Unlock the value in your big data reservoir using oracle big data discovery and rittman mead

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Unlock the Value in your Big Data Reservoir using Oracle Big Data Discovery and Rittman MeadMark Rittman, CTO, Rittman MeadMarch 2016

[email protected] www.rittmanmead.com @rittmanmead


2Mark Rittman, Co-Founder of Rittman MeadOracle ACE Director, specialising in Oracle BI&DW14 Years Experience with Oracle TechnologyRegular columnist for Oracle MagazineAuthor of two Oracle Press Oracle BI booksOracle Business Intelligence Developers GuideOracle Exalytics RevealedWriter for Rittman Mead Blog :http://www.rittmanmead.com/blogEmail : [email protected] : @markrittmanAbout the Speaker



3Started back in 1997 on a bank Oracle DW projectOur tools were Oracle 7.3.4, SQL*Plus, PL/SQL and shell scriptsWent on to use Oracle Developer/2000 and Designer/2000Our initial users queried the DW using SQL*PlusAnd later on, we rolled-out Discoverer/2000 to everyone elseAnd life was fun15+ Years in Oracle BI and Data Warehousing


4Over time, this data warehouse architecture developedAdded Oracle Warehouse Builder to automate and model the DW buildOracle 9i Application Server (yay!) to deliver reports and web portalsData Mining and OLAP in the databaseOracle 9i for in-database ETL (and RAC)Data was typically loaded from Oracle RBDMS and EBSIt was turtles Oracle all the way downThe Oracle-Centric DW Architecture


5Many customers and organisations are now running initiatives around big dataSome are IT-led and are looking for cost-savings around data warehouse storage + ETLOthers are skunkworks projects in the marketing department that are now scaling-upProjects now emerging from pilot exercisesAnd design patterns starting to emergeMany Organisations are Running Big Data Initiatives


6Typical implementation of Hadoop and big data in an analytic context is the data lakeAdditional data storage platform with cheap storage, flexible schema support + computeData lands in the data lake or reservoir in raw form, then minimally processedData then accessed directly by data scientists, or processed further into DWCommon Big Data Design Pattern : Data Reservoir


So What is a Data Reservoir?


What Does it Do?


And Does it Replace My Data Warehouse?


An Interesting Question.

T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India)E : [email protected] : www.rittmanmead.com


Meanwhile, back in the real world









Customer 360-Degree Insight





17Data from Real-Time, Social & Internet Sources is Strange

Single Customer View

Enriched Customer Profile

Correlating

ModelingMachineLearning

ScoringTypically comes in non-tabular formJSON, log files, key/value pairsUsers often want it speculativelyHavent though through final purposeSchema can change over timeOr maybe there isnt even oneBut the end-users want it nowNot when your ETL team are next free



18Hadoop & NoSQL better suited to exploratory analysis of newly-arrived data reservoir type-dataFlexible schema - applied by user rather than ETLCheap expandable storage for detail-level dataBetter native support for machine-learning anddata discovery tools and processesPotentially a great fit for our new and emergingcustomer 360 datasets, and great platform for analysisIntroducing Hadoop - Cheap, Flexible Storage + Compute



19Combine with DW for Big Data Management Platform



Start with pilot for area of the business that needs a single view of customersThen, over time, iterate and build out the Customer 360-degree viewDelivering a Successful Customer 360-Degree View

Start with a business area thatneeds a single customer view

Obtain clear understanding of customer online & offline behaviour

Build out Predictive Modelsand Decision Enginesto deliver value now

Build out Hadoop Data Reservoir, Feedsand link to DW + CRM

Iterate and Build-out,add new integrations,incrementally buildingcapability

Develop and Implement Strategy, Deliver Business ValueBuild DevOps Capability

Pilot & Quick Win

Create Full Production InfrastructurePilot (Virtualised / Commodity) Hadoop Infrastructure



21But These Data Sources are Strange



Correlating


ScoringTypically comes in non-tabular formJSON, log files, key/value pairsUsers often want it speculativelyHavent though through final purposeSchema can change over timeOr maybe there isnt even oneBut the end-users want it nowNot when your ETL team are next free






Correlating


Scoring






24Introducing the Data Lab for Raw/Unstructured Data


25Data loaded into the reservoir needs preparation and curation before presenting to usersSpecialist skills typically needed to ingest and understand data - and those staff are scarceHow do we staff and scale projects as our use of big data matures?But Working with Unstructured Textual Data Is Hard


Hold on


Haven't we heard this story before?



29Part of the acquisition of Endeca back in 2012 by Oracle CorporationBased on search technology and concept of faceted searchData stored in flexible NoSQL-style in-memory database called Endeca ServerAdded aggregation, text analytics and text enrichment features for data discoveryExplore data in raw form, loose connections, navigate via search rather than hierarchiesUseful to find out what is relevant and valuable in a dataset before formal modelingWhat Was Oracle Endeca Information Discovery?


30Proprietary database engine focused on search and analyticsData organized as records, made up of attributes stored as key/value pairsNo over-arching schema, no tables, self-describing attributes Endeca Server hallmarks:Minimal upfront designSupport for jagged dataAdministered via web service callsNo data left behindLoad and GoBut limited in scale (>1m records) what if it could be rebuilt on Hadoop?Endeca Server Technology Combined Search + Analytics


2012


2013


2014


2014


2014


2015


2015


and 2015


2016


40A visual front-end to the Hadoop data reservoir, providing end-user access to datasetsCatalog, profile, analyse and combine schema-on-read datasets across the Hadoop clusterVisualize and search datasets to gain insights, potentially load in summary form into DWOracle Big Data Discovery


41What Does Big Data Discovery Do?

Provide a visual catalog and search function across data in the data reservoirProfile and understand data, relationships, data quality issuesApply simple changes, enrichment to incoming dataVisualize datasets including combinations (joins)


Start with pilot for area of the business that needs a single view of customersThen, over time, iterate and build out the Customer 360-degree viewDelivering a Successful Customer 360-Degree View

Start with a business area thatneeds a single customer view

Obtain clear understanding of customer online & offline behaviour



Iterate and Build-out,add new integrations,incrementally buildingcapability

Develop and Implement Strategy, Deliver Business ValueBuild DevOps Capability

Pilot & Quick Win

Create Full Production InfrastructurePilot (Virtualised / Commodity) Hadoop Infrastructure



Delivering a Successful Customer 360-Degree View



Build DevOps Capability



44Provide a visual catalog and search function across data in the data reservoirProfile and understand data, relationships, data quality issuesApply simple changes, enrichment to incoming dataVisualize datasets including combinations (joins)What Does Big Data Discovery Do?


45Rittman Mead want to understand drivers and audience for their websiteWhat is our most popular content? Who are the most in-demand blog authors?Who are the influencers? What do they read? Three data sources in scope:Example Scenario : Social Media Analysis

RM Website Logs

Twitter Stream

Website Posts, Comments etc


46Datasets in Hive have to be ingested into DGraph engine before analysis, transformationCan either define an automatic Hive table detector process, or manually uploadTypically ingests 1m row random sample1m row sample provides > 99% confidence that answer is within 2% of value shownno matter how big the full dataset (1m, 1b, 1q+)Makes interactivity cheap - representative dataset Ingesting & Sampling Datasets for the DGraph Engine


47Ingested datasets are now visible in Big Data Discovery StudioCreate new project from first dataset, then add secondView Ingested Datasets, Create New Project


48Ingestion process has automatically geo-coded host IP addressesOther automatic enrichments run after initial discovery step, based on datatypes, contentAutomatic Enrichment of Ingested Datasets


49For the ACCESS_PER_POST_CAT_AUTHORS dataset, 18 attributes now availableCombination of original attributes, and derived attributes added by enrichment processInitial Data Exploration On Uploaded Dataset Attributes


50Data ingest process automatically applies some enrichments - geocoding etcCan apply others from Transformation page - simple transformations & Groovy expressionsData Transformation & Enrichment


51Uses Salience text engine under the coversExtract terms, sentiment, noun groups, positive / negative words etcTransformations using Text Enrichment / Parsing


52Choose option to Create New Attribute, to add derived attribute to datasetPreview changes, then save to transformation scriptCreate New Attribute using Derived (Transformed) Values

1

2

3


53Users can upload their own datasets into BDD, from MS Excel or CSV fileUploaded data is first loaded into Hive table, then sampled/ingested as normalUpload Additional Datasets

123


54Used to create a dataset based on the intersection (typically) of two datasetsNot required to just view two or more datasets together - think of this as a JOIN and SELECTJoin Datasets On Common Attributes


55Select from palette of visualisation componentsSelect measures, attributes for displayCreate Discovery Pages for Dataset Analysis


56Visualize and Interact With Hadoop Datasets


57BDD Studio dashboards support faceted search across all attributes, refinementsAuto-filter dashboard contents on selected attribute values - for data discoveryFast analysis and summarisation through Endeca Server technologyFaceted Search Across Entire Data Reservoir

Further refinement onOBIEE in post keywords3

Results now filteredon two refinements4


58Visual Analyzer also provides a form of data discovery for BI usersSimilar to Tableau, Qlikview etcInspired by BI elements of OEIDUses OBIEE RPD as the primary datasource, so data needs to be curated + structuredProbably a better option for users who arent concerned its big dataBut can still connect to Hadoop viaHive, Impala and Oracle Big Data SQLComparing BDD to Oracle Visual Analyzer


59Data in the data reservoir typically is raw, hasnt been organised into facts, dimensions yetIn this initial phase, you dont want to it to be - too much up-front work with unknown dataLater on though, users will benefit from structure and hierarchies being added to dataBut this takes work, and you need to understand cost/benefit of doing it now vs. laterManaged vs. Free-Form Data Discovery


60Transformations within BDD can then be used to create curated fact + dim Hive tablesCan be used then as a more suitable dataset for use with OBIEE RPD + Visual AnalyzerOr exported then in to Exadata or Exalytics to combine with main DW datasetsExport Prepared Datasets Back to Hive, for OBIEE + VA


61Users in Visual Analyzer then havea more structured dataset to useData organised into dimensions, facts, hierarchies and attributesCan still access Hadoop directlythrough Impala or Big Data SQLBig Data Discovery though was key to initial understanding of dataFurther Analyse in Visual Analyzer for Managed Dataset


62Oracle Big Data Discovery used to go back to the raw event data add more meaningEnrich data, extract nouns + terms, add reference data from file, RDBMS etcUnderstand sentiment + meaning of tweets, link disparate + loosely coupled eventsFaceted search dashboardsOracle BDD for Data Wrangling + Data Enrichment



63Previous counts assumed that all tweet references equally importantBut some Twitter users are far more influential than othersSit at the centre of a community, have 1000s of followersA reference by them has massive impact on page viewsPositive or negative comments from them drive perceptionCan we identify them?Potentially reach out with analyst programStudy what website posts go viralUnderstand out audience, and the conversation, betterBut Who Are The Influencers In Our Community?



64Rittman Mead website features many types of contentBlogs on BI, data integration, big data, data warehousingOp-Eds (OBIEE12c - Three Months In, Whats the Verdict?)Articles on a theme, e.g. performance tuningDetails of new courses, new promotionsDifferent communities likely to form around these content typesDifferent influencers and patterns of recommendation, discoveryCan we identify some of the communities, segment our audience?What Communities and Networks Are Our Audience?



65Graph Example : RM Blog Post Referenced on Twitter

Lifting the Lid on OBIEE Internals with Linux Diagnostics Tools http://t.co/gFcUPOm5pI

0000Page Views

1000Page Views

Follows

2000Page Views

Follows

3000Page Views



66Network Effect Magnified by Extent of Social Graph


3000Page Views7005Page Views




67Retweets by Influential Twitter Users Drive Visits


3000Page Views

Retweet5003Page Views

RT: Lifting the Lid on OBIEE Internals with Linux Diagnostics Tools http://t.co/gFcUPOm5pI



68Retweets, Mentions and Replies Create Communities

RetweetReplyMentionReply#bigdatasql

ReplyMentionMentionMentionMention#thatswhatshesaid



69Property Graph Terminology


Mentions


RetweetsNode, or VertexDirected Connection, or EdgeNode, or Vertex



70Different types of Twitter interaction could imply more or less influence

Retweet of another users Tweet implies that person is worth quotingor you endorse their opinion

Reply to another users tweet could be a weaker recognition of that persons opinion or view

Mention of a user in a tweet is a weaker recognition that they are part of a community / debateDetermining Influencers - Factors to Consider



71Relative Importance of Edge Types Added via Weights


Mentions, Weight = 30


Retweet, Weight = 100Edge PropertyEdge Property



72Graph, spatial and raster data processing for big dataRuns on-prem, or in Oracle Big Data Cloud ServiceInstallable on commodity cluster using CDHData stored in Apache HBase or Oracle NoSQL DBComplements Spatial & Graph in Oracle DatabaseDesigned for trillions of nodes, edges etcOut-of-the-box spatial enrichment servicesOver 35 of most popular graph analysis functionsGraph traversal, recommendationsFinding communities and influencers, Pattern matchingOracle Big Data Spatial & Graph



73Calculating Top 10 Users using Page Rank Algorithm

Top 10 influencers: markrittman rmoff rittmanmead mRainey JeromeFr Nephentur borkur BIExperte i_m_dave dw_pete



74Visualising the Social Graph Around Particular Users



75Calculating Shortest Path Between Users



76Edge Bundling to Better Illustrate Connection Frequency



77Determining Communities via Twitter Interactions



78Determining Communities via Twitter Interactions

Clusters based on actual interaction patterns, not hashtags Detects real communities, not ones that exist just in-theory



79Extend your organisations reach into your data with Oracle Big Data Discovery, Cloudera Hadoop and the Rittman Mead Big Data Rapid Start.The Big Data Rapid Start is a fixed price, two week engagement delivered by Rittman Meads team of Oracle, Big Data and Data Discovery consultants, designed to quickly provide everything required to begin discovering the hidden value of your data.Move forward with confidence in the technology, process and application of Big Data Discovery with the support of the worlds leaders.Big Data Rapid Start from Rittman Mead


80Articles on the Rittman Mead Bloghttp://www.rittmanmead.com/category/oracle-big-data-appliance/http://www.rittmanmead.com/category/big-data/http://www.rittmanmead.com/category/oracle-big-data-discovery/Rittman Mead offer consulting, training and managed services for Oracle Big DataOracle & Cloudera partnershttp://www.rittmanmead.com/bigdataAdditional Resources


Unlock the Value in your Big Data Reservoir using Oracle Big Data Discovery and Rittman MeadMark Rittman, CTO, Rittman MeadMarch 2016



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Unlock the value in your big data reservoir using oracle big data discovery and rittman mead