_* OR sourcetype=iis*) status=2*
uri=* uri_path=* status=* clientip=* referer=* useragent=*
| stats count AS "Count of HTTP_Sucess" by ”useragent"
| sort limit=0 "useragent" | fields - _span
| fields "useragent" "Count of HTTP_Success"
| fillnull "Count of HTTP_Success"
| fields "useragent" *
38. Using the Splunk Search Language
Pivot Search String
| pivot <modelname> <objectID> [statsfns, rowsplit, colsplit, filters, …]
Example:
| pivot WebIntelligence HTTP_Request count(HTTP_Request) AS "Count of HTTP_Request" SPLITROW status
AS "status" SORT 0 status
Behind the scenes:
sourcetype=access_* OR sourcetype=iis* uri=* uri_path=* status=* clientip=* referer=* useragent=*
| stats count AS "Count of HTTP_Request" by "status"
| sort limit=0 "status" | fields - _span
| fields "status", "Count of HTTP_Request"
| fillnull "Count of HTTP_Request"
| fields "status" *
39. Warnings
• | datamodel and | pivot are generating commands
– They must be at the beginning of the search string
•
Use objectIDs NOT user-visible object names
41. Data Model on Disk
Each data model is a separate JSON file
Lives in <myapp>/local/data/models
(or <myapp>/default/data/models for
pre-installed models)
Has associated conf stanzas
and metadata
42. Editing Data Model JSON
At your own risk!
Models edited via the UI are validated
Manually edited data models: NOT SUPPORTED
Exception: installing a new model by adding the file to
<myapp>/<local OR default>/data/models is probably okay
43. Deleting a Data Model
Use the UI for appropriate cleanup
Potential for bad state if manually deleting model on disk
44. Interacting With a Data Model
Use data model builder and pivot UI – safest option!
Use REST API – for developers (see docs for details)
Use | datamodel and | pivot Splunk search commands
46. Data Model Acceleration
Turn on
acceleration via UI
Setting written to conf file
Admin or power user
Backend magic
Poll: are there new
accelerated
models?
Kick off collection
Acceleration
Non-technical user
Run search using on-disk acceleration
Run a pivot report
No acceleration
Kick off ad-hoc acceleration and run search
47. Model-Wide Acceleration
Only accelerates first eventbased object and descendants
Does not accelerate search and
transaction-based objects
Pivot search:
| tstats count AS "Count of HTTP_Success" from datamodel="WebIntelligence" where
(nodename="HTTP_Request") (nodename="HTTP_Request.HTTP_Success") prestats=true | stats count AS
"Count of HTTP_Success”
48. Ad-Hoc Object Acceleration
Kick off acceleration on pivot page (re) load for non-accelerated models
and search/transaction objects
Amortize cost of ad-hoc acceleration over repeated pivoting on
same object
Pivot search:
| tstats count AS "Count of HTTP_Success" from sid=1379116434.663 prestats=true | stats count AS
"Count of HTTP_Success”
Splunk 6 takes large-scalemachine data analytics to the next level by introducing three breakthrough innovations:Pivot – opens up the power of Splunk search to non-technical users with an easy-to-use drag and drop interface to explore, manipulate and visualize data Data Model – defines meaningful relationships in underlying machine data and making the data more useful to broader base of non-technical usersAnalytics Store – patent pending technology that accelerates data models by delivering extremely high performance data retrieval for analytical operations, up to 1000x faster than Splunk 5Let’s dig into each of these new features in more detail.
How does theAnalytics Store, Data Model and Pivot benefit users across the enterprise?Lets start with the IT Professional – this includes the Splunk Administrator or an advanced Splunk user that is familiar with SPL.Using Splunk 6 they can:Create data modelsShare data models with other users – delivering a consistent view of the dataAccelerate data models using the Analytics StoreCreate reports using Pivot (although being power users, they may prefer using SPL directly!)Next we have the enterprise developer.Using Splunk 6 they can:Leverage data models built by IT, making searches more portable (using common Data Models ensures predictability of results)Leverage the Pivot interface in custom enterprise appsFinally, there are additional users that can now benefit – for example, the business or data analyst. Using Splunk 6 they can:Create reports, dashboards, charts and other visualizations using the Pivot interface and based on data models that provide an abstracted view of the raw data. Splunk 6 is not meant to replace existing BI and Business Analytics tools, but it does provide new visibility, insights and intelligence from operational data that can be used by business analysts to augment these tools. Data from Splunk software can also be leveraged directly using the Splunk API and SDKs and integrated into existing business analytics tools. For example, the recently announced Pentaho Business Analytics for Splunk® Enterprise (http://apps.splunk.com/app/1554), enables business users to utilize Pentaho to rapidly visualize and gain additional insights from Splunk’s machine data platform using existing in-house skills.
-The Splunk search language is very expressive. - Can perform a wide variety of tasks ranging from filtering to data munging and reporting- There are various search commands for complex transformations and statistics (e.g. correlation, prediction etc)
What does the search do?Basically, first it normalizes the individual accesses, which should be representable as a model object.Next it aggregates by guid to create an "instance" object, which should be representable in a DM.It calculates a field on that instance object, "type".Then it builds a timechart. of those, using a special "_time" value.Low overhead to start but learning curve quickly gets steepObtaining website usage metrics should not require understanding Apache vs IIS formatAdmins won’t know apriori what questions are being asked of the data…so they can’t provide canned dashboards for all scenariosBackup search for example: eventtype=pageview | eval stage_2=if(searchmatch("uri=/download*"), _time, null()) | eval stage_1=if(searchmatch("uri=/product*"), _time, null()) | eval stage_3=if(searchmatch("uri=*download_track*"), _time, null()) | stats min(stage_*) as stage_* by cookie | search stage_1=* | where isnull(stage_2) OR stage_2 >= stage_1 | where isnull(stage_3) OR stage_3 >= stage_2 | eval stage = case(isnull(stage_2), "stage_1", isnull(stage_3), "stage_2", 1==1, "stage_3") | stats count by stage | reverse | accum count as cumulative_count | reverse | streamstats current=f max(cumulative_count) as stage_1_count last(cumulative_count) as prev_count
What are the important “things” in your data?E.g. WebIntelligence might haveHTTPAccessHTTPSuccessUser SessionHow are they related?There’s more than one “right” way to define your objects
Constraints filter down to a set of a dataAttributes are the fields and knowledge associated with the objectBoth are inherited!
A child object is a type of its parent object: e.g. An HTTP_Success object is a type of HTTP_AccessAdding a child object is essentially a way of adding a filter on the parentsA parent-child relationship makes it easy to do queries like “What percentage of my HTTP_Access events are HTTP_Success events?”
Constraints are essentially the search broken down into a hierarchy, attributes are the associated fields and knowledge
Arbitrary searches that include transforming commands to define the dataset that they representFix example here? TODO
Enable the creation of objects that represent transactionsUse fields that have already been added to the model via event or search objects
This is how we capture knowledge
Required: Only events that contain this field will be returned in PivotOptional: The field doesn't have to appear in every event Hidden: The field will not be displayed to Pivot users when they select the object in PivotUse this for fields that are only being used to define another attribute, such as an eval expression Hidden & Required: Only events that contain this field will be returned, and the field will be hidden from use in Pivot
Be careful about lookup permissions – must be available in the context where you want to use them