SlideShare a Scribd company logo
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |
Big Data Discovery
Relevante Informationen im Daten-Meer identifizieren
und nutzbar machen
Harald Erb
Oracle Business Analytics
OPITZ CONSULTING inspire|IT - Konferenz
Frankfurt/Main, 11. Mai 2015
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
» Harald Erb
» Principal Sales Consultant
» Business Analytics Architecture
Domain Lead - DE/CH Cluster
» Kontakt
+49 (0)6103 397-403
» harald.erb@oracle.com
Referent
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and should not be relied upon
in making purchasing decisions. The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and should not be relied upon
in making purchasing decisions. The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.
4
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 5
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 6
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
Characteristics of Digital Business Leaders
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Data-driven Decisions
7
Identify(business)question
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
Verifyearlierfindings
Find out who
has investi-
gated such or
a similar
problem in the
past and the
approach that
has been taken
Designofasolutionmodel
Formulate a
detailled
hypothesis
how specific
variables
might
influence the
result of the
chosen model
Gatherallnecessarydata
Analysethedata
Present&implementresults
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.
Data Science + Knowledge Discovery
Adopted from Thomas H. Davenport, Harvard Business Manager 2013
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 8
Vertical and Horizontal Data Science Skills
Data Warehouse HorizontalVertical
Deep technical skills
Eigenvalues,
Lasso-related regressions
Experts in Bayesian
networks, R
Support Vector Machine
Hadoop, NoSQL, Data
Modeling, DW
Cross-discipline knowledge
Machine
Learning &
Statistics
Visualization
skills
Domain
expertise
Storytelling
experts
Programming
experience
Aware of
pitfalls
& rules of
thumb
The Specialist The Unicorn
Look for the individual
Unicorn
or build a Data
Science Team?
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Enabling Data-driven Innovations in Organizations
9
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
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 10
Information Management – Conceptual View
Discovery Lab
Innovation
Discovery
Output
Events
& Data
Actionable
Events
Event Engine Data
Reservoir
Data Factory Enterprise
Information Store
Business
Intelligence
Actionable
Information
Actionable
Insights
Data
Streams
Execution
Structured
Enterprise
Data
Other
Data
Line of governance
Source: Oracle White Paper “Information Management and Big Data – A Reference Architecture”
ACC
BICC
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
» Event Engine: Components which process data in-flight to
identify actionable events and then determine next-best-
action based on decision context and event profile data and
persist in a durable storage system.
» Data Reservoir: Economical, scale-out storage and parallel
processing for data which does not have stringent
requirements for formalisation or modelling. Typically
manifested as a Hadoop cluster or staging area in a relational
database.
» Data Factory: Management and orchestration of data into and
between the Data Reservoir and Enterprise Information Store
as well as the rapid provisioning of data into the Discovery Lab
for agile discovery.
» Enterprise Information Store: Large scale formalised and
modelled business critical data store, typically manifested by
an (Enterprise) Data Warehouse. When combined with a Data
Reservoir, these form a Big Data Management System.
» Reporting: BI tools and infrastructure components for timely
and accurate reporting.
» Discovery Lab: A set of data stores, processing engines, and
analysis tools separate from the everyday processing of data
to facilitate the discovery of new knowledge of value to the
business. This includes the ability to provision new data into
the Discovery Lab from outside the architecture.
» Execution: Flow of data for execution are tasks which support
and inform daily operations
» Innovation: Flow of data for innovation are tasks which drive
new insights back to the business
» Arranging solutions on either side of this division (as shown by
the red line) helps inform system requirements for security,
governance, and timeliness.
Information Management – Conceptual View
11
Source: Oracle White Paper “Information Management and Big Data – A Reference Architecture”
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Discovery Lab: Design Pattern
» Iterative development approach –
data oriented NOT development oriented
» Small group of highly skilled individuals
(aka “Data Scientists” or a team organized
as an Analytical Competence Center, ACC)
with privileged access to enterprise data
sources
» Specific focus on identifying commercial
value for exploitation
» Wide range of tools and techniques
applied
» Typically separate infrastructure but could
also be unified Reservoir if resource
managed effectively
» Data provisioned through Data Factory or
own ETL processes
ACC
12
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 13
Discovery Lab: Activity Cycles
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Discovery Lab: Data Provisioning
14
Analysis Processing & Delivery
Discovery Lab & Development Environment
Data
Science
(Primary
Toolset)
Statistics Tools
Data & Text Mining Tools
Faceted Query Tools
Programming & Scripting
Data Modelling Tools
Query & Search Tools
Pre-Built
Intelligence
Assets
Intelligence
Analysis
Tools
Ad Hoc Query
& Analysis Tools
OLAP Tools
Forecasting &
Simulation Tools
Reporting Tools
Virtualisation&
InformationServices
Data
Factory
flow
ACC may quickly develop new reporting through mashups
from any available internal and external sources and may
used advanced analytical tools for innovative analysis
Data Quality & Profiling
Graphical rendering tools
Dashboards & Reports
Scorecards
Charts & Graphs
Sandbox – Project 3
Sandbox – Project 2
Sandbox – Project 1
Data store
Analytical
Processing
General BI
flow
1
2
The majority of BI development
activity will be from existing
sources – performed by the BICC
developing new reports to existing
or new channels
Raw Data
ACC
BICC
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Need To Get Analytic Value Fast
15
Tool Complexity
» Early Hadoop tools only for experts
» Existing BI tools not designed for Hadoop
» Emerging solutions lack broad capabilities
80% effort typically
spent on evaluating
and preparing data
Data Uncertainty
» Not familiar and overwhelming
» Potential value not obvious
» Requires significant manipulation
Overly dependent on
scarce and highly
skilled resources
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Oracle Big Data Discovery
16
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 17
Oracle Big Data Discovery: The Visual Face of Hadoop
find explore transform discover share
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Oracle Big Data Discovery: Components
18
Oracle Big Data Discovery Workloads
Hadoop Cluster
(Oracle Big Data Appliance or
Commodity Hardware with
Cloudera CDH 5.)
BDD node
data node
data node
data node
data node
name node
Data Processing, Workflow & Monitoring
• Profiling: catalog entry creation, data type &
language detection, schema configuration
• Sampling: dgraph (index) file creation
• Transforms: >100 functions
• Enrichments: location (geo), text (cleanup,
sentiment, entity, key-phrase, whitelist tagging)
Self-Service Provisioning & Data Transfer
• Personal Data: Upload CSV and XLS to HDFS
In-Memory Discovery Indexes
• DGraph: Search, Guided Navigation, Analytics
Studio
• Web UI: Find, Explore, Transform, Discover, Share
Hadoop 2.x
Filesystem
(HDFS)
Workload Mgmt
(YARN)
Metadata
(HCatalog)
Other Hadoop
Workloads
MapReduce
Spark
Hive
Pig
Oracle Big Data SQL
(Oracle Big Data Appliance only)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Oracle Big Data Discovery: Deployment Example
19
Diagram adopted from RittmannMead, Blog, 2015
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 20
Oracle Big Data Discovery: Preparation of Data Sources
Have to be created as Hive Tables and registered in the Hive Metastore
Hive Table definition for fixed-width
or delimited files
Hive Table with a standard Regex SerDe
(“Serializer-Deserializer”) to map more
complex file structures by using Regular
Expressions into regular table columns
Hive Table using a custom developed SerDe
to map nested file structures of a JSON file into
regular table columns
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Big Data Discovery
Upload of XLS und CSV files and
automatic Hive Table creation
HUE (Hadoop User Experience)
Upload of various file formats, table
creation wizzards, web-based
Hive Query Client
21
Oracle Big Data Discovery: Preparation of Data Sources
There are multiple ways to get new Data Sets loaded…
Hive Command Line
Interface is similar to the MySQL
command line
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Oracle Big Data Discovery: Preparation of Data Sources
22
…or by using your favorite Data Integration / ETL Tool
Oracle Data Integrator 12.1.3 with Advanced Big Data Option
(Supporting HDFS, Hive, HBase, Scoop, Pig, Spark)
IKM SQL to Hive-
HBase-File (SQOOP)
IKM File-Hive To Oracle
(OLH, OSCH)
File
(FS/HDFS)
IKM File To Hive
(Load Data)
Any RDBMS
Oracle DB
Any RDBMS
IKM File-Hive to SQL
(SQOOP)
IKM Hive Transform
IKM Hive Control Append
Hive
Hive
HBase
Hive
Hive HBase
LKM HBase to Hive
IKM Hive to HBase
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 23
Data Processing Workflow including Profiling and Enrichment
Oracle Big Data Discovery: Data Ingestion
1M of 100M
Diagram Source: RittmannMead Blog, 2015
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 24
Demonstration
Oracle Big Data Discovery
Oracle Big Data Discovery
Demonstration
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Catalog
25
» Access a rich,
interactive catalog of
all data in Hadoop
» Familiar search and
guided navigation for
ease of use
» See data set
summaries, user
annotation and
recommendations
» Provision personal
and enterprise data
to Hadoop via self-
service
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Explore
26
» 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
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 2727
» Intuitive, user
driven data
wrangling
» 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
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 28
Preferred method for the Business Analyst
Transform – User friendly…
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 29
(based on Groovy Programming Language / Library)
Preferred Method for IT / Data Engineer / Data Scientist …
Transform – … but flexible
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 30
» Join and blend data for
deeper perspectives
» Easy usage - 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
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 31
» Share projects,
bookmarks and
snapshots with
others
» Build galleries and
tell big data stories
» Collaborate and
iterate as a team
» Publish blended
data to HDFS for
leverage in other
tools
Share
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 32
Data Discovery & Analytics Lifecycle
Typical Effort
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 33
Data Discovery & Analytics Lifecycle
More Time left for Analysis and Interpretation of Results
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Analytics: More Data Variety available – Better Results
34
Example: Marketing Campaigns
Getting „lift“ on responders
Data Mining-based prediction results with
Response Modelling including hundreds
of input variables like:
Naïve Guess or
Random
1000 Population Size (% of Total Cases)
%ofPositiveResponders
Model with 20 variables
Model with 75 variables
Model with 250 variables
» Demographic data
» Purchase POS
transactional data
» Polystructured data,
text & comments
» Spatial location data
» Long term vs. recent
historical behaviour
» Web visits
» Sensor data
» …
100
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Oracle R Enterprise (ORE)
» Allows distributed processing of huge data volumes
» Benefits from DB features, e.g. Security and SQL access
» R Studio = GUI for Data Analysts
35
Oracle Data Mining (ODM)
» Implemented in the Oracle Database kernel
» Direct access via PL/SQL API and SQL operators
» Oracle Data Miner GUI embedded in SQL Developer
Oracle Advanced Analytics
Native SQL Data Mining/Analytic Functions + High-performance R Integration
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 36
Monetizing New Insights
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Discovery Lab: End of Research Phase or Project
Oracle
Advanced Analytics
Oracle Big
Data Discovery
Apply statistical & predictive models
No Data Movement; Bring algorithms to
the data
Utilize Oracle R and Data Mining for
Massive Computing Scalability on Hadoop
or Oracle
Integrated with SQL and BI tools
Find data for analytics & data science
projects
Explore the shape and quality of the data
Transform data for analytics
Discover and visualize insights in data sets
Share insights with analysts and
downstream systems
Share
Insight
Interpret & Evaluate
Select, Prepare & Transform
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Documention of Analyis Steps Providing a clear view on
probabilities
38
Storytelling / Infographics
Discovery Lab: Explanation & Validation of the Results
Individuals of the Analytical Competence Center need to frame the results
obtained in a comprehensible story. This kind of presentation intends to
motivate decision makers and relevant stake-holders to take action
Result of 1000 simulations of a $100 million investment in a
new factory: Estimation expects an annual return of 20%
over a 10-year lifespan, but the risk to loose invested money
is still 8%Big Data Discovery – Gallery feature documents all discovery
steps taken to achieve new insights
Example of individually created Infographics explaining
key findings of new insights
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 39
Discovery and monetising steps have different requirements
Making Sense from Diverse Data…
Research & Development
» Unbounded discovery
» Self-Service sandbox
» Wide toolset
» Agile methods
Promotion to Data-driven Services
» Commercial exploitation
» Narrower toolset
» Integration to operations
» Non-functional requirements
» Code standardisation &
governance
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Productize,
Secure &
Govern
Experiment,
Prototype &
Collaborate
Data Reservoir
Polystructured
Data
Data Warehouse
Oracle 12c Database
StructuredData
Oracle Big
Data Discovery
Oracle
Big Data SQL
Hadoop (HDFS)
Oracle R for
Hadoop
Oracle Advanced Analytics
(Data Mining, Oracle R Enterprise)
Tables in Hadoop
Tables in DB
SQL join
In-Memory Appliance
Oracle BI Foundation Suite
(ROLAP/MOLAP, Mobile,…)
Oracle SQL
Queries
Exalytics
Exadata
Big Data
Appliance
… with an Unified Information Management Architecture
Experiment, Prototype, Collaborate
» Quickly find, explore, transform,
analyze and share discoveries
in Big Data Discovery
» Publish results to the Hadoop
Distributed File System (HDFS)
» Use to build predictive models with
Oracle R for Hadoop
Productize, Secure, Govern
» Connect published HDFS files to
secure Oracle DB using Oracle
Big Data SQL
» No data movement required
» Seamlessly extends existing DWH
and BI investments with non-
traditional data in Hadoop
40
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 41
Example
Actionable Information: Enterprise Business Intelligence
Self-service BI across all Data Sources
Personalized access from everywhere
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 42
Actionable Insights: Predictive Business Intelligence
Oracle Business Intelligence: Dashboards, Alerts,...
» Understandable prediction results, Self-service BI
» Making analysis results available to every business user,
i.e. potential cross-selling effects to responsible Buyers
» Operated by Business Analysts / BICC, etc.
Oracle 12c In-Database Mining / Statistics
» Operationalize Data Mining Models as part of Oracle BI
Dashboards, calculated on-the-fly
» Available query types: Classification & regression (incl.
Multi-target problems), clustering, anomaly detection,
feature extraction
Predictive Query Example
SELECT cust_income_level, cust_id
, ROUND(probanom,2) AS probanom
, ROUND(pctrank,3)*100 AS pctrank
FROM (SELECT cust_id, cust_income_level, probanom
, PERCENT_RANK()
OVER (PARTITION BY cust_income_level
ORDER BY probanom DESC) AS pctrank
FROM (SELECT cust_id, cust_income_level
, PREDICTION_PROBABILITY(OF ANOMALY,0 USING *)
OVER (PARTITION BY cust_income_level)
AS probanom
FROM customers
)
)
WHERE pctrank <= .05
ORDER BY cust_income_level, probanom DESC;
Example
(1/2)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 43
Actionable Insights: Forward-looking Applications
Example: Oracle Human
Capital Management:
»Includes Oracle Advanced
Analytics factory-installed
Predictive Analytics:
»Employees likely to leave
& predicted performance
»Top reasons, expected
behavior
»Real-time "What if?"
analysis
Example
(2/2)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 44
Actionable Events: Intelligent Customer Experience
iBeacons
» Bluetooth Low Energy (BLE)
» Optimized for small bursts of data.
» Impressive battery Life
» Ideal for sensors
Requirements
– Find purchase pattern from data of shopper’s purchase history
– Leverage all the data, including real-time context from Beacon, CRM data, purchase
history data, to improve the relevance of the offer
– Leverage predictive models to alleviate the reliance on the rule based models
– Being able to understand customer’s feedback on Beacon marketing
Example
(1/2)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Actionable Events: Intelligent Customer Experience
Solution Architecture
Analysis and Offering
Decision Engine
Unstructured Text Analysis
(VOC analysis)
Rule Based
Statistical
Model-based
Modeling Processing
Real Time Offering
Qualitative indices
Text Mining
Data Dictionary
Text Analysis
Collection
Batch
collection
Real Time
Collection
Web Crawling
Open API
Storage and processing Utilization
ETL
TreatmentStore
Hadoop
File
Reduce
Map
HDFS
Datafile#1 HDFS
Datafile#2 HDFS
Datafile#n HDFS
NoSQL DB
Transaction
(Key-Value)
Stores
Big Data Connectors
Mobile Apps
Unstructured Data
Visualization
Coupon
Mileage
…..
New
information
Keywords
Visualization
Search Vigan
Visualization
Dash
Board
Mobile
Real
Time
Formal &
Informal
Integration
Source system
Other internal and external
systems
Beacon
Time
Phone Number
Distance
Beacon MAC
Customer
…..
Martial Status
Customer Type
Customer ID
…..
Num of Children
Occupation
Gender
Purchase
Amount
Product
Customer ID
…..
Quantity
Date
Smart App
Web
VOC
SNS
ODS DW
Advanced
Analytics on
Purchase
Pattern
Example
(2/2)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
Actionable Events: Intelligent Customer Experience
Solution Architecture – Product View
Analysis and Offering
Decision Engine
Unstructured Text Analysis
(VOC analysis)
Rule Based
Statistical
Model-based
Modeling Self-Learning
Real Time Offering
Qualitative indices
Text Mining
Data Dictionary
Text Analysis
Collection
Batch
collection
Real Time
Collection
Web Crawling
Open API
Storage and processing Utilization
ETL
TreatmentStore
Hadoop
File
Reduce
Map
HDFS
Datafile#1 HDFS
Datafile#2 HDFS
Datafile#n HDFS
NoSQL DB
Transaction
(Key-Value)
Stores
Big Data Connectors
Mobile Apps
Unstructured Data
Visualization
Coupon
Mileage
…..
New
information
Keywords
Visualization
Search Vigan
Visualization
Dash
Board
Mobile
Real
Time
Formal &
Informal
Integration
Source system
Other internal and external
systems
Beacon
Time
Phone Number
Distance
Beacon MAC
Customer
…..
Martial Status
Customer Type
Customer ID
…..
Num of Children
Occupation
Gender
Purchase
Amount
Product
Customer ID
…..
Quantity
Date
Smart App
Web
VOC
SNS
ODS DW
Advanced
Analytics on
Purchase
Pattern
Oracle Big Data Appliance
Oracle Event
Processing
Endeca
Information
Discovery
Oracle
Advanced
Analytics
OracleDatabase
Oracle Big Data
Connectors
Oracle Data
Integrator
Oracle Golden Gate
Oracle Data
Integrator
Example
(2/2)
Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 47

More Related Content

What's hot

2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing
2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing
2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing
Jeffrey T. Pollock
 
Contexti / Oracle - Big Data : From Pilot to Production
Contexti / Oracle - Big Data : From Pilot to ProductionContexti / Oracle - Big Data : From Pilot to Production
Contexti / Oracle - Big Data : From Pilot to Production
Contexti
 
Big Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data LakeBig Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data Lake
Caserta
 
Developing a Strategy for Data Lake Governance
Developing a Strategy for Data Lake GovernanceDeveloping a Strategy for Data Lake Governance
Developing a Strategy for Data Lake Governance
Tony Baer
 
The Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of HadoopThe Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of Hadoop
Inside Analysis
 
Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...
Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...
Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...
StampedeCon
 
The Value of the Modern Data Architecture with Apache Hadoop and Teradata
The Value of the Modern Data Architecture with Apache Hadoop and Teradata The Value of the Modern Data Architecture with Apache Hadoop and Teradata
The Value of the Modern Data Architecture with Apache Hadoop and Teradata
Hortonworks
 
Data Governance for Data Lakes
Data Governance for Data LakesData Governance for Data Lakes
Data Governance for Data Lakes
Kiran Kamreddy
 
The Data Lake - Balancing Data Governance and Innovation
The Data Lake - Balancing Data Governance and Innovation The Data Lake - Balancing Data Governance and Innovation
The Data Lake - Balancing Data Governance and Innovation
Caserta
 
Deploying a Governed Data Lake
Deploying a Governed Data LakeDeploying a Governed Data Lake
Deploying a Governed Data Lake
WaterlineData
 
Incorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic ArchitectureIncorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic Architecture
Caserta
 
The Emerging Data Lake IT Strategy
The Emerging Data Lake IT StrategyThe Emerging Data Lake IT Strategy
The Emerging Data Lake IT Strategy
Thomas Kelly, PMP
 
Agile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachAgile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric Approach
SoftServe
 
Data Governance, Compliance and Security in Hadoop with Cloudera
Data Governance, Compliance and Security in Hadoop with ClouderaData Governance, Compliance and Security in Hadoop with Cloudera
Data Governance, Compliance and Security in Hadoop with Cloudera
Caserta
 
Making Big Data Easy for Everyone
Making Big Data Easy for EveryoneMaking Big Data Easy for Everyone
Making Big Data Easy for Everyone
Caserta
 
Flash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lonFlash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lon
Jeffrey T. Pollock
 
You're the New CDO, Now What?
You're the New CDO, Now What?You're the New CDO, Now What?
You're the New CDO, Now What?
Caserta
 
Oracle Data Integration - Overview
Oracle Data Integration - OverviewOracle Data Integration - Overview
Oracle Data Integration - Overview
Jeffrey T. Pollock
 
Data Discovery & Lineage in Enterprise Hadoop
Data Discovery & Lineage in Enterprise HadoopData Discovery & Lineage in Enterprise Hadoop
Data Discovery & Lineage in Enterprise Hadoop
DataWorks Summit
 
Data Lakes - The Key to a Scalable Data Architecture
Data Lakes - The Key to a Scalable Data ArchitectureData Lakes - The Key to a Scalable Data Architecture
Data Lakes - The Key to a Scalable Data Architecture
Zaloni
 

What's hot (20)

2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing
2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing
2010.03.16 Pollock.Edw2010.Modern D Ifor Warehousing
 
Contexti / Oracle - Big Data : From Pilot to Production
Contexti / Oracle - Big Data : From Pilot to ProductionContexti / Oracle - Big Data : From Pilot to Production
Contexti / Oracle - Big Data : From Pilot to Production
 
Big Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data LakeBig Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data Lake
 
Developing a Strategy for Data Lake Governance
Developing a Strategy for Data Lake GovernanceDeveloping a Strategy for Data Lake Governance
Developing a Strategy for Data Lake Governance
 
The Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of HadoopThe Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of Hadoop
 
Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...
Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...
Beyond a Big Data Pilot: Building a Production Data Infrastructure - Stampede...
 
The Value of the Modern Data Architecture with Apache Hadoop and Teradata
The Value of the Modern Data Architecture with Apache Hadoop and Teradata The Value of the Modern Data Architecture with Apache Hadoop and Teradata
The Value of the Modern Data Architecture with Apache Hadoop and Teradata
 
Data Governance for Data Lakes
Data Governance for Data LakesData Governance for Data Lakes
Data Governance for Data Lakes
 
The Data Lake - Balancing Data Governance and Innovation
The Data Lake - Balancing Data Governance and Innovation The Data Lake - Balancing Data Governance and Innovation
The Data Lake - Balancing Data Governance and Innovation
 
Deploying a Governed Data Lake
Deploying a Governed Data LakeDeploying a Governed Data Lake
Deploying a Governed Data Lake
 
Incorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic ArchitectureIncorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic Architecture
 
The Emerging Data Lake IT Strategy
The Emerging Data Lake IT StrategyThe Emerging Data Lake IT Strategy
The Emerging Data Lake IT Strategy
 
Agile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachAgile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric Approach
 
Data Governance, Compliance and Security in Hadoop with Cloudera
Data Governance, Compliance and Security in Hadoop with ClouderaData Governance, Compliance and Security in Hadoop with Cloudera
Data Governance, Compliance and Security in Hadoop with Cloudera
 
Making Big Data Easy for Everyone
Making Big Data Easy for EveryoneMaking Big Data Easy for Everyone
Making Big Data Easy for Everyone
 
Flash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lonFlash session -streaming--ses1243-lon
Flash session -streaming--ses1243-lon
 
You're the New CDO, Now What?
You're the New CDO, Now What?You're the New CDO, Now What?
You're the New CDO, Now What?
 
Oracle Data Integration - Overview
Oracle Data Integration - OverviewOracle Data Integration - Overview
Oracle Data Integration - Overview
 
Data Discovery & Lineage in Enterprise Hadoop
Data Discovery & Lineage in Enterprise HadoopData Discovery & Lineage in Enterprise Hadoop
Data Discovery & Lineage in Enterprise Hadoop
 
Data Lakes - The Key to a Scalable Data Architecture
Data Lakes - The Key to a Scalable Data ArchitectureData Lakes - The Key to a Scalable Data Architecture
Data Lakes - The Key to a Scalable Data Architecture
 

Similar to Big Data Discovery

1° Sessione Oracle CRUI: Analytics Data Lab, the power of Big Data Investiga...
1° Sessione Oracle CRUI: Analytics Data Lab,  the power of Big Data Investiga...1° Sessione Oracle CRUI: Analytics Data Lab,  the power of Big Data Investiga...
1° Sessione Oracle CRUI: Analytics Data Lab, the power of Big Data Investiga...
Jürgen Ambrosi
 
Analytic Excellence - Saying Goodbye to Old Constraints
Analytic Excellence - Saying Goodbye to Old ConstraintsAnalytic Excellence - Saying Goodbye to Old Constraints
Analytic Excellence - Saying Goodbye to Old Constraints
Inside Analysis
 
6 enriching your data warehouse with big data and hadoop
6 enriching your data warehouse with big data and hadoop6 enriching your data warehouse with big data and hadoop
6 enriching your data warehouse with big data and hadoop
Dr. Wilfred Lin (Ph.D.)
 
Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?
Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?
Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?
Harald Erb
 
How to Identify, Train or Become a Data Scientist
How to Identify, Train or Become a Data ScientistHow to Identify, Train or Become a Data Scientist
How to Identify, Train or Become a Data Scientist
Inside Analysis
 
Why do Data Warehousing & Business Intelligence go hand in hand?
Why do Data Warehousing & Business Intelligence go hand in hand? Why do Data Warehousing & Business Intelligence go hand in hand?
Why do Data Warehousing & Business Intelligence go hand in hand?
Vineet Chaturvedi
 
Why Everything You Know About bigdata Is A Lie
Why Everything You Know About bigdata Is A LieWhy Everything You Know About bigdata Is A Lie
Why Everything You Know About bigdata Is A Lie
Sunil Ranka
 
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BI
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIAugmentation, Collaboration, Governance: Defining the Future of Self-Service BI
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BI
Denodo
 
Mastering in data warehousing & BusinessIintelligence
Mastering in data warehousing & BusinessIintelligenceMastering in data warehousing & BusinessIintelligence
Mastering in data warehousing & BusinessIintelligence
Edureka!
 
The Data Lake: Empowering Your Data Science Team
The Data Lake: Empowering Your Data Science TeamThe Data Lake: Empowering Your Data Science Team
The Data Lake: Empowering Your Data Science Team
Senturus
 
A7 getting value from big data how to get there quickly and leverage your c...
A7   getting value from big data how to get there quickly and leverage your c...A7   getting value from big data how to get there quickly and leverage your c...
A7 getting value from big data how to get there quickly and leverage your c...
Dr. Wilfred Lin (Ph.D.)
 
Cloudian 451-hortonworks - webinar
Cloudian 451-hortonworks - webinarCloudian 451-hortonworks - webinar
Cloudian 451-hortonworks - webinar
Hortonworks
 
BAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, SydneyBAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, Sydney
Sai Paravastu
 
Conociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big data
Conociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big dataConociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big data
Conociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big data
Mundo Contact
 
Oracle big data publix sector 1
Oracle big data publix sector 1Oracle big data publix sector 1
Oracle big data publix sector 1
Redazione InnovaPuglia
 
Unlocking New Insights with Information Discovery
Unlocking New Insights with Information DiscoveryUnlocking New Insights with Information Discovery
Unlocking New Insights with Information Discovery
Alithya
 
Keyrus US Information
Keyrus US InformationKeyrus US Information
Keyrus US Information
Devon Ziegenfuss
 
Keyrus US Information
Keyrus US InformationKeyrus US Information
Keyrus US Information
Julian Tong
 
Big Data analytics per le IT Operations
Big Data analytics per le IT OperationsBig Data analytics per le IT Operations
Big Data analytics per le IT Operations
HP Enterprise Italia
 
Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Chain Sys Corporation
 

Similar to Big Data Discovery (20)

1° Sessione Oracle CRUI: Analytics Data Lab, the power of Big Data Investiga...
1° Sessione Oracle CRUI: Analytics Data Lab,  the power of Big Data Investiga...1° Sessione Oracle CRUI: Analytics Data Lab,  the power of Big Data Investiga...
1° Sessione Oracle CRUI: Analytics Data Lab, the power of Big Data Investiga...
 
Analytic Excellence - Saying Goodbye to Old Constraints
Analytic Excellence - Saying Goodbye to Old ConstraintsAnalytic Excellence - Saying Goodbye to Old Constraints
Analytic Excellence - Saying Goodbye to Old Constraints
 
6 enriching your data warehouse with big data and hadoop
6 enriching your data warehouse with big data and hadoop6 enriching your data warehouse with big data and hadoop
6 enriching your data warehouse with big data and hadoop
 
Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?
Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?
Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?
 
How to Identify, Train or Become a Data Scientist
How to Identify, Train or Become a Data ScientistHow to Identify, Train or Become a Data Scientist
How to Identify, Train or Become a Data Scientist
 
Why do Data Warehousing & Business Intelligence go hand in hand?
Why do Data Warehousing & Business Intelligence go hand in hand? Why do Data Warehousing & Business Intelligence go hand in hand?
Why do Data Warehousing & Business Intelligence go hand in hand?
 
Why Everything You Know About bigdata Is A Lie
Why Everything You Know About bigdata Is A LieWhy Everything You Know About bigdata Is A Lie
Why Everything You Know About bigdata Is A Lie
 
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BI
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIAugmentation, Collaboration, Governance: Defining the Future of Self-Service BI
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BI
 
Mastering in data warehousing & BusinessIintelligence
Mastering in data warehousing & BusinessIintelligenceMastering in data warehousing & BusinessIintelligence
Mastering in data warehousing & BusinessIintelligence
 
The Data Lake: Empowering Your Data Science Team
The Data Lake: Empowering Your Data Science TeamThe Data Lake: Empowering Your Data Science Team
The Data Lake: Empowering Your Data Science Team
 
A7 getting value from big data how to get there quickly and leverage your c...
A7   getting value from big data how to get there quickly and leverage your c...A7   getting value from big data how to get there quickly and leverage your c...
A7 getting value from big data how to get there quickly and leverage your c...
 
Cloudian 451-hortonworks - webinar
Cloudian 451-hortonworks - webinarCloudian 451-hortonworks - webinar
Cloudian 451-hortonworks - webinar
 
BAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, SydneyBAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, Sydney
 
Conociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big data
Conociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big dataConociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big data
Conociendo y entendiendo a tu cliente mediante monitoreo, analíticos y big data
 
Oracle big data publix sector 1
Oracle big data publix sector 1Oracle big data publix sector 1
Oracle big data publix sector 1
 
Unlocking New Insights with Information Discovery
Unlocking New Insights with Information DiscoveryUnlocking New Insights with Information Discovery
Unlocking New Insights with Information Discovery
 
Keyrus US Information
Keyrus US InformationKeyrus US Information
Keyrus US Information
 
Keyrus US Information
Keyrus US InformationKeyrus US Information
Keyrus US Information
 
Big Data analytics per le IT Operations
Big Data analytics per le IT OperationsBig Data analytics per le IT Operations
Big Data analytics per le IT Operations
 
Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...Neoaug 2013 critical success factors for data quality management-chain-sys-co...
Neoaug 2013 critical success factors for data quality management-chain-sys-co...
 

More from Harald Erb

Actionable Insights with AI - Snowflake for Data Science
Actionable Insights with AI - Snowflake for Data ScienceActionable Insights with AI - Snowflake for Data Science
Actionable Insights with AI - Snowflake for Data Science
Harald Erb
 
Snowflake for Data Engineering
Snowflake for Data EngineeringSnowflake for Data Engineering
Snowflake for Data Engineering
Harald Erb
 
Dataiku & Snowflake Meetup Berlin 2020
Dataiku & Snowflake Meetup Berlin 2020Dataiku & Snowflake Meetup Berlin 2020
Dataiku & Snowflake Meetup Berlin 2020
Harald Erb
 
Does it only have to be ML + AI?
Does it only have to be ML + AI?Does it only have to be ML + AI?
Does it only have to be ML + AI?
Harald Erb
 
Delivering rapid-fire Analytics with Snowflake and Tableau
Delivering rapid-fire Analytics with Snowflake and TableauDelivering rapid-fire Analytics with Snowflake and Tableau
Delivering rapid-fire Analytics with Snowflake and Tableau
Harald Erb
 
Machine Learning - Eine Challenge für Architekten
Machine Learning - Eine Challenge für ArchitektenMachine Learning - Eine Challenge für Architekten
Machine Learning - Eine Challenge für Architekten
Harald Erb
 
DOAG Big Data Days 2017 - Cloud Journey
DOAG Big Data Days 2017 - Cloud JourneyDOAG Big Data Days 2017 - Cloud Journey
DOAG Big Data Days 2017 - Cloud Journey
Harald Erb
 
Do you know what k-Means? Cluster-Analysen
Do you know what k-Means? Cluster-Analysen Do you know what k-Means? Cluster-Analysen
Do you know what k-Means? Cluster-Analysen
Harald Erb
 
Big Data Discovery + Analytics = Datengetriebene Innovation!
Big Data Discovery + Analytics = Datengetriebene Innovation!Big Data Discovery + Analytics = Datengetriebene Innovation!
Big Data Discovery + Analytics = Datengetriebene Innovation!
Harald Erb
 
DOAG News 2012 - Analytische Mehrwerte mit Big Data
DOAG News 2012 - Analytische Mehrwerte mit Big DataDOAG News 2012 - Analytische Mehrwerte mit Big Data
DOAG News 2012 - Analytische Mehrwerte mit Big Data
Harald Erb
 
Oracle Unified Information Architeture + Analytics by Example
Oracle Unified Information Architeture + Analytics by ExampleOracle Unified Information Architeture + Analytics by Example
Oracle Unified Information Architeture + Analytics by Example
Harald Erb
 
Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...
Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...
Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...
Harald Erb
 

More from Harald Erb (12)

Actionable Insights with AI - Snowflake for Data Science
Actionable Insights with AI - Snowflake for Data ScienceActionable Insights with AI - Snowflake for Data Science
Actionable Insights with AI - Snowflake for Data Science
 
Snowflake for Data Engineering
Snowflake for Data EngineeringSnowflake for Data Engineering
Snowflake for Data Engineering
 
Dataiku & Snowflake Meetup Berlin 2020
Dataiku & Snowflake Meetup Berlin 2020Dataiku & Snowflake Meetup Berlin 2020
Dataiku & Snowflake Meetup Berlin 2020
 
Does it only have to be ML + AI?
Does it only have to be ML + AI?Does it only have to be ML + AI?
Does it only have to be ML + AI?
 
Delivering rapid-fire Analytics with Snowflake and Tableau
Delivering rapid-fire Analytics with Snowflake and TableauDelivering rapid-fire Analytics with Snowflake and Tableau
Delivering rapid-fire Analytics with Snowflake and Tableau
 
Machine Learning - Eine Challenge für Architekten
Machine Learning - Eine Challenge für ArchitektenMachine Learning - Eine Challenge für Architekten
Machine Learning - Eine Challenge für Architekten
 
DOAG Big Data Days 2017 - Cloud Journey
DOAG Big Data Days 2017 - Cloud JourneyDOAG Big Data Days 2017 - Cloud Journey
DOAG Big Data Days 2017 - Cloud Journey
 
Do you know what k-Means? Cluster-Analysen
Do you know what k-Means? Cluster-Analysen Do you know what k-Means? Cluster-Analysen
Do you know what k-Means? Cluster-Analysen
 
Big Data Discovery + Analytics = Datengetriebene Innovation!
Big Data Discovery + Analytics = Datengetriebene Innovation!Big Data Discovery + Analytics = Datengetriebene Innovation!
Big Data Discovery + Analytics = Datengetriebene Innovation!
 
DOAG News 2012 - Analytische Mehrwerte mit Big Data
DOAG News 2012 - Analytische Mehrwerte mit Big DataDOAG News 2012 - Analytische Mehrwerte mit Big Data
DOAG News 2012 - Analytische Mehrwerte mit Big Data
 
Oracle Unified Information Architeture + Analytics by Example
Oracle Unified Information Architeture + Analytics by ExampleOracle Unified Information Architeture + Analytics by Example
Oracle Unified Information Architeture + Analytics by Example
 
Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...
Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...
Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...
 

Recently uploaded

all about the data science process, covering the steps present in almost ever...
all about the data science process, covering the steps present in almost ever...all about the data science process, covering the steps present in almost ever...
all about the data science process, covering the steps present in almost ever...
palaniappancse
 
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
janvikumar4133
 
Biometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdfBiometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdf
Joel Ngushwai
 
Introduction to the Red Hat Portfolio.pdf
Introduction to the Red Hat Portfolio.pdfIntroduction to the Red Hat Portfolio.pdf
Introduction to the Red Hat Portfolio.pdf
kihus38
 
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
birajmohan012
 
Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data
Fine-Tuning of Small/Medium LLMs for Business QA on Structured DataFine-Tuning of Small/Medium LLMs for Business QA on Structured Data
Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data
kevig
 
Maruti Wagon R on road price in Faridabad - CarDekho
Maruti Wagon R on road price in Faridabad - CarDekhoMaruti Wagon R on road price in Faridabad - CarDekho
Maruti Wagon R on road price in Faridabad - CarDekho
kamli sharma#S10
 
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
dizzycaye
 
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy DsouzaOpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata
 
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
tanupasswan6
 
CHAPTER-1-Introduction-to-Marketing.pptx
CHAPTER-1-Introduction-to-Marketing.pptxCHAPTER-1-Introduction-to-Marketing.pptx
CHAPTER-1-Introduction-to-Marketing.pptx
girewiy968
 
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
ginni singh$A17
 
potential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in generalpotential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in general
huseindihon
 
Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
6459astrid
 
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
satpalsheravatmumbai
 
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
sharonblush
 
Welcome back to Instagram. Sign in to check out what your
Welcome back to Instagram. Sign in to check out what yourWelcome back to Instagram. Sign in to check out what your
Welcome back to Instagram. Sign in to check out what your
Virni Arrora
 
ch8_multiplexing cs553 st07 slide share ss
ch8_multiplexing cs553 st07 slide share ssch8_multiplexing cs553 st07 slide share ss
ch8_multiplexing cs553 st07 slide share ss
MinThetLwin1
 
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
revolutionary575
 
Harendra Singh, AI Strategy and Consulting Portfolio
Harendra Singh, AI Strategy and Consulting PortfolioHarendra Singh, AI Strategy and Consulting Portfolio
Harendra Singh, AI Strategy and Consulting Portfolio
harendmgr
 

Recently uploaded (20)

all about the data science process, covering the steps present in almost ever...
all about the data science process, covering the steps present in almost ever...all about the data science process, covering the steps present in almost ever...
all about the data science process, covering the steps present in almost ever...
 
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
 
Biometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdfBiometric Question Bank 2021 - 1 Soln-1.pdf
Biometric Question Bank 2021 - 1 Soln-1.pdf
 
Introduction to the Red Hat Portfolio.pdf
Introduction to the Red Hat Portfolio.pdfIntroduction to the Red Hat Portfolio.pdf
Introduction to the Red Hat Portfolio.pdf
 
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
 
Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data
Fine-Tuning of Small/Medium LLMs for Business QA on Structured DataFine-Tuning of Small/Medium LLMs for Business QA on Structured Data
Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data
 
Maruti Wagon R on road price in Faridabad - CarDekho
Maruti Wagon R on road price in Faridabad - CarDekhoMaruti Wagon R on road price in Faridabad - CarDekho
Maruti Wagon R on road price in Faridabad - CarDekho
 
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
 
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy DsouzaOpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
 
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
 
CHAPTER-1-Introduction-to-Marketing.pptx
CHAPTER-1-Introduction-to-Marketing.pptxCHAPTER-1-Introduction-to-Marketing.pptx
CHAPTER-1-Introduction-to-Marketing.pptx
 
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
 
potential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in generalpotential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in general
 
Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Premium Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
 
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
VIP Kanpur Girls Call Kanpur 0X0000000X Doorstep High-Profile Girl Service Ca...
 
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
 
Welcome back to Instagram. Sign in to check out what your
Welcome back to Instagram. Sign in to check out what yourWelcome back to Instagram. Sign in to check out what your
Welcome back to Instagram. Sign in to check out what your
 
ch8_multiplexing cs553 st07 slide share ss
ch8_multiplexing cs553 st07 slide share ssch8_multiplexing cs553 st07 slide share ss
ch8_multiplexing cs553 st07 slide share ss
 
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
 
Harendra Singh, AI Strategy and Consulting Portfolio
Harendra Singh, AI Strategy and Consulting PortfolioHarendra Singh, AI Strategy and Consulting Portfolio
Harendra Singh, AI Strategy and Consulting Portfolio
 

Big Data Discovery

  • 1. Copyright © 2015, Oracle and/or its affiliates. All rights reserved.
  • 2. Copyright © 2015, Oracle and/or its affiliates. All rights reserved.Copyright © 2015, Oracle and/or its affiliates. All rights reserved. | Big Data Discovery Relevante Informationen im Daten-Meer identifizieren und nutzbar machen Harald Erb Oracle Business Analytics OPITZ CONSULTING inspire|IT - Konferenz Frankfurt/Main, 11. Mai 2015
  • 3. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. » Harald Erb » Principal Sales Consultant » Business Analytics Architecture Domain Lead - DE/CH Cluster » Kontakt +49 (0)6103 397-403 » harald.erb@oracle.com Referent
  • 4. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. 4
  • 5. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 5
  • 6. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 6 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 Characteristics of Digital Business Leaders
  • 7. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Data-driven Decisions 7 Identify(business)question 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 Verifyearlierfindings Find out who has investi- gated such or a similar problem in the past and the approach that has been taken Designofasolutionmodel Formulate a detailled hypothesis how specific variables might influence the result of the chosen model Gatherallnecessarydata Analysethedata Present&implementresults 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. Data Science + Knowledge Discovery Adopted from Thomas H. Davenport, Harvard Business Manager 2013
  • 8. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 8 Vertical and Horizontal Data Science Skills Data Warehouse HorizontalVertical Deep technical skills Eigenvalues, Lasso-related regressions Experts in Bayesian networks, R Support Vector Machine Hadoop, NoSQL, Data Modeling, DW Cross-discipline knowledge Machine Learning & Statistics Visualization skills Domain expertise Storytelling experts Programming experience Aware of pitfalls & rules of thumb The Specialist The Unicorn Look for the individual Unicorn or build a Data Science Team?
  • 9. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Enabling Data-driven Innovations in Organizations 9 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
  • 10. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 10 Information Management – Conceptual View Discovery Lab Innovation Discovery Output Events & Data Actionable Events Event Engine Data Reservoir Data Factory Enterprise Information Store Business Intelligence Actionable Information Actionable Insights Data Streams Execution Structured Enterprise Data Other Data Line of governance Source: Oracle White Paper “Information Management and Big Data – A Reference Architecture” ACC BICC
  • 11. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. » Event Engine: Components which process data in-flight to identify actionable events and then determine next-best- action based on decision context and event profile data and persist in a durable storage system. » Data Reservoir: Economical, scale-out storage and parallel processing for data which does not have stringent requirements for formalisation or modelling. Typically manifested as a Hadoop cluster or staging area in a relational database. » Data Factory: Management and orchestration of data into and between the Data Reservoir and Enterprise Information Store as well as the rapid provisioning of data into the Discovery Lab for agile discovery. » Enterprise Information Store: Large scale formalised and modelled business critical data store, typically manifested by an (Enterprise) Data Warehouse. When combined with a Data Reservoir, these form a Big Data Management System. » Reporting: BI tools and infrastructure components for timely and accurate reporting. » Discovery Lab: A set of data stores, processing engines, and analysis tools separate from the everyday processing of data to facilitate the discovery of new knowledge of value to the business. This includes the ability to provision new data into the Discovery Lab from outside the architecture. » Execution: Flow of data for execution are tasks which support and inform daily operations » Innovation: Flow of data for innovation are tasks which drive new insights back to the business » Arranging solutions on either side of this division (as shown by the red line) helps inform system requirements for security, governance, and timeliness. Information Management – Conceptual View 11 Source: Oracle White Paper “Information Management and Big Data – A Reference Architecture”
  • 12. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Discovery Lab: Design Pattern » Iterative development approach – data oriented NOT development oriented » Small group of highly skilled individuals (aka “Data Scientists” or a team organized as an Analytical Competence Center, ACC) with privileged access to enterprise data sources » Specific focus on identifying commercial value for exploitation » Wide range of tools and techniques applied » Typically separate infrastructure but could also be unified Reservoir if resource managed effectively » Data provisioned through Data Factory or own ETL processes ACC 12
  • 13. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 13 Discovery Lab: Activity Cycles
  • 14. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Discovery Lab: Data Provisioning 14 Analysis Processing & Delivery Discovery Lab & Development Environment Data Science (Primary Toolset) Statistics Tools Data & Text Mining Tools Faceted Query Tools Programming & Scripting Data Modelling Tools Query & Search Tools Pre-Built Intelligence Assets Intelligence Analysis Tools Ad Hoc Query & Analysis Tools OLAP Tools Forecasting & Simulation Tools Reporting Tools Virtualisation& InformationServices Data Factory flow ACC may quickly develop new reporting through mashups from any available internal and external sources and may used advanced analytical tools for innovative analysis Data Quality & Profiling Graphical rendering tools Dashboards & Reports Scorecards Charts & Graphs Sandbox – Project 3 Sandbox – Project 2 Sandbox – Project 1 Data store Analytical Processing General BI flow 1 2 The majority of BI development activity will be from existing sources – performed by the BICC developing new reports to existing or new channels Raw Data ACC BICC
  • 15. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Need To Get Analytic Value Fast 15 Tool Complexity » Early Hadoop tools only for experts » Existing BI tools not designed for Hadoop » Emerging solutions lack broad capabilities 80% effort typically spent on evaluating and preparing data Data Uncertainty » Not familiar and overwhelming » Potential value not obvious » Requires significant manipulation Overly dependent on scarce and highly skilled resources
  • 16. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Oracle Big Data Discovery 16
  • 17. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 17 Oracle Big Data Discovery: The Visual Face of Hadoop find explore transform discover share
  • 18. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Oracle Big Data Discovery: Components 18 Oracle Big Data Discovery Workloads Hadoop Cluster (Oracle Big Data Appliance or Commodity Hardware with Cloudera CDH 5.) BDD node data node data node data node data node name node Data Processing, Workflow & Monitoring • Profiling: catalog entry creation, data type & language detection, schema configuration • Sampling: dgraph (index) file creation • Transforms: >100 functions • Enrichments: location (geo), text (cleanup, sentiment, entity, key-phrase, whitelist tagging) Self-Service Provisioning & Data Transfer • Personal Data: Upload CSV and XLS to HDFS In-Memory Discovery Indexes • DGraph: Search, Guided Navigation, Analytics Studio • Web UI: Find, Explore, Transform, Discover, Share Hadoop 2.x Filesystem (HDFS) Workload Mgmt (YARN) Metadata (HCatalog) Other Hadoop Workloads MapReduce Spark Hive Pig Oracle Big Data SQL (Oracle Big Data Appliance only)
  • 19. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Oracle Big Data Discovery: Deployment Example 19 Diagram adopted from RittmannMead, Blog, 2015
  • 20. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 20 Oracle Big Data Discovery: Preparation of Data Sources Have to be created as Hive Tables and registered in the Hive Metastore Hive Table definition for fixed-width or delimited files Hive Table with a standard Regex SerDe (“Serializer-Deserializer”) to map more complex file structures by using Regular Expressions into regular table columns Hive Table using a custom developed SerDe to map nested file structures of a JSON file into regular table columns
  • 21. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Big Data Discovery Upload of XLS und CSV files and automatic Hive Table creation HUE (Hadoop User Experience) Upload of various file formats, table creation wizzards, web-based Hive Query Client 21 Oracle Big Data Discovery: Preparation of Data Sources There are multiple ways to get new Data Sets loaded… Hive Command Line Interface is similar to the MySQL command line
  • 22. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Oracle Big Data Discovery: Preparation of Data Sources 22 …or by using your favorite Data Integration / ETL Tool Oracle Data Integrator 12.1.3 with Advanced Big Data Option (Supporting HDFS, Hive, HBase, Scoop, Pig, Spark) IKM SQL to Hive- HBase-File (SQOOP) IKM File-Hive To Oracle (OLH, OSCH) File (FS/HDFS) IKM File To Hive (Load Data) Any RDBMS Oracle DB Any RDBMS IKM File-Hive to SQL (SQOOP) IKM Hive Transform IKM Hive Control Append Hive Hive HBase Hive Hive HBase LKM HBase to Hive IKM Hive to HBase
  • 23. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 23 Data Processing Workflow including Profiling and Enrichment Oracle Big Data Discovery: Data Ingestion 1M of 100M Diagram Source: RittmannMead Blog, 2015
  • 24. Copyright © 2015, Oracle and/or its affiliates. All rights reserved.Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 24 Demonstration Oracle Big Data Discovery Oracle Big Data Discovery Demonstration
  • 25. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Catalog 25 » Access a rich, interactive catalog of all data in Hadoop » Familiar search and guided navigation for ease of use » See data set summaries, user annotation and recommendations » Provision personal and enterprise data to Hadoop via self- service
  • 26. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Explore 26 » 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
  • 27. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 2727 » Intuitive, user driven data wrangling » 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
  • 28. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 28 Preferred method for the Business Analyst Transform – User friendly…
  • 29. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 29 (based on Groovy Programming Language / Library) Preferred Method for IT / Data Engineer / Data Scientist … Transform – … but flexible
  • 30. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 30 » Join and blend data for deeper perspectives » Easy usage - 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
  • 31. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 31 » Share projects, bookmarks and snapshots with others » Build galleries and tell big data stories » Collaborate and iterate as a team » Publish blended data to HDFS for leverage in other tools Share
  • 32. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 32 Data Discovery & Analytics Lifecycle Typical Effort
  • 33. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 33 Data Discovery & Analytics Lifecycle More Time left for Analysis and Interpretation of Results
  • 34. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Analytics: More Data Variety available – Better Results 34 Example: Marketing Campaigns Getting „lift“ on responders Data Mining-based prediction results with Response Modelling including hundreds of input variables like: Naïve Guess or Random 1000 Population Size (% of Total Cases) %ofPositiveResponders Model with 20 variables Model with 75 variables Model with 250 variables » Demographic data » Purchase POS transactional data » Polystructured data, text & comments » Spatial location data » Long term vs. recent historical behaviour » Web visits » Sensor data » … 100
  • 35. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Oracle R Enterprise (ORE) » Allows distributed processing of huge data volumes » Benefits from DB features, e.g. Security and SQL access » R Studio = GUI for Data Analysts 35 Oracle Data Mining (ODM) » Implemented in the Oracle Database kernel » Direct access via PL/SQL API and SQL operators » Oracle Data Miner GUI embedded in SQL Developer Oracle Advanced Analytics Native SQL Data Mining/Analytic Functions + High-performance R Integration
  • 36. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 36 Monetizing New Insights
  • 37. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Discovery Lab: End of Research Phase or Project Oracle Advanced Analytics Oracle Big Data Discovery Apply statistical & predictive models No Data Movement; Bring algorithms to the data Utilize Oracle R and Data Mining for Massive Computing Scalability on Hadoop or Oracle Integrated with SQL and BI tools Find data for analytics & data science projects Explore the shape and quality of the data Transform data for analytics Discover and visualize insights in data sets Share insights with analysts and downstream systems Share Insight Interpret & Evaluate Select, Prepare & Transform
  • 38. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Documention of Analyis Steps Providing a clear view on probabilities 38 Storytelling / Infographics Discovery Lab: Explanation & Validation of the Results Individuals of the Analytical Competence Center need to frame the results obtained in a comprehensible story. This kind of presentation intends to motivate decision makers and relevant stake-holders to take action Result of 1000 simulations of a $100 million investment in a new factory: Estimation expects an annual return of 20% over a 10-year lifespan, but the risk to loose invested money is still 8%Big Data Discovery – Gallery feature documents all discovery steps taken to achieve new insights Example of individually created Infographics explaining key findings of new insights
  • 39. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 39 Discovery and monetising steps have different requirements Making Sense from Diverse Data… Research & Development » Unbounded discovery » Self-Service sandbox » Wide toolset » Agile methods Promotion to Data-driven Services » Commercial exploitation » Narrower toolset » Integration to operations » Non-functional requirements » Code standardisation & governance
  • 40. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Productize, Secure & Govern Experiment, Prototype & Collaborate Data Reservoir Polystructured Data Data Warehouse Oracle 12c Database StructuredData Oracle Big Data Discovery Oracle Big Data SQL Hadoop (HDFS) Oracle R for Hadoop Oracle Advanced Analytics (Data Mining, Oracle R Enterprise) Tables in Hadoop Tables in DB SQL join In-Memory Appliance Oracle BI Foundation Suite (ROLAP/MOLAP, Mobile,…) Oracle SQL Queries Exalytics Exadata Big Data Appliance … with an Unified Information Management Architecture Experiment, Prototype, Collaborate » Quickly find, explore, transform, analyze and share discoveries in Big Data Discovery » Publish results to the Hadoop Distributed File System (HDFS) » Use to build predictive models with Oracle R for Hadoop Productize, Secure, Govern » Connect published HDFS files to secure Oracle DB using Oracle Big Data SQL » No data movement required » Seamlessly extends existing DWH and BI investments with non- traditional data in Hadoop 40
  • 41. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 41 Example Actionable Information: Enterprise Business Intelligence Self-service BI across all Data Sources Personalized access from everywhere
  • 42. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 42 Actionable Insights: Predictive Business Intelligence Oracle Business Intelligence: Dashboards, Alerts,... » Understandable prediction results, Self-service BI » Making analysis results available to every business user, i.e. potential cross-selling effects to responsible Buyers » Operated by Business Analysts / BICC, etc. Oracle 12c In-Database Mining / Statistics » Operationalize Data Mining Models as part of Oracle BI Dashboards, calculated on-the-fly » Available query types: Classification & regression (incl. Multi-target problems), clustering, anomaly detection, feature extraction Predictive Query Example SELECT cust_income_level, cust_id , ROUND(probanom,2) AS probanom , ROUND(pctrank,3)*100 AS pctrank FROM (SELECT cust_id, cust_income_level, probanom , PERCENT_RANK() OVER (PARTITION BY cust_income_level ORDER BY probanom DESC) AS pctrank FROM (SELECT cust_id, cust_income_level , PREDICTION_PROBABILITY(OF ANOMALY,0 USING *) OVER (PARTITION BY cust_income_level) AS probanom FROM customers ) ) WHERE pctrank <= .05 ORDER BY cust_income_level, probanom DESC; Example (1/2)
  • 43. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 43 Actionable Insights: Forward-looking Applications Example: Oracle Human Capital Management: »Includes Oracle Advanced Analytics factory-installed Predictive Analytics: »Employees likely to leave & predicted performance »Top reasons, expected behavior »Real-time "What if?" analysis Example (2/2)
  • 44. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 44 Actionable Events: Intelligent Customer Experience iBeacons » Bluetooth Low Energy (BLE) » Optimized for small bursts of data. » Impressive battery Life » Ideal for sensors Requirements – Find purchase pattern from data of shopper’s purchase history – Leverage all the data, including real-time context from Beacon, CRM data, purchase history data, to improve the relevance of the offer – Leverage predictive models to alleviate the reliance on the rule based models – Being able to understand customer’s feedback on Beacon marketing Example (1/2)
  • 45. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Actionable Events: Intelligent Customer Experience Solution Architecture Analysis and Offering Decision Engine Unstructured Text Analysis (VOC analysis) Rule Based Statistical Model-based Modeling Processing Real Time Offering Qualitative indices Text Mining Data Dictionary Text Analysis Collection Batch collection Real Time Collection Web Crawling Open API Storage and processing Utilization ETL TreatmentStore Hadoop File Reduce Map HDFS Datafile#1 HDFS Datafile#2 HDFS Datafile#n HDFS NoSQL DB Transaction (Key-Value) Stores Big Data Connectors Mobile Apps Unstructured Data Visualization Coupon Mileage ….. New information Keywords Visualization Search Vigan Visualization Dash Board Mobile Real Time Formal & Informal Integration Source system Other internal and external systems Beacon Time Phone Number Distance Beacon MAC Customer ….. Martial Status Customer Type Customer ID ….. Num of Children Occupation Gender Purchase Amount Product Customer ID ….. Quantity Date Smart App Web VOC SNS ODS DW Advanced Analytics on Purchase Pattern Example (2/2)
  • 46. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. Actionable Events: Intelligent Customer Experience Solution Architecture – Product View Analysis and Offering Decision Engine Unstructured Text Analysis (VOC analysis) Rule Based Statistical Model-based Modeling Self-Learning Real Time Offering Qualitative indices Text Mining Data Dictionary Text Analysis Collection Batch collection Real Time Collection Web Crawling Open API Storage and processing Utilization ETL TreatmentStore Hadoop File Reduce Map HDFS Datafile#1 HDFS Datafile#2 HDFS Datafile#n HDFS NoSQL DB Transaction (Key-Value) Stores Big Data Connectors Mobile Apps Unstructured Data Visualization Coupon Mileage ….. New information Keywords Visualization Search Vigan Visualization Dash Board Mobile Real Time Formal & Informal Integration Source system Other internal and external systems Beacon Time Phone Number Distance Beacon MAC Customer ….. Martial Status Customer Type Customer ID ….. Num of Children Occupation Gender Purchase Amount Product Customer ID ….. Quantity Date Smart App Web VOC SNS ODS DW Advanced Analytics on Purchase Pattern Oracle Big Data Appliance Oracle Event Processing Endeca Information Discovery Oracle Advanced Analytics OracleDatabase Oracle Big Data Connectors Oracle Data Integrator Oracle Golden Gate Oracle Data Integrator Example (2/2)
  • 47. Copyright © 2015, Oracle and/or its affiliates. All rights reserved. 47