The document discusses the convergence of SQL, NoSQL and Hadoop technologies. It notes that these were originally separated but are now joining together. Analytics problems now span these different platforms, and many platforms now support multiple data workloads and personas. However, challenges remain around common security, federated querying, and workload/query orchestration across platforms. The ideal solution would be a logical hub that can manage policies, security, and workloads across the different data platforms and engines.

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Hadoop, SQL & NoSQL –
No longer an either or
question
Tony Baer
Hadoop Summit 2014
June 4, 2014

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 Where we’ve come – Twins separated at birth & joyous reunion
 Why/how the convergence?
 Loose ends
Agenda

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SQL
RDBMS
File systems
Hierarchical Data stores
OODBMS
SQL, NoSQL, Hadoop
1970s
1980s
1990s
2000s
2010s
Network Data stores

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Early
Development
Commercialization Ecosystem
Formation
1960s 1980s 1990s 2000s
“Prehistoric”
EF Codd
publishes
seminal
RDBMS
model
IBM
System R,
Ingres
DB2,
Oracle,
Teradata,
PC-based
DBMSs
SQL
becomes
de facto
enterprise
standard
data
platform
Tooling
emerges
SQL market
consolidates:
Oracle, DB2,
SQL Server,
Teradata
NewSQL
analytic
platforms
emerge
Mainframe era Midranges &
PCs emerge
Big Data
2014
DBMSs add
multiple
engines
Database timeline
1970s
Client/server &
n-Tier
Ecosystem
Broadens
CODASYL,
IMS
MySQL/
LAMP stack
emerges
J2EE,
.NET

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Early Development Commercialization Ecosystem Formation
2003 - 2005 2009 2011 2012 2013
First
Advanced
SQL
platforms
emerge
Hadoop
emerges
Other
NoSQL
platforms
emerge
Cloudera
intros
comm’l
Hadoop
support
Major
vendors
enter Big
Data
market
Tooling
emerges
2nd wave
NewSQL
platforms
emerge
Big Data Tools
emerge
Internet firm early
adopters
Enterprise early
adopters (FS & Media)
Mainstream
adoption
begins
2014
Big Data
Apps
emerge
Big Data platform timeline
Hortonworks
enters
market
MongoDB,
Cassandra
emerge

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Platform proliferation =
Data processing silos
SQL
RDBMS
NewSQL
RDBMS
NoSQL
Key-Value
NoSQL
JSON
Hadoop
OLTP
(ACID)
OLTP
(Non-
ACID)
BI
Query &
Report
Analytics
OLTP
(Non-
ACID)
Advanced
Analytics
Operational
Decision
Support
Operational
Decision
Support
MapReduce-
based
Advanced
Analytics

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 Where we’ve come – Twins separated at birth & joyous reunion
 Why/how the convergence?
 Loose ends
Agenda

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Analytic SLA requirements vary
Batch Periodic Interactive Real-time
Exploratory
Analytics Standard
reporting
Days/Hours Seconds Split seconds
Interactive
query
Search
Streaming
Decision
Support
Modeling
Operational
Decision
Support
Hours/Minutes

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Analytics problems cross silos –
Operational examples
 Customer engagement
 Interaction – Customer 360 query in DW
 Behavior – Enrich with sentiment analysis on Hadoop
 Engagement – Manage real-time engagement on NoSQL
database
 Risk mitigation
 Baseline – Model party & transactional risk on DW or
Hadoop
 Enrich – Analyze, rank impact of externalities on Hadoop
 Ingest – Real-time market feeds via streaming in-memory
 Define – Decision processes offline via BPM
 Act – Allow/deny credit on system of record

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Architecture –
Common threads
 Aggressive tiering
 Multiple storage engines
 Multiple workload types
 On the horizon:
 Federated query
 Workload/query orchestration
 Loose ends:
 Common security?

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SQL Databases adding multiple personas
 IBM DB2
 BLU architecture adds columnar, data skipping, advanced tiering
 New MongoDB-compliant JSON data store
 Oracle Database 12c
 “In-Memory” option adds DRAM-based columnar, extreme compression
 Microsoft SQL Server
 PDW adds columnar indexing
 PolyBase feature adds Hadoop integration
 Teradata
 Teradata 14.10 adds “Intelligent Memory” data tiering, columnar, Hadoop integration
 Aster 6 adds graph, file store, “SNAP” framework for choreographing SQL, MapReduce, graph
& Hadoop processing
 SAP
 “Smart Data Access” federated query over HANA, Sybase IQ, Teradata & Hadoop

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Hadoop growing beyond MapReduce
 Apache Hadoop 2.0’s new YARN resource allocation framework allows
multiple workloads
 Interactive SQL – lots of flavors
 Spark – The new MapReduce & more…
 Search
 Streaming
 Loose ends:
 Graph ready for prime time?

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Emerging NewSQL + NoSQL databases
 JSON data stores exploding
 Intuitive for representing Internet data
 MongoDB, Couchbase
 IBM, Teradata… potentially Oracle adding JSON
 New transaction stores … not full ACID
 Cassandra for NoSQL (integrated to Hadoop)
 NuoDB, Clustrix, MemSQL & others reinvent OLTP for
distributed Internet apps
 HBase
 DynamoDB, Berkeley DB (Oracle NoSQL database) &
other key-value stores

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A variety of overlapping choices
NewSQL
JSON
Graph
Hadoop
SQL
Deep analytics
Stream
Graph
NoSQL
Account/user profiles
Interactive content
Graph
Machine data
JSON
SQL RDBMS
OLTP
DW
JSON
Distributed OLTP
Fast, deep analytics
Active Archiving
SQLRDBMS
NewSQLRDBMS
NoSQLKey-Value
NoSQLJSON
Hadoop
From To

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A variety of overlapping choices –
But…
Who owns
the logical
hub?
SQL RDBMS NewSQL
Hadoop NoSQL
OLTP
DW
Active Archiving
JSON
Distributed OLTP
Fast, deep analytics
JSON
Graph
SQL
Deep analytics
Stream
Graph
Account/user profiles
Interactive content
Graph
Machine data
JSON

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 Where we’ve come – Twins separated at birth & joyous reunion
 Why/how the convergence?
 Loose ends
Agenda

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Loose ends
 Ideally, policy-based federated
query will be the solution
 Who owns federated query?
 Data platform?
 BI tool?
 Application?
 Who owns workload management?
 Who owns security?
Tug of war between data platforms likely

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Takeaways
 Analytics no longer limited by platform constraints
 Data platforms are taking multiple personas –
 Platform choice is not either/or
 But
 Analytics are no longer silo’ed
 Execution remains silo’ed
 The brass ring will be a logical hub for
 Policy/SLA-based workload targeting & management
 Security & operations/performance management

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Thank you
Tony Baer
Ovum
(646) 546-5330
@TonyBaer
tony.baer@ovum.com