Webinar Future Of Database — What To Expect? Noel Yuhanna, Principal Analyst June 11, 2013. Call in at 12:55 p.m. Eastern time June 11th, 2013. Call in at 12:55 p.m. Eastern time

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

Future: automated upgrade/schema changes On an average, a mission-critical database goes down 44 hours every year for planned outages (99.5 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

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

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

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

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.

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

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.

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!

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.

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.

NoSQL databases are on the move . . . Current adoption of NoSQL is 20%; it is expected to double by 2017. 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.

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.

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.

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.

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

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

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

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

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.

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.

Noel Yuhanna +1 650.581.3807 nyuhanna@forrester.com www.forrester.com