Data Models Breakout Session

Editor's Notes

• #2: Welcome to SplunkLive [City]. Thank you for taking the time to attend today’s event.
• #4: Splunk 6 takes large-scale machine 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 users Analytics Store – patent pending technology that accelerates data models by delivering extremely high performance data retrieval for analytical operations, up to 1000x faster than Splunk 5 Let’s dig into each of these new features in more detail.
• #5: How does the Analytics 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 models Share data models with other users – delivering a consistent view of the data Accelerate data models using the Analytics Store Create 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 apps Finally, 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.
• #7: How can you leverage Splunk?
• #8: How can you leverage Splunk?
• #13: How can you leverage Splunk?
• #14: - 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)
• #15: 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 steep Obtaining website usage metrics should not require understanding Apache vs IIS format Admins won’t know apriori what questions are being asked of the data…so they can’t provide canned dashboards for all scenarios Backup 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
• #19: What are the important “things” in your data? E.g. WebIntelligence might have HTTPAccess HTTPSuccess User Session How are they related? There’s more than one “right” way to define your objects
• #20: Constraints filter down to a set of a data Attributes are the fields and knowledge associated with the object Both are inherited!
• #21: A child object is a type of its parent object: e.g. An HTTP_Success object is a type of HTTP_Access Adding a child object is essentially a way of adding a filter on the parents A parent-child relationship makes it easy to do queries like “What percentage of my HTTP_Access events are HTTP_Success events?”
• #23: How can you leverage Splunk?
• #24: Constraints are essentially the search broken down into a hierarchy, attributes are the associated fields and knowledge
• #25: Arbitrary searches that include transforming commands to define the dataset that they represent Fix example here? TODO
• #26: Enable the creation of objects that represent transactions Use fields that have already been added to the model via event or search objects
• #27: This is how we capture knowledge
• #28: Required: Only events that contain this field will be returned in Pivot Optional: 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 Pivot Use 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
• #30: Be careful about lookup permissions – must be available in the context where you want to use them
• #32: How can you leverage Splunk?
• #43: How can you leverage Splunk?
• #49: Divanny will help make this slide MUCH prettier
• #50: This could be more slides, more details
• #52: This could be more slides, more details
• #53: How can you leverage Splunk?