1. Webinar Future Of Database — What To Expect?
Noel Yuhanna, Principal Analyst
June 11, Call in at 12:55 p.m. Eastern time
June 11th, Call in at 12:55 p.m. Eastern time

2. Business growth and speed are driving new apps and data requirements that are changing the way we store, process, and access data.

3. Agenda
Current drivers and trends impacting database
Future of database — what to expect?
How will future of DBMS affect DBAs and developers?
Recommendations

4. Performance remains the top challenge for the past decade . . . why?

5. Four key trends that are impacting databases
Next- generation apps
Budget issues
Data volume, variety, velocity
Global apps
Database

6. Four key trends that are impacting databases (cont.)
Social network apps Real-time apps LOB apps Big data apps Mobile apps Collaboration
Next- generation apps
Budget issues
Data volume, variety, velocity
Global apps
Database

7. Four key trends that are impacting databases (cont.)
Social network apps Real-time apps LOB apps Big data apps Mobile apps Collaboration
Next- generation apps
Budget issues
Data volume, variety, velocity
Global apps
Database
TBs into TBs Larger EDW Unstructured data Admin challenges Performance issues Scale — unpredictable workload

8. Four key trends that are impacting databases (cont.)
Social network apps Real-time apps LOB apps Big data apps Mobile apps Collaboration
Budget concern remains. Doing more with less Automation is the key. Need for optimized system Subscription model
Next- generation apps
Budget issues
Data volume, variety, velocity
Global apps
Database
TBs into TBs Larger EDW Unstructured data Admin challenges Performance issues Scale — unpredictable workload

9. Four key trends that are impacting databases (cont.)
Social network apps Real-time apps LOB apps Big data apps Mobile apps Collaboration
Budget concern remains. Doing more with less Automation is the key. Need for optimized system Subscription model
Next- generation apps
Budget issues
Data volume, variety, velocity
Global apps
Database
TBs into TBs Larger EDW Unstructured data Admin challenges Performance issues Scale — unpredictable workload
Ensure security. Need for 24x7 availability Deliver high performance. Ensure on-demand scale. Minimize planned downtime.

10. What new DBMS technology to invest to support these trends?
NoSQL
Mobile database
Cloud database
DB appliances
In-memory DB
MPP EDW
Cloud database
Open source DB
NoSQL databases
DB appliances
Next- generation apps
Budget issues
Data volume, variety, velocity
Global apps
Database
Integrate with Hadoop
NoSQL
DB appliances
In-memory DB
MPP EDW
In-memory DB
DB appliances
Cloud databases

11. Database categorization based on function
In-memory database
Enterprise data warehouse
Relational OLTP
Mobile DB
Database appliance
Key-value
Object DB
Graph DB
Document DB
Cloud database
NoSQL (non-relational)
Traditional data sources
CRM
Legacy apps
Public data
Social media
Sensors
Marketplace
New data sources
Geo-location
Relational
Scale-out
relational
Traditional EDW
Column-store EDW
ERP
MPP EDW

12. Top vendors — OLTP — DW — in-memory
Relational OLTP
Actian Ingres
EnterpriseDB
IBM DB2
IBM Informix
MariaDB
Microsoft SQL Server
MySQL*
Oracle DBMS
PostgreSQL
SAP Sybase ASE
VMware vFabric Postgres
Data warehouse
Actian Vectorwise
Amazon Redshift
EMC GreenPlum
HP Vertica
IBM DB2
Kognitio
Microsoft SQL Server
Oracle DBMS
ParAccel
SAP Sybase IQ
Teradata
In-memory database
Aerospike
Altibase XDB
IBM solidDB
MemSQL
Oracle TimesTen
SAP HANA
Starcounter
VoltDB
*Open source projects

13. Top vendors — public cloud and mobile databases
Cloud databases/DBaaS
Amazon DynamoDB
Amazon RDS
Amazon SimpleDB
Caspio
Cloudant
Clustrix
EnterpriseDB Postgres Plus Cloud
Heroku Postgres
Microsoft SQL Database
NuoDB
ObjectRocket MongoDB
Rackspace
salesforce.com Database.com
Mobile databases
HanDBase
IBM Mobile Database
McObject eXtremeDB
Microsoft SQL Server Compact
Oracle Database Mobile
Raima RDM Mobile
SAP Sybase SQL Anywhere
SQLite*
*Open source projects

14. Top vendors — NoSQL Document database 10gen MongoDB Apache CouchDB*
Couchbase*
eXist-db
MarkLogic
MongoDB*
OrientDB*
terrastore
Key-value store
Aerospike
DataStax Cassandra
Amazon DynamoDB
IBM Informix C-ISAM
Amazon SimpleDB
Keyspace
Apache Cassandra
memcached
Basho Riak
Oracle NoSQL
Couchbase*
Redis
Object database
InterSystems Caché
NeoDatis Object Database
ObjectDB
Objectivity Objectivity/DB
ObjectStore
Versant db4o Object Database
Versant Object Database
VMware GemStone/S
Graph database
AllegroGraph
FLockDB*
GraphBase
IBM DB2
Neo Technology Neo4j
Neo4j*
Objectivity InfiniteGraph
OrientDB*
*Open source projects

15. Top vendors — scale-out RDBMS and appliances
Scale-out relational
Clustrix
MemSQL
ScaleArc
ScaleBase
ScaleDB
StormDB
TransLattice
VMware vFabric SQLFire
VoltDB
Database appliances
Oracle Exadata
EMC Greenplum
HP Vertica
IBM PureData System
Microsoft PDW
SAP HANA Appliance
Teradata

16. Oracle, Microsoft, IBM, and Teradata dominate the data warehouse space . . .

17. Oracle and Microsoft dominate the OLTP market
Oracle and Microsoft dominate the OLTP market with MongoDB and Cassandra adoption picking up momentum . . .

18. Agenda
Current drivers and trends impacting database
Future of database — what to expect?
How will future of DBMS affect DBAs and developers?
Recommendations

19. Future of database — what to expect?
Automation
Availability
In-memory
Manageability
Appliances
Agile model
DBMS
Intelligence
Scale
Cloud

20. Evolution of database technology
Distributed in-memory DB, appliances, and intelligence
Intelligence and scale
Flexibility
Document
Graph
Key-value
store
Specialization
XML
DBMS
Object
DBMS
Embedded
DBMS
Object- oriented
DBMS
Mainstream
Object
DBMS
Evolution
Mobile
DBMS
Relational
DBMS
Discovery
Network
DBMS
Hierarchical
DBMS
Files
60s
70s
80s
90s
00s
10s
20s
Time

21. Future: DBMS automation already a top focus for most leading DBMS provider . . .
Performance and tuning
Tuning SQL statements at runtime — automatically
Support mixed workloads — OLTP/DW or BI seamlessly.
Backup and recovery
Track backups and recover databases — automatically.
Upgrades and patch deployments
Seamless and integrated — no downtime
Other administration tasks
Self-securing, self-healing, and continuous DB availability
Tighter integration of structured and unstructured data

22. Future: automated upgrade/schema changes
On an average, a mission-critical database goes down 44 hours every year for planned outages ( availability).
Upgrades, patch deployment, migrations, application upgrades, schema changes, hardware maintenance . . .
Some automation already seen — online indexes, online reorganization, online parameters . . .
DBMS are becoming true 24x7 — no shutdown required.
No shutdown for any upgrades — database or hardware . . .
No shutdown for patch deployment — security or bug fixes . . .
No shutdown for schema changes . . .
No shutdown for maintenance

23. Future: Database administration challenges are shifting
1990
1995
2000
2005
Performance and tuning
Patch/upgrade
Security
2010
2015
Challenging
Less
More
HA and DR
2020

24. Database-to-DBA ratio is increasing
2010
2005
2000
1995
1990 30 10 40 50
Average number of databases/instances 20
2015
2020 60 70 80 90
100
45 DB-to-DBA
Capped at 12TB
Source: Forrester estimate

25. Future: self-securing intelligent database
“Current DBMS technology is not intelligent to differentiate a hacker from a user.”
Select * from master
Suspicious activity?
DBMS
User logs in over a weekend for the first time in two years — suspicious?
Will secure itself
Intercept hackers in real-time.
Alert DBAs/admin.
Becomes data-aware

26. Falling memory prices and new in-memory technologies offer new possibilities . . .
Distributed in-memory
Falling memory prices
Memory prices that were 100K/GB in 1990 were down to $5/GB in 2012.
Data stored in cache/memory can be accessed 20x to 50x faster than disk.
In-memory across distributed cluster delivers powerful horizontal scale.

27. What’s new with distributed in-memory?
Traditional DB
Distributed in-memory DB
Memory
Persistence
Distributed in-memory
DBMS
Servers
Rows or cols
Rows or cols
Scales to 100s TB  into PB
Rows or cols
Disk block

28. In-memory database delivers faster actionable results and transactions . . .
Faster insights
Advanced analytics
Real time
Transactions
Persistence
In-memory database
(horizontal scale)
CRM
Social media
Clickstream
Big data
Logs
By 2017, 40% of enterprises will be running in-memory applications it should be part of your DBMS strategy.

29. Database appliances are here to stay . . .
Integrated system
High performance
Scalable
Single vendor
Automated
Improved SLAs
Lower cost
Self-service
Oracle HP
IBM
Teradata
23% of enterprises are using appliances today, and this will double in three years!

30. Hadoop-HBase has lots of potential . . .
Leverages an extensible framework for building advanced analytics and new data management capabilities. It is flexible, economical, open source, and scalable, and it offers distributed processing.
Hadoop is deployed by 20% of enterprises with another 33% likely to deploy over the next three years!
HBase
Apache HBase is an open source, distributed, column-oriented store modeled after Google’s Bigtable. HBase provides Bigtable-like capabilities on top of Hadoop and HDFS. It supports random, real-time read/write access to big data.
HBase adoption is around 5% but is likely to double in the next two years.

31. Public cloud databases are viable to support mission-critical applications . . .
Benefits:
On-demand scale
Automated
Easy to provision
Cost effective
Better availability
Challenges:
Security
Latency
SLAs
Automated scale
Use case:
Mobile applications: analytics, social, SaaS, eCommerce, data services apps . . .
Departmental apps: LOB apps, small group/community apps, collaboration . . .
Application dev and testing: provision new instance, app testing and dev
SMB applications
Database backup and archive
Note: Adoption of cloud database is around 20%; it will double over the next three years.

32. NoSQL databases are on the move . . .
Current adoption of NoSQL is 20%; it is expected to double by Most of the adoption is POC and test environment (80%).
Forrester estimates the current NoSQL market size to be $200 million, which includes software licenses, support, and consulting services; and this will likely grow to $1 billion by 2017.
NoSQL offers choices to support new types of apps — social networking, real-time analytics, collaboration, and others.
NoSQL can be broken down into the following four key categories:
Graph database.
Document database.
Key-value database.
Object database.

33. Graph databases speed up access to connected data . . .
Graph databases simplify and speed up access to data containing many relationships. Graph structures consist of nodes (things), edges (relationships), and properties (key values) to store and access complex data relationships.
Unlike key-value stores, graph databases directly support relationships and can rapidly access complex networks of connected data.
Common use cases include social network applications like Facebook, Twitter, and LinkedIn; recommendation engines; pattern analysis for detecting fraud and understanding consumer behavior; analysis of communication networks for load balancing and routing; and predictive analytics.
Neo4j is one of the more popular graph databases; several others are available including AllegroGraph, FlockDB, GraphBase, IBM DB2 NoSQL Graph Store, Objectivity InfiniteGraph, and OrientDB.

34. Document database offers a flexible data model
Document database stores each row as a document offering the flexibility to have any number of columns and fields of any size and type.
The structure or schema of each document is as flexible as the application requires and can evolve rapidly to meet new requirements.
Although a relational store can also handle such information, document databases are better suited to handling such requirements especially when the app requires a flexible schema structure and support for unpredictable relationships between data elements.
The best-known solutions are Apache CouchDB, Couchbase, eXist- dbx, MarkLogic Server, MongoDB, OrientDB, and terrastore.

35. Key-value provide fast access to distributed data
NoSQL key-value store databases can handle web scale — thousands of servers, millions of users — with extremely fast, optimized storage and retrieval.
Key-value stores accomplish this by leaving out many features of relational databases and implementing only features that extreme web apps need.
The best-known solutions in this space are Aerospike, Amazon DynamoDB, Amazon SimpleDB, Apache Cassandra, Basho Riak, Couchbase, DataStax Cassandra, IBM Informix C-ISAM, Keyspace, memcached, Oracle NoSQL, and Redis.

36. Agenda
Current drivers and trends impacting database
Future of database — what to expect?
How will future of DBMS affect DBAs and developers?
Recommendations

37. How will the DBA role change in the coming years?
Support more than just RDBMS — NoSQL — graph databases, document databases, key-value databases, etc.
Support database machine/appliances — OLTP, OLAP, data warehouses — first-tier support.
Understanding the hardware, storage, server — infrastructure requirements and implementation strategy
Managing hundreds of databases using common policies, tools, and frameworks
Utilizing latest virtualization, private and public cloud platforms
Leveraging in-memory databases where required
Working with developers and architects to help determine the right DBMS for applications

38. How will the developers role change from a database perspective?
Understanding of non-RDBMS such as NOSQL — graph, key-value, object, document databases
Choosing right DBMS — but ensuring standards — data models, policies, and governance
Working with DBAs in choosing the right DBMS platform
Leveraging in-memory and cloud databases where necessary

39. Agenda
Current drivers and trends impacting database
Future of database — what to expect?
How will future of DBMS affect DBAs and developers?
Recommendations

40. What to expect?
Disk-based databases will eventually be replaced by in- memory databases and SSDs (disk will become like tapes).
In-memory databases will become the standard platform for transactions and analytics going forward.
50% of all new app development will be on NoSQL by 2016.
Cloud database adoption will double in the next three years.
Appliances are here to stay — adoption will grow.
Hadoop-HBase innovation will further continue — increase in adoption.
Distributed data warehouse/data mart adoption will grow.

41. Recommendations
RDBMS can handle multiple terabytes of data (40TB for OLTP, 250TB for DW); for more, look beyond relational, appliances, columnar data warehouses, or in-memory technologies.
Look at Hadoop and HBase to process very large amounts of data — especially around unstructured.
Automate database administration activities to improve productivity and lower cost. New DBMS version improve automation.
Look at NoSQL to support new application requirements — social network, real-time apps, analytics, mobile apps, etc.
Database appliances should be part of your DBMS strategy.
Invest in in-memory database — competitive advantage.
To lower cost, look at cloud database, database consolidation, clustering, standardization of DBMS, and automation.

42. Noel Yuhanna