Explorative Datenanalyse im Data Lab

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. |

Explorative Datenanalyse im Data Lab Team-Sport oder nur etwas für Nerds?

Harald Erb Oracle Business Analytics & Big Data

DOAG 2016 Konferenz, Nürnberg


• Harald Erb • Principal Sales Consultant

• Information Architect

• Kontakt

+49 (0)6103 397-403

• [email protected]

Kontakt

DOAG 2016 Konferenz, Nürnberg 2

http://www.linkedin.com/in/haralderb


Characteristics of Digital Business Leaders


They ‘Reframe’ Challenges Looking at them from new perspectives and multiple angles

They Sprint They work at pace - researching, testing and evaluating current ideas while generating new ones

They Appreciate That

Failure Can Be Good

and are not afraid of new ideas

They Convert Data Into Value

They invest heavily in analyzing their own data and data from external sources to establish patterns and un-noticed opportunities


Synergizing Skills

4

Perf.

Mgmt.

Knowledge Discovery

Dynamic Dashboards and Reports

Volume and Fixed Reporting

Knowledge Driven Business Process

Executive: Decisions effecting

strategy and direction

Business Analysts: Day-to-Day performance

of a business unit

Information Consumer: Reporting on

individual transactions

Automated Process: Decisions effecting

execution of an indiv. transactions

Insight

Data Scientists: Information analysis to

meet strategic goals

BICC

Analytical Competence Center (ACC)

» Separate group reporting to CxO. not part of a Business Intelligence Competence Center (BICC)

» Mission: broadening the adoption of Analytics across the organization

» Skilled resource pool of Data Scientists, Statisticians and Business Experts

» Data-driven approach (not development-driven) with privileged access to enterprise data sources

» Group will be assigned to projects for a limited time

ACC



Enabling Data-driven Decisions

5

Ide

nti

fy (

bu

sin

ess

) q

ue

stio

n

Become clear about all aspects of the decision to be taken or the problem to be solved.

Try to identify alternatives to your percep-tion

Ve

rify

ear

lier

fin

din

gs

Find out who has investi-gated such or a similar problem in the past and the approach that has been taken

De

sign

of

a so

luti

on

mo

de

l Formulate a detailled hypothesis how specific variables might influence the result of the chosen model

Gat

he

r al

l ne

cess

ary

dat

a

An

alys

e t

he

dat

a

Pre

sen

t & im

ple

me

nt

resu

lts Gather all

available information about the variables of your hypo-thesis. The relevance of a dataset might address your business question directly or needs to be derived

Apply a statistical model and evaluate the correctness of the approach. Repeat this procedure until the right method has been identified.

Frame the results obtained in a compre-hensible story. This kind of presentation intends to motivate decision makers and relevant stake-holders to take action

Non-Analysts & Executives: should take a closer look on steps 1 and 6 of the analysis process if they plan to make use of statistical analysis.


Knowledge Discovery Adopted from Thomas H. Davenport, Harvard Business Manager 2013


Data Projects: Process

6 DOAG 2016 Konferenz, Nürnberg

Ad

op

ted

fro

m H

uge

nb

erg

20

11

-S.1

68

Week

Task

Create Work Plan

Hypothesis

Business question

Analysis

Source

Create Analysis Plan

Structure Problem

What? How?

Hypothesis

Yes

No ?

Why?

Define Problem

Fundamental business question to be solved:

Problem area: Root of

problem:

Decision maker:

Decision criteria:

Boundaries of problem handling:

Solution limitations:

•Necessary information?

•Available Information? Which quality?

•Data owner?

•Available data sets?

•Business problem?

•What is at issue?

•What needs to be analyzed?

•Precise goal definition

•Deliminations

•Useful data / structure?

•Hypothesis definition

•Verify correlations

•Descriptive analysis

•Data preparation

•Select


Data Lab: Key Requirements

Based on Raw Data

Full Access to Data Sources

(Select only)

Complete Sandbox

Environment

Agile Experimentation,

Fail Fast



Data Lab Scenario

Sandbox Data Management



Stages of Data Transformation

Refinement of Raw Data


Signal Data Information Knowledge Wisdom

L0 - Ingestion L1 - Cleansed L2 - Normalised

Accounts

Parties

Account

Parties

Party

Addresses

Party

ContactsParty IDs

Party

EventsParty

Ratings

Account

Limits

Party

History

Collaterals

Account

Collaterals

Party

Collaterals

Account

Balances

Account

Relations

L3 – Presented

Customer

Dimension

Account

Dimension

Currency

Dimension

Product

Dimension

Organization

Dimension

Calendar

Dimension

Account Daily

Facts

Account

Transactions

Transaction

Types

Channel

Dimension

CoA

Dimension

Company

Dimension

•Format/Domain checks

•Completeness checks

•Duplicates detection

•Not null validations

•Enrichment

•Record level cleansing and business rules

•Referential integrity

•Context based business rules and quality checks

•Aggregate level checks

•Derived and enriched data for Self-service Business Intelligence

•File validation

•Row completeness

•Raw Data Stores for Data Science

Know nothing Know what Know how Know why

Data Warehouse

Data Lake

Source Systems

Addressing a key requirement for Data Labs

9


Data Management: Architecture (Logical View)


Line of Governance

Data Lake

Data Processing

Data Enrichment Raw Data

Sets

Curated & Transformed

Data Sets Data

Aggregation

Data Lab

Sandboxes

Data Catalog Data Discovery

Transformations Prototyping

Analytic Tools

Enterprise Information

Store

Operational Data Store

Data Federation & Virtualization Layer

Co

mm

on

SQ

L A

cces

s to

A

LL D

ata

Orchestration, Scheduling & Monitoring

Metadata Management

Data Ingestion

Batch Integration

Real-Time Integration

Data Streaming

Data Wrangling

Reporting / Business

Intelligence

Data Driven Applications

Advanced Analytics

Non-structured Sources

Logs

Social Media

External Data

Interactions

Structured Data

Master Data

Applications

Channels

Data Stores

Adhoc Files or Relational

Data Sets


Data Management: Oracle Platform



Logs

Social Media

External Data

Interactions

Structured Data

Master Data

Applications

Channels

Data Stores

Oracle Software

Cloudera CDH 5.7+ / Apache Software

Oracle Platform

Oracle Exadata Oracle Big Data Appliance

Oracle Exalytics

Oracle x86 Servers


Metadata Management


Intelligence


Advanced Analytics


Data Management: Functional Areas



Logs

Social Media

External Data

Interactions

Structured Data

Master Data

Applications

Channels

Data Stores

Oracle Software


Oracle Platform


Metadata Management


Data Ingestion

Data Store Data Discovery & Analyze

Unified Data Services

Process

Online

Lifecycle/Governance

Data Warehouse


Intelligence


Advanced Analytics


Data Management: Data Discovery & Analytics



Intelligence


Advanced Analytics


Logs

Social Media

External Data

Interactions

Structured Data

Master Data

Applications

Channels

Data Stores

Oracle Software


Oracle Platform


Oracle GoldenGate for Big Data

Flume & Kafka

Oracle Data Integrator

Oracle Stream

Analytics

Scoop

Oracle NoSQL DB

Kudu (Relational)

Filesystem (HDFS)

HBase (NoSQL)

Oracle Data Integrator Batch

(Map Reduce, Hive, Pig, Spark)

Stream (Spark)

HBase (NoSQL)

WebHDFS, Fluentd,

Storm, Tika ....

Oracle Database

Oracle SQL

Database Security (Roles, View, VPD, …)

Oracle Advanced Analytics

Oracle Advanced Security

In-Memory

Data Ingestion

Data Store

Process

Online

Data Warehouse Data Discovery & Analyze

Secu

rity

(S

entr

y +

Rec

ord

Ser

vice

)

Re

sou

rce

Man

agem

ent

(Y

arn

)

Unified Data Services

Search (Solr)

SQL (Impala)

Model (Spark ML)

Big Data Spatial & Graph

Adv. Analytics for Hadoop

Big Data Discovery

Oracle Big Data SQL

Ora

cle

Big

Dat

a C

on

nec

tors

Cloudera Navigator

Lifecycle/Governance

Oracle Enterprise Metadata Management (OEMM)

Oracle Data Factory Engine | Oracle Data Integrator | Oracle Enterprise Data Quality


Data Lab Scenario

Exploratory Analysis Oracle Big Data Discovery



Activities

Data Lab


Download from: http://www.the-modeling-agency.com/crisp-dm.pdf

Generic tasks (bold) and outputs (italic)

CRISP-DM reference model

http://www.the-modeling-agency.com/crisp-dm.pdf








Oracle Big Data Discovery

16

Know nothing Know what Know how Know why

Signal Data Information Knowledge Wisdom

(Operational) Business Intelligence (100…1000+ User)

Data Warehouse Data Lake

L0 - Ingestion L1 - Cleansed L2 - Normalised

Accounts

Parties

Account

Parties

Party

Addresses

Party

ContactsParty IDs

Party

EventsParty

Ratings

Account

Limits

Party

History

Collaterals

Account

Collaterals

Party

Collaterals

Account

Balances

Account

Relations

L3 – Presented

Customer

Dimension

Account

Dimension

Currency

Dimension

Product

Dimension

Organization

Dimension

Calendar

Dimension

Account Daily

Facts

Account

Transactions

Transaction

Types

Channel

Dimension

CoA

Dimension

Company

Dimension

Source Systems

Oracle BI Plattform

Common Enterprise

Information Model

Oracle BI Data Visualization

Oracle BI Dashboards, (Ad-hoc) Reports, …

Data Projects (1…20+ User)

Oracle Data Visualization Desktop




Team Sport: One tool for Business Analysts and Data Scientists


17

DWH / OLTP

Databases

Database Administrator

(Enterprise IT)

Hadoop

Data Integration Specialist

(Enterprise IT)

Data Engineer

Data Science

Discovery Output

Business Analyst

New KPI, Report Requirement

Data Scientist

New Data Set (cleaned / enriched)

Members of the same

Data Project



Analysis Scenario 1: Prototype Testing


Analysis Scenario 1: Prototype Testing

1. A flexible environment to exploit all available data for prototype testing discovery 2. Can driver comments really add value to our prototype testing discovery? 3. What is the relationship between errors?

Oracle Confidential – Internal/Restricted/Highly Restricted

Telemetry

3

1

2 Errors

Driver Comments

Analysis & Dashboarding

Discovery Lab

1.2 Billion rows at 100Hz

Data Platform

Factory Storage

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | DOAG 2016 Konferenz, Nürnberg 20

Analysis Scenario 2: Investigate Car Complaints

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | DOAG 2016 Konferenz, Nürnberg 21

M I S S I O N

Analysis Scenario 2: Investigate Car Complaints

Help the Quality Team to trace back warranty claims and support issues to reduce warranty cost and minimize supplier risk,

in order to improve product quality and customer satisfaction.


Available Data


hadoop fs -cat /user/oracle/warranty/claims_full.txt | less

Internal Data (Warranty Claims)

Additional Data (i.e. demographics)


Data Processing Workflow

Oracle Big Data Discovery mit Daten versorgen

23

File Upload BDD Studio

Big Data Discovery Data Proc. Client

New Data Set in BDD Data Catalog

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 24

1

2

5

4 3

Data Loading – Data Ingest Overview

1. Data ingest is triggered by data upload, the command line interface, or the Hive Table detector

2. Records are read and sampled into Spark

3. Data profiling occurs, to determine schema, search configuration and which enrichment apply

4. Auto enrichments are performed

5. Data is ingested into Big Data Discovery


About the Sampling technique


• BDD leverages a Simple Random Sampling algorithm – Each individual is chosen randomly and entirely by chance with the

same probability of being chosen at any stage during the sampling process. Each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals

• Sampling is dependable – Accuracy is about the size of the sample, not the size of the source

• A 1M random sample provides more than 99% confidence that the answer is within 0.2 % of the value shown, no matter how big the source dataset is (1B/1T/1Q+).

• Sampling makes interactivity cheap – Will you pay 10, 100, 1000x the cost to get the last <<1% of the

confidence? • Maybe sometimes, but not in discovery and not for every dataset


Data Profiling & Enrichments


• Profiling is a process that determines the characteristics (columns) in the Hive tables, for each source Hive table discovered by Big Data Discovery during data processing.

– Attribute type determination (discovery) • Includes strings to dates, geocodes, long or boolean

– Attribute value distributions

– Determines attribute searchability

– Provides “hints” to Studio as to what content (components) should be on the default Project page

• Enrichments are derived from a data set's additional information such as terms, locations, the language used, sentiment, and views. Big Data Discovery determines which enrichments are useful for each discovered data set, and automatically runs them on samples of the data. As a result of automatically applied enrichments, additional derived metadata (columns) are added to the data set, such as geographic data, a suggestion of the detected language, or positive or negative sentiment.


Find

27

• A rich, interactive catalog of all data in Hadoop

• Familiar search and guided navigation for ease of use

• Data set summaries, user annotation and recommendations

• Personal and enterprise data upload to Hadoop via self-service



Find

28

• A rich, interactive catalog of all data in Hadoop

• Familiar search and guided navigation for ease of use

• Data set summaries, user annotation and recommendations

• Personal and enterprise data upload to Hadoop via self-service



Like shopping online for your data

Find – Data Sets Tab

29

Navigate • Project or Dataset – by

Author and Tags

• Contains – datetime or Geo

• Number of records or attributes

• Recently Viewed, Most Popular, Newly Added

Data Quick Look • Data Set Info

– Tags, Views, Last Updated

– Project, used, created by

• Actions

– Explore, Add to project, Edit Tags, Delete

• Related Data Sets by data source

– “Often used with these data sets”

• Preview

– First 15 rows, all columns

Search • Keyword

• Data Sets

• Projects

• Data Set Metadata

• Project Metadata

• Recently Viewed, Most Popular, Newly Added



Find (Catalog) – Data Set Quick Look


• Data Set Info – Tag

• Actions – Explore

– Add to project

– Edit Tags

– Delete

• Summary – Views

– Last Updated Data Set Info

Quick Look


Find (Catalog) – Data Set Quick Look


• Used in Projects

– Project name

– Data Sets used

– Created by

Data Set

Used in Project

Quick Look


Find (Catalog) – Navigation & Search


• Searches

– Keyword

– Data Sets

– Projects

– Data Set Metadata

– Project Metadata

– Attribute Metadata

Search Everything


Like shopping online for your data

Find – Projects Tab

33

Projects Tab

• Search and navigate

• Project Categories

–Recently Viewed

–Most Popular

–View all

Projects Quick Look

• Add Tags, attributes, data sets, pages

• Open Project, Edit Tags, Delete

• Summary



Find (Catalog) - Projects Tab


• Searchable

• Navigable

• Project Categories

– Recently Viewed

–Most Popular

– View all

Search Everything

Discover Analytical

Projects

Navigate by Metadata


Find (Catalog) - Project Quick Look

• Add Tags

• Attributes

• Data Sets

• Pages

• Actions

–Open Project

– Edit Tags

– Delete

• Summary

35

Project

Quick Look



Explore

36

• Visualize all attributes by type

• Sort attributes by information potential

• Assess attribute statistics, data quality and outliers

• Use scratch pad to uncover correlations between attributes



Quicklook by Geocode

• Overview

• Details

• Summary stats

• Refineable

Explore

Scratchpad

• Graphic type changes as additional attributes are added

• Autoselects best visualization

• Offers next best graphics option(s)

Intuitive, machine-guided data exploration

Search, Navigate, Sort

• Attributes, refinements, keyword

• Sort by:

– Name (alpha)

– Information Potential

– Relationship to an attribute



Explore

• Search: – Attributes

– Refinements

– Keyword

• Sort order – Name

– Information Potential

– Relationship to an attribute

• Navigable

38

Tiles vs. Tabular view

Navigation menu

Search menu

Sort Order



Explore – Sort Attributes

• Sort order

– Name (alpha)

– Information Potential • Based on Entropy

– Relationship to an attribute • Based on Information Gain

39

Sort by



Core Capabilities: Explore – Quick Look –Geocode

• Overview

• Details

• Summary stats

– Refineable



Explore – Scratchpad

• Graphic type changes as additional attributes are added

• Autoselects best visualization

• Offers next best graphics option(s)



Document all relevant information and insights about the original data sources

Working with Metadata


Available Code Books, Data Set specifications

Document all relevant information, insights directly

in BDD Studio...

... and make immediately use of it, i.e. via text search in BDD


• Intuitive, user driven data wrangling

• Data Shaping

• Extensive library of powerful data transformations and enrichments

• Preview results, undo, commit and replay transforms

• Test on sample data then apply to full data set in Hadoop

Transform - Overview



Transform – Function Families Available via a User Guided Interface



Full Guided Navigation

Attribute Transformation

Smart Attribute Filtering

Interactive Transform

History

Visual Data

Quality Summaries

45

Data Shaping TAB

Aggregation, Join, etc.




• Uses Groovy - an object-oriented programming language for the Java platform. – Code written in the Java language is valid in Groovy

– It is flexible and easy to use.

• Features of the Editor include:

46

– Syntax highlighting enables color-coding of different elements in your transformation to indicate their type.

– Auto complete lets you view a list of autocomplete suggestions for the word you're typing, by pressing Ctrl+space.

– Error checking includes a built-in static parser that performs error checking when you preview or save your transformation.

Transform Editor Available via a User Guided Interface



Enrichments

• Infer language

• Detect sentiment

• Identify key phrases, entities, noun groups

• Whitelist tagger

• Address and IP geotagger

Function Families

• String

• Mathematical

• Conversion

• Conditional

• Datetime

• Data cleansing

• Geotagging

User-driven data wrangling

Commit, Create Data Set

• Commit

– Execute transformations on sample data available in the app – this updates the original collection in the Dgraph.

• Create a Data Set

– Executes transformation on the entire data set in HDFS, creating a new entry in the BDD Catalog & Hive.

• Rollback

– Attributes created by a transformation, then deleted, will be removed on Commit.

47

Data Shaping

• Aggregate

• Persistent Join

• Row Filter




• Geo Tagging

– Partial Address match

– Address disambiguation (based on population)

– IPv4 support

• Numeric Transformation

– Round, Ceiling, Floor, Absolute Value

– Operators (+ - / *), sqrt, mod, log, ln, min/max, sin/asin/sinh, cos/acos/cosh, tan/atan/tanh

• String

– Split, Trim, Uppercase, Lowercase, Titlecase, Concatenate

• Type Conversion

– Boolean, Datetime, Double, Integer, String, Long, Time, Geocode

• Datetime (accessed via Transformation Editor)

– Date Diff, Date Add, getYear, Month, Day, Hour, Minute

– Truncate Date

• Data Cleansing

– Binning numerics

• Conditional

– Accessed via Transformation Editor

• If /else if statement

– Optionally based on refinement state

– Can be nested

• Drag/drop or double-click functions and attributes onto editor

48

Transform – Function Families Transformation Functions



• In addition to transforming data, Big Data Discovery allows for users to enrich data sets in numerous ways:

• Term Extraction – Extract relevant phrases and noun groups from unstructured text

• Entity Recognition – Find people, places and organizations mentioned in unstructured text

• Sentiment Analysis – Determine the document and sub-document level sentiment of unstructured text

• Geo Tagging – Generate standardized geographic information based on an unstructured address or IP address

• Language Detection

• Applied via User Guided Interface

49

Transform – Function Families Enrichment Functions



Windows Metro

Driver Issues

Usb Port

Light Gaming

Gaming Laptop

Key Phrases

Document Sentiment

Detected Language

• I was looking for a laptop mainly to browse and for multimedia purpose and found this inexpensive built-like-a-tank Dell XPS 14. I am very impressed with the build quality, stunning looks and there is absolutely a very nice feel to it. I have many MBPs at home and this one is better in appearance and worked out of the box with minimal set up. I was up and running in 10 minutes or so.

• I found the touch pad not up to par with Apple track pad but with the latest driver update it came pretty close. I uninstalled the McAfee software and went with Norton security as it comes free with Comcast. There is a new BIOS update from February 2014 and also audio driver update that you may want to apply immediately. Do the Wifi-BT update as well. en

POSITIVE

50

Transform – Function Families Text Enrichments



Extract Key Phrases from Freetext

Text Enrichment



Determine sentiment and extract related phrases

Text Enrichment



• Partial Address match

• Address disambiguation (based on population)

• IPv4 support

53

Input: west loop chicago Input: 148.87.19.206 city Chicago

country US

county Cook County

geocode 41.85003 -87.65005

latitude 41.85003

longitude -87.65005

population 2695598

state Illinois

city Redwood City

country US

geocode 37.4852 -122.2364

latitude 37.4852

longitude -122.2364

state California

Transform – Function Families Geo-Tagging Enrichments



One goal of all the ETL work: Linking Data Sets via Joins

Combining Data Sets



• Join and blend data for deeper perspectives

• Compose project pages via drag and drop

• Use powerful search and guided navigation to ask questions

• See new patterns in rich, interactive data visualizations

Discover



Discovery Dashboards

• Control over layout, filtering, exposed dimensions/metrics

• Formatting controls – Is Dimension, available aggregations, multi-

OR/And, Include in Available Refinements

– Component level: currency, # of decimals, date format, etc.

• Data set linking: 2 or more data sets within a project, visual attribute linking

• Automatic view creation and widening of data sets

Discover Drag-and-drop dashboards for fast , easy analysis

Navigation

• Intelligent refine using any combination of data elements

• Color highlighting

• Alternate counts for metrics

• Linked navigation across data sets

Visualizations

• New visualizations

– D3 Chart library

– More intuitive interface

– Seamless integration of new visualization components (Custom Visualization Component extension)



Rich and highly interactive Library of Visualization Portlets




Data Lab Scenario

Advanced Analytics BDD Shell

Jupyter Notebook



Data Scientist continues with Machine Learning tasks


59

DWH / OLTP

Databases

Database Administrator

(Enterprise IT)

Hadoop

Data Integration Specialist

(Enterprise IT)

Data Engineer

Data Science

Discovery Output

Business Analyst

New KPI, Report Requirement

Data Scientist

New Data Set (cleaned / enriched)



Handling of sparse Data / NULL values

Shaping a Data Set for further processing



Aggregation



• Roll up low-level data to higher grains

– Production Year

– Vehicle Model Year

– Vehicle Make

• Intuitive UI helps analysts find the right grains

• Execute at full scale using Spark

• Results can be sampled or indexed in full


Combining multiple Data Sets



• Blend huge datasets in BDD

– UI to support experimentation, preview

– Execute at scale with Spark

• Results can be sampled or indexed in full


Export new Data Set Hive Table in Hadoop




Data Density, Validation of attribute correlations

Analytic features in Oracle Big Data Discovery


Scatter Plot Scatter Plot Matrix


• Python-based shell

• Exposes all BDD data objects

• Easy-to-use Python Wrappers for BDD APIs and Python Utilities

• Use of Third-party Libraries, e.g., Pandas and NumPy

BDD-Shell interface Point of Contact with Data Scientists

• BDD Shell is an interactive

tool designed to work with BDD without using Studio's front-end

• Provides a way to explore and manipulate the internals of BDD and interact with Hadoop



(Re-)use data from Oracle Big Data Discovery while working with the BDD Shell

Data Analysis with Python


List of Oracle Big Data Discovery Data Sets

Import Spark Machine Learning library MLlib

Converting a Oracle Big Data Discovery Data Set into an Apache Spark Dataframe

Import Package NumPy (Numerical Python)


• Easiest way to use the BDD-Shell

– Visual appeal, ease of use, collaboration features of an integrated platform

– Power and flexibility of custom code

– Pick up BDD’s datasets and leverage Machine Learning algorithms to infer new insight

68

Leveraging Notebooks for a better user experience Point of Contact with Data Scientists

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 69 DOAG 2016 Konferenz, Nürnberg

www.jupyter.org


(Re-)use data from Oracle Big Data Discovery while working with Jupyter

Data Analysis with Python



Explorative Datenanalyse im Data Lab...

...besser/nachhaltiger im interdisziplinären Team!



Unterstützt agile Data Experimente / Data Projekte

Oracle’s Unified Big Data Management & Analytics Strategie


Experiment

• Big Data Discovery

• R on Hadoop

• Spatial and Graph for Hadoop

In der Cloud und On-premises

Aggregate

• Big Data Preparation

• Data Integrator

• GoldenGate

• IoT

connect people to the information they need

Manage

• Hadoop Platform

• Big Data SQL

• NoSQL Database

• Oracle Database

collect, secure and make data available

innovation through experimentation with data

Analyze & act

• Data Visualization

• Business Intelligence

• Spatial and Graph

• Advanced Analytics

transform the workplace with actionable insights

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. | 73 DOAG 2016 Konferenz, Nürnberg

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