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OAC Workshop - From A to Z

A23789f299ed06fe7d9f1c6940440bfa?s=47 FTisiot
March 12, 2019

OAC Workshop - From A to Z



March 12, 2019


  1. @rittmanmead Oracle Analytics Cloud Workshop AnD Summit 2019

  2. @rittmanmead !2 Francesco Tisiot BI Tech Lead at

    Rittman Mead Verona, Italy Rittman Mead Blog 10 Years Experience in BI/Analytics @FTisiot Oracle ACE
  3. @rittmanmead About Rittman Mead !3 Rittman Mead is

    a data and analytics company who specialise in data visualisation, predictive analytics, enterprise reporting and data engineering. We use our skill, experience and know-how to work with organisations across the world to interpret their data. We enable the business, the consumers, the data providers and IT to work towards a common goal, delivering innovative and cost-effective solutions based on our core values of thought leadership, hard work and honesty. We work across multiple verticals on projects that range from mature, large scale implementations to proofs of concept and can provide skills in development, architecture, delivery, training and support.
  4. @rittmanmead !4 •OAC Overview •Versions, Licensing and Provisioning

    •Migrating to the Cloud •Importing Data Sources •Mode 1 and Mode 2 Overview •Report Writing •Oracle DV •Mashups, Dataflow and OAC Integration •Advanced Analytics / Machine Learning Course Agenda
  5. @rittmanmead INTRO 101 - OAC Overview and Features

  6. @rittmanmead !6 •OAC Product Overview •OAC Key Components

    •OAC Comparison to OBIEE Agenda - OAC Overview
  7. @rittmanmead !7 •Analytics “Platform” •Fully scalable •Can be

    used in the cloud, on premise or data centre Oracle Analytics Cloud Service
  8. @rittmanmead !8 • Functionality includes (but not limited

    to): ‣ Oracle Cloud ‘My Services’ - Used to create instances (similar to SSI’s in OBIEE 12c) ‣ Oracle Analytics Cloud Console - Used to manage datasources - Manage Users - Manage Backups/Restores via snapshots ‣ Enterprise Reporting (BICS) - OBI Data Models (RPD) - BI Publisher - OBIEE Analysis/Dashboards ‣ Oracle Data Visualiser - Data Discovery, Visualisation and Storyboarding - Mashups - Data preparation Oracle Analytics Cloud Service Functionality
  9. @rittmanmead !9 •Used to manage the platform •Track

    Billing •User Metrics •Environment Up/Down Time Oracle Cloud ‘My Services’
  10. @rittmanmead !10 •Web based management tool ‣Manages: -DV

    Projects -Data Sources -Data Sets -Users -Application Roles Oracle Analytics Cloud Console
  11. @rittmanmead !11 •OBIEE in the cloud •Lift and

    Shift from existing OBIEE instances •Full functional Analysis & Dashboards OAC Enterprise Reporting
  12. @rittmanmead !12 •Dynamically visualise data as you drag

    and drop attributes and measures •Quickly create visualisations to distribute to other users •Use custom data sources, e.g. spreadsheets or database •Also includes Data Visualiser Desktop OAC Oracle Data Visualiser
  13. @rittmanmead !13 •Import Data from Files or Database

    •Create Relationships •Add Data Filters •Add Calculations OAC Data Visualisation - Data Preparation
  14. @rittmanmead !14 Two Service Options Analytics Cloud Classic

    Analytics Cloud Services managed by Oracle: Backup & Recovery Service Monitoring Patching & Upgrades Test & Production instances Based on Oracle Cloud Infrastructure (OCI) Services managed by You: Based on Oracle Cloud Infrastructure Classic
  15. @rittmanmead !15 Three Edition Options Enterprise Edition Data

    Lake Edition Standard Edition Data Discovery Data Preparation What-If Planning Big Data Storage Data Transformation via Apache Spark Data Lake Connectivity Enterprise Analysis & Dashboarding Published Reporting Day by Day
  16. @rittmanmead !16 Two Purchasing Options Monthly Flex Pay

    As You Go Based on Universal Credits model No minimum tenure Payments made in arrears Based on consumption Suitable for: Rapid Prototyping Testing & Sampling Elastic Scalable Based on Universal Credits model 12 month minimum tenure Payments made in advance Unused credits are forfeited Suitable for: Predictable, production workloads Long running platforms
  17. @rittmanmead !17 • OAC instances are provisioned in

    Oracle Compute Units (OCPU’s) shapes to satisfy different requirements • The shape can be altered after service creation ‣ Flex capacity up (or down) based on immediate need Several Shape Options
  18. @rittmanmead !18 • Mobile Web & BI Mobile

    App ‣ All DV projects will auto-render on mobile devices ‣ The heritage mobile app supports all OAC content • Synopsis Mobile App ‣ Automatic Excel/CSV ingestion & analysis ‣ Extending to all DV supported sources • Day by Day ‣ Included within Enterprise Edition ‣ Search driven analytics ‣ Voice recognition allows you to verablise questions ‣ Embedded learning enables a tailored experience Mobile Options
  19. @rittmanmead !19 • Secure Access ‣ Remote hosts

    access cloud via SSH ‣ Specific access rules can be configured Security & Access • Users & Roles in OAC can be managed via two methods: ‣ Embedded Weblogic LDAP server ‣ Oracle Identity Cloud Service (IDCS) - Shared model across all cloud services - Supports SSO - Enables AD integration ‣ IDCS only relevant for Universal Credit
  20. Architectural Options

  21. !21 Oracle Cloud • Simplest integration architecture:

    ‣ Data held in Oracle Database Cloud Service (DBCS) ‣ ETL via Oracle Data Integration Platform Cloud (DIPC) • Data acquisition from Cloud Applications: ‣ BI Cloud Connector ‣ Goldengate ‣ OAC’s new Data Replication features • Data acquisition from on-premises applications: ‣ Data Sync ‣ APEX Data Loader ‣ SQL Developer tools ‣ Data Pump
  22. !22 Hybrid • Database remains on-premises ‣

    Frequently a staging post for full migration • Data access via Remote Data Connector ‣ Runs on dedicated application server (WLS/Tomcat) ‣ Secure connectivity via public/private SSL key ‣ Supports Oracle, DB2, Teradata, SQL Server • Beware performance challenges ‣ Potential latency due to network bandwidth ‣ Check network capacity and test carefully
  23. !23 Multi-Cloud • Commonly adopted cloud strategy

    ‣ Spread investment across different cloud environments ‣ Mix private and public cloud workloads • Data Sets have a broad support for connectivity ‣ Relational, Big Data, Semi-structured & proprietry • Data Model supports fewer options (via RDC) ‣ Oracle, DB2, Terdata, SQL Server
  24. @rittmanmead Getting Started With Your First Instance

  25. @rittmanmead !25 Two Service Options Analytics Cloud Classic

    Analytics Cloud Services managed by Oracle: Backup & Recovery Service Monitoring Patching & Upgrades Test & Production instances Based on Oracle Cloud Infrastructure (OCI) Services managed by You: Based on Oracle Cloud Infrastructure Classic
  26. @rittmanmead !26 Analytics Cloud (OAC)

  27. @rittmanmead !27 Analytics Cloud (OAC)

  28. @rittmanmead !28 Real Provisioning Example - OAC

  29. @rittmanmead !29 Real Provisioning Example - OAC

  30. @rittmanmead !30 Real Provisioning Example - OAC Choose

    Edition Choose Feature Set
  31. @rittmanmead !31 Real Provisioning Example - OAC

  32. @rittmanmead !32 Real Provisioning Example - OAC

  33. @rittmanmead !33 Real Provisioning Example - OAC

  34. @rittmanmead !34 Real Provisioning Example - OACC Provisioning

    1 - Database 2 - OACC
  35. @rittmanmead !35 Real Provisioning Example - OACC -

  36. @rittmanmead !36 Choose Region Real Provisioning Example -

    OACC - Database * Must match the Region Chosen in OACC
  37. @rittmanmead !37 Real Provisioning Example - OACC -

  38. @rittmanmead !38 Choose Backup Options Real Provisioning Example

    - OACC - Database
  39. @rittmanmead !39 Real Provisioning Example - OACC -

  40. @rittmanmead !40 Real Provisioning Example - OACC

  41. @rittmanmead !41 Real Provisioning Example - OACC

  42. @rittmanmead !42 Real Provisioning Example - OACC

  43. @rittmanmead !43 Real Provisioning Example - OACC Choose

    Region * Must match the Region Chosen in ODBCS
  44. @rittmanmead !44 Real Provisioning Example - OACC Choose

  45. @rittmanmead !45 Real Provisioning Example - OACC

  46. @rittmanmead !46 Real Provisioning Example - OACC

  47. @rittmanmead !47 Real Provisioning Example - OACC

  48. @rittmanmead !48 Real Provisioning Example - OACC

  49. @rittmanmead !49 Real Provisioning Example - OACC

  50. @rittmanmead Moving from on-premises OBIEE to OAC

  51. @rittmanmead •Cloud is “in” – management support assured

    •No need to involve internal IT and slow processes •Scale up and down as needed •Pay as you go – down to hourly basis •No capital expenses, only operating expenses! •“Fight for budget” a lot smoother Why to move to cloud
  52. @rittmanmead •OAC and OACC contain DV •OBIEE requires

    additional license for DV •“Cloud first” strategy means cloud gets new features earlier Why to move to cloud
  53. @rittmanmead •Choose whether to use RPD or thin

    client modeller ‣Less dependency on skilled staff ‣Free up skilled staff for core tasks ‣Leave common tasks to super users ‣RPD and Admin Tool still there ‣Leverage existing know-how! Why to move to cloud
  54. @rittmanmead Thin client modeller and Admin Tool

  55. @rittmanmead Thin client modeller and Admin Tool

  56. @rittmanmead Your ETL Toolset

  57. @rittmanmead Your ETL toolset or Data Flows

  58. @rittmanmead !58 • Migrating to OAC is relatively

    straight forwards ‣ The method depends on your starting point… Migration Options From BICS Create BAR File * Upload Based on Oracle Cloud Infrastructure Classic 11g Run the Migration Tool Deploy Import Bundle to OAC Minimum OBIEE Does not apply to Autonomous version Manual Identity Store configuration 12c Create 12c BAR file Upload Snapshot to OAC or Manually upload Data Model Archive/Unarchive Web Catalog
  59. @rittmanmead !59 • DVCS to OAC/OACC ‣ Both

    BICS and DVCS not sold anymore ‣ Must be migrated at some point • OAC/OACC to on-premises ‣ On-premises won’t go away ‣ Allow for easier / cheaper on-boarding and development • OACC has full accessibility • Supports all devops and lifecycle technologies known from OBIEE Migration Options
  60. @rittmanmead •Clean and correct first! •Old 11g was

    a lot more permissive and fuzzy on certain rules •Clean on-premises means fluid migration •Fix-in-cloud often a lot harder •Review and reconsider your security •Eliminate legacy burdens •Trim unused excess •Institute standards Migration Considerations
  61. @rittmanmead INTRO 101 - OAC Overview and Features

  62. @rittmanmead DATASOURCE101 - Creating New OAC Datasources

  63. @rittmanmead !63 •OAC can access data sources from

    the cloud or on premise (depending on licensing) •Some examples of data sources are: ‣Oracle Database ‣Oracle Essbase ‣Hive ‣Impala ‣MySQL ‣SQL Server ‣Amazon Redshift ‣ODBC ‣and many more! Oracle Analytics Cloud Data Sources
  64. @rittmanmead !64 •New Data Sources (Connections) are added

    through the Oracle Analytics Cloud Dashboard •Uses a Wizard to create the data sources, and then are available for use by anyone who has access Oracle Analytics Cloud Data Sources
  65. @rittmanmead !65 •Once you have created a data

    source, you can create a Data Set. Which can be: ‣A Database Object (Table, View, etc) ‣File (Excel, CSV, etc) ‣Essbase Application ‣SQL Statements •Data Sets can be filtered to remove data that is not required •Data Sets are managed within Visual Analyzer Oracle Analytics Cloud Data Sets
  66. @rittmanmead !66 •Once you have created a data

    set, this can be used inside Visual Analyzer to create a project and visualisations based on that data Oracle Analytics Cloud Data Sets
  67. @rittmanmead !67 •Oracle BICS Remote Data Connector ‣Oracle

    Business Intelligence Cloud Service Remote Data Connector (BICS RDC) enables querying data residing in on-premises relational sources without moving data to cloud. ‣Requires a Weblogic/Tomcat Server to host application •Oracle BICS Data Sync ‣Oracle Business Intelligence Cloud Service Data Sync supports loading data from files, on premise and cloud sources into schema provisioned on Oracle Business Intelligence Cloud Service. ‣Java based service that is installed on a server. Oracle Analytics Cloud Data Sets
  68. @rittmanmead !68 •In Oracle Analytics Cloud Dashboard ‣Navigate

    to the data tab ‣Click the ‘Create’ Button ‣Choose ‘Connection’ Example Connection & Dataset: Step 1
  69. @rittmanmead !69 •On the ‘Create Connection’ dialogue ‣Select

    the data source type required (e.g. Oracle Database) Example Connection & Dataset: Step 2
  70. @rittmanmead !70 •On the ‘Create Connection’ form ‣Enter

    the details of the Connection ‣This will vary depending on the type ‣Click Save Example Connection & Dataset: Step 3
  71. @rittmanmead !71 •This will then be displayed on

    the Data tab under Connections Example Connection & Dataset: Step 4
  72. @rittmanmead !72 •Next we need to create our

    Data Set •This is done from the data tab, using the same ‘Create’ menu we used before Example Connection & Dataset: Step 5
  73. @rittmanmead !73 •Since we have used an Oracle

    connection, we get a list of available schemas. •Double-click on the schema to continue Example Connection & Dataset: Step 6
  74. @rittmanmead !74 •We then get a list of

    database objects we can choose and search through •We can also add individual columns •Data Sets are saved as objects in the OAC Dashboard Example Connection & Dataset: Step 7
  75. @rittmanmead !75 •Repeat this for any other data

    sets that may be required •We can also add filters to data sets Example Connection & Dataset: Step 8
  76. @rittmanmead !76 •Once this has been completed we

    can see the two new data sets in the Oracle Analytics Cloud Desktop under Data \ Data Sets Example Connection & Dataset: Step 9
  77. @rittmanmead !77 •These data sets can then be

    used inside Visual Analyser projects ‣more on this later! Example Connection & Dataset: Step 10
  78. @rittmanmead DATASOURCE101 - Creating New OAC Datasources

  79. @rittmanmead !79 •OAC URL: •User: training •Pwd:

    Password01!! •Labs: Environment
  80. @rittmanmead DATASOURCE101 Lab 1 - Creating a new

    Connection and Data Set
  81. @rittmanmead DATAVIZ 101 - OAC Data Visualization

  82. @rittmanmead !82 Exploration: • Scattered data • Uncertainty

    • Hypothesis testing • Why did this happen • Detailed analysis • Learning from data • Analysis follows data Oracle Analytics Cloud Data Visualization Explanation: • Consolidated data • Certainty • Hypothesis confirmation • What happened • High level metrics • Teaching with data • Data guides analysis
  83. @rittmanmead !83 Data Visualization (Explore): • Scattered data

    (Data mashups) • Uncertainty (Quick to build new charts, save analysis paths) • Hypothesis testing (Individual uses to test a theory) • Why did this happen (Look for patterns behind the trends) • Detailed analysis (Track multiple analysis paths) • Learning from data (fast workflow promotes creativity) • Analysis follows data (Try many things, save false starts, unlimited filter combos) Oracle Analytics Cloud Data Visualization Answers (Explain): • Consolidated data (RPD) • Certainty (Format dashboards to convey known information) • Hypothesis confirmation (Company gets the single source of truth) • What happened (Show tiles of KPIs) • High level metrics (Present the most relevant information quickly) • Teaching with data (Slower workflow promotes best practice presentation) • Data guides analysis (Dashboard creator guides users with selective use of drill down and filtering)
  84. @rittmanmead !84 • Everything placed on the canvas

    is automatically connected • Filtering ‣Global filters affect all charts • Brushing ‣Highlighting data points in one visualization highlights them in all visualizations • Drilling ‣Drilling on one visualization automatically filters the others Oracle Analytics Cloud Data Visualization
  85. @rittmanmead !85 • The subject area view is

    similar to Answers Oracle Analytics Cloud Data Visualization
  86. @rittmanmead !86 • The data exploration bar contains

    options for chart creation based on the grammar of graphics • A grammar of graphics is a tool that enables users to concisely describe the components of a graphic Oracle Analytics Cloud Data Visualization
  87. @rittmanmead !87 •The canvas displays the chart •Filters

    can be added by dropping columns into the filter view Oracle Analytics Cloud Data Visualization
  88. @rittmanmead !88 • Drag and drop a measure

    and an attribute from the subject area onto the canvas Note: Hold the ctrl key to select both at the same time • VA will automatically generate a chart for your single measure • Time attributes automatically generate line charts Oracle Analytics Cloud Data Visualization
  89. @rittmanmead !89 • Select a chart and drag

    additional measure or attributes to the data exploration bar to modify the chart’s properties • Add filters by dropping columns into the filter bar • Modify the filter to limit values Oracle Analytics Cloud Data Visualization
  90. @rittmanmead !90 • Visualizations can be dragged and

    dropped to create different layouts • Each visualization will automatically adjust its size to fill its new placement Oracle Analytics Cloud Data Visualization
  91. @rittmanmead !91 • On the menu header go

    to save/save as and give a name to your project Oracle Analytics Cloud Data Visualization
  92. @rittmanmead DATAVIZ 101 - OAC Data Visualization

  93. @rittmanmead DATAVIZ101 Lab 1 - Creating a VA

  94. @rittmanmead MASHUPS101 - Using External Data & Data

  95. @rittmanmead !95 •Adding your own data is sometimes

    referred to as “mash-up” •Data mashups provide the ability to introduce out of band or personal data into a governed data environment. •The expectation is to continue to maintain the self-service model •The goal is to enable the behaviors with a minimum amount of intervention. •Add your own data to analyze on its own. •Add your own data as an extension to an existing subject area. Concepts
  96. @rittmanmead !96 •Create new VA project and select

    an existing or new data set and add it to the project Import External Data Source
  97. @rittmanmead !97 • data relationships Import External Data

  98. @rittmanmead !98 •Columns from external subject areas can

    be added and manipulated as before •Select measures and attributes from both subject areas then drag and drop to the visualisation and data element area: Combine External and Enterprise Data
  99. @rittmanmead !99 •To refresh data you must ensure

    that the newer spreadsheet file contains a sheet with the same name as the original one •The sheet must contain the same columns that are already matched with the subject area. •In the Data Sources pane, or the Subject Areas pane, right-click the data that you want to refresh. •Reload Data Data Refresh
  100. @rittmanmead !100 •To remove a datasource from a

    project right-click the data that you want to remove. •Select remove from Project •To permanently delete a data source right-click the data that you want to remove. •Select delete to erase the data from storage. Remove and Delete Data
  101. @rittmanmead !101 •Data flows are used to create

    ‘curated’ Data Sets ‣ Manipulate data sets to add additional columns ‣ Join Data Sets together ‣ Filter Data Sets ‣ Sentiment Analysis ‣ Machine Learning Training ‣ Create Essbase Cubes Data Flows
  102. @rittmanmead !102 • In Oracle Analytics Cloud, go

    to the Data Tab • Select Create -> Data Flow: Creating a New Data Flow: Step 1
  103. @rittmanmead !103 • A new tab will open

    and ask you to choose a starting data set (or to create a new one): Creating a New Data Flow: Step 2
  104. @rittmanmead !104 • This will take you to

    the Data Flow editor where we can start manipulating our data Creating a New Data Flow: Step 3
  105. @rittmanmead !105 • Click the + icon next

    to the data set you imported at the beginning to add a new step to your data flow ‣This will raise a context menu for the different types of steps you can use: Creating a New Data Flow: Step 4
  106. @rittmanmead !106 • Each step type has its

    own options that can be configured: Creating a New Data Flow: Step 5
  107. @rittmanmead !107 • You then need to set

    an output point to your data set ‣Data can be stored either in the cloud data storage or back to a database that you have created a connection too: Creating a New Data Flow: Step 6
  108. @rittmanmead !108 • You can then schedule the

    Data Flow to run: Creating a New Data Flow: Step 7
  109. @rittmanmead MASHUPS101 - Using External Data & Data

  110. @rittmanmead MASHUPS101 - Lab 1 - Using External

  111. @rittmanmead AA01 - Advanced Analytics

  112. @rittmanmead !112 •Description of Metrics and Attributes Augmented

  113. @rittmanmead !113 Key Drivers/Anomalies

  114. What Problem are we Trying to Solve? Supervised Unsupervised “I

    want to predict the value of Y, here are some examples” “Here is a dataset, make sense out of it!” Classification Regression Clustering
  115. @rittmanmead !115 •Data Visualization provides one click access

    to ‣Trending ‣Clustering ‣Outlier Detection ‣Forecasting BI Analyst Experience
  116. @rittmanmead !116 DataFlow Train Model

  117. @rittmanmead !117 More Detailed ML

  118. @rittmanmead !118 Which Model - Parameters To Pick?

  119. @rittmanmead !119 Select, Try, Save, Change, Try, Save

  120. @rittmanmead !120 Model Evaluation

  121. @rittmanmead !121 Model Evaluation 502/(502+896) = 64.09% 471/(471+866)=64.77%

  122. @rittmanmead !122 Use On the Fly

  123. @rittmanmead !123 Step of a Data Flow

  124. @rittmanmead AA01 - Advanced Analytics