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The Future of Analyticsor „The New SQL“
Gerhard Otterbach, Sales Manager Teradata Germany
Hanau, Feb. 28th , 2018
“Teradata was big data before there was big data”Donald Feinberg , Gartner
Teradata At A Glance: 39 Years Ago
>High-
impact
business
outcomes
Solving business problems through innovative use of data and analytics:
• Data and Analytic Strategy
• Business Value Frameworks
• Data Science Services
Business Solutions
Design, build, deployment, maintenance and operation of Analytic solutions based on Teradata and Open Source technologies:
• Data Strategy and Roadmaps
• Ecosystem Architecture
• Design and Implementation
• Managed Services
Architecture Expertise
Scalable and high-performance DBMS, Analytic and Integration Fabric technologies, available on-premise and in
the Cloud
• Teradata database, • Teradata Aster® Analytics, • Hadoop • Teradata QueryGrid™,
Presto, • Listener™, • Unity, • AppCenter
TechnologySolutions
Our Portfolio:
Teradata At A Glance
• ~1,400 + Customers in 77 Countries
• ~10,000 Employees including ~5,000 Consultants
• Market Cap: U.S. $4 Billion+
• World’s Most Ethical Companies –Ethisphere Institute
• Fortune: Top 10 U.S. Software Company
• The “Completeness of Vision” leader in Gartner’s Magic Quadrant “Data Warehouse and Data Management Solutions for Analytics”
• Leader in the Forrester Wave™:
Big Data Hadoop-Optimized Systems
In-Memory Database Platforms
4
Challenge I – The Big Data Landscape
5
Challenge II – The Never Ending Fight – But who is right?
vs. No-SQLSQL
6
Challenge II – The Never Ending Fight – But who is right?
AND! No-SQLSQL
7
Challenge III – Data Lake?
vs. No ControlGovernance/Control
8
Challenge III – Data Reservoir!
Controlled „No Control“
9
De
scrip
tive
Pre
dic
tive
Pre
scrip
tive
What is happening?REPORTING
ANALYZING
PREDICTING
MACHINE LEARNING
DEEP LEARNING
Is it real? Why is it happening?
What are the hidden patterns? What will happen next?
Self-learning systems with regression.
Deep neural networks.
OPERATIONALIZING What is happening right now?
ACTIVATING Make it happen with automation.
2010s
2000s
1990s
Challenge IV – Artificial Intelligence HypeAnalytical Evolution in the past 30 years
10
Challenge IV – Artificial Intelligence HypeArtificial Intelligence outside the Charts – Hard touch down to be expected!
10
Artificial Intelligence
Deep Learning
Machine Learning
Data Science
Big Data
AdvancedAnalytics
PredictiveAnalytics
DataMining
IDA
Source: Alexander Linden, Gartner BI Summit, March 2017
11
The solution: Analyze Anything
Connectors and APIs
SQL/No-SQL Engine
Adv. AnalyticsEngines
Data Mgmt.Engine
AIEngine
Custom Engines
Tools and
Languages
Analytic Functions, Engines, and Data Types
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The solution: The Teradata Analytics Platform
Connectors and APIs
Teradata + Hadoop
TD ASTER SPARK TensorFlow Custom Engines*
Tools and
Languages
Analytic Functions, Engines, and Data Types
* Future
Storage: Teradata, Hadoop, S3/BloBStorage…
Deployment Options: On Premise; Public Cloud, Private Cloud; Hybrid
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Teradata
Engine
Persistent Storage
Cross EngineOrchestration
SQL Access QueryPoint, BI and Visualization tools
Hadoop
S3/BloB*
Others
* Future
CustomEngine*
AsterEngines
Tensor-Flow
Engine
SparkEngine
S3/BLOB Storage*
LanguagesSQL, Python, R, C/C++, JavaScript, Java, Perl,
Ruby
ToolsJupyter, RStudio,
KNIME, SAS, Dataiku
The solution: The Teradata Analytics Platform
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Teradata Analytics Platform Deployment Strategies
14
Move
Anytime
Buy Any
Way
Analyze
Anything
Deploy
Anywhere
Same Teradata Software across all deployment options enables seamless application portability
Teradata Hardware
Commodity Hardware
Public Cloud
Teradata Cloud
15
To Summarize
Analytics Landscape is and will remain complex
SQL and No-SQL will remain next to and combined with each other
Artificial Intelligence is not a hype but operationalization is a must
An “everywhere” Analytics Platform that integrates newest tools,
engines, languages and storage systems is the key to success!
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