Dale Wickizer<br />Chief Technology Officer<br />U.S. Public Sector<br />The “Tech”tonic Shift<br />
World Data Explosion <br />2<br />Growth Over the Next Decade:<br />Servers (Phys/VM):  10x<br />Data/Information:      50...
> 90% unstructured data
End user and machine generated</li></ul>Source: Gantz, John and Reinsel, David, “Extracting Value from Chaos”, IDC IVIEW, ...
3<br />The Earth Is Moving Beneath Traditional Applications (“Tech”tonic Shift)<br />High<br />Tier 1 BP<br />OLTP<br />“D...
Integrated Data Protection
Secure Multi-Tenancy</li></ul>DSS/DW<br />Collaboration<br />No SQL<br />Columnar<br />DBs<br />Web Infra<br />App Dev<br ...
Big Data Content</li></ul>IT Infra<br />FMV<br />DVS<br />Content <br />Repositories<br />Tech <br />Comp<br />HPC<br />Ho...
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Dale Wickizer, CTO, NetApp Public Sector

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The “Tech”Tonic Shift

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  • How many people were here for the earthquake we had on August 23 of this year? I was actually downtown hosting a panel discussion at an event where the theme was…I kid you not… “Shake IT up”…you just can’t make this stuff up. :^)Today I want to talk to you about a different “Tech”tonic shift: One that is definitely going to shake up traditional enterprise applications as well as the IT organizations that manage them.
  • That shift isbeing driven by an explosion of data being generated and consumed in the world. Data has grown by a factor of 9 over the past 5 years, crossing 1.2 ZB for the first time! (If anyone wonders what 1.2 ZB is, Wikibon has this great graphic, showing it is the equivalent of 75 billion fully loaded iPads, stacked end-to-end and side-by-side, covering Wembley stadium, in a column more than 4 miles high). This year it will grow to 1.8 ZB. More than 90% of this data was unstructured and much of it machine generated, in response to data stored by end users. Over the next decade, this data growth is expected to accelerate, increasing by a factor of 50. Over the same time, the number of files is expected to increase by more than a factor of 75, which will break many traditional applications and file systems. Sadly, the number of IT professionals will only grow by less than 50% and IT budgets will remain relatively flat!
  • We already see enterprises beginning to adopt High Speed Analytics and and scale out storage technologies to provide new Decision Support methodologies.We see new non-traditional NoSQL and columnar database applications coming online everyday, like MongoDB, Cassandra, ParAccel and Hbase. These applications leverage the fast search and index capabilities of Hadoop. They are able to process workloads 100 times faster than Teradata, Netezza or Oracle Exadata over all types of data (structured and unstructured) at a fraction of the cost. In addition, we see the traditional players scrambling to have their own MapReduce functionality (GreenPlum, Teradata, Netezza, Oracle Exadata), but in reality they are using it only as a glorified ETL front-end. Their legacy structured database still sits behind it all. They don’t yet understand that with all this unstructured data coming in, nightly batch processing to do ETL may be going away. The data will be ingested and stored in its native unstructured format and processed directly from there for decision support.
  • The large volumes of data and metadata are going to drive the need for denser equipment racks, physically, electrically and thermally), placing a great strain to today’s data centers.Fortunately, containerized data centers have come of age and offer an economical and “green” alternative to traditional data centers. They are built to handle these densities, as well as the rigors of transport. Pictured here are some 20’ foot containers built by WWT. Yes, that is a C17 on the right.Some organizations, constrained by law or economics, have begun to invest in this technology and repurpose their old data centers and server rooms as office space, obviating the need to build new data centers and office buildings. Frankly, most organizations should probably give this approach more careful consideration, rather than spending tens of millions of dollars to refit old data centers or build new ones to keep pace with technology.
  • So What? Well there are several trends that Big Data could drive in large enterprises: Higher bandwidth data will result in more, large distributed repositories as network bandwidth lags behind. Tendency will be access data where it is generated and lives, vs moving it to a central location to save time, money and bandwidth. As already mentioned, unstructured data puts a strain on existing enterprise applications. There is already a shift beginning towards disruptive solutions based on NoSQL and MapReduce; distributed repositories put even more strain on the metadata layer. Look for a shakeup in the application vendor space as traditional vendors get kicked to the curb. Similarly the glut of more data will put strain on IT organizations, both from a management and an environmentals standpoint. Dense, object-based storage and new containerized data centers are cost-effective and efficient means that can help.
  • Dale Wickizer, CTO, NetApp Public Sector

    1. 1. Dale Wickizer<br />Chief Technology Officer<br />U.S. Public Sector<br />The “Tech”tonic Shift<br />
    2. 2. World Data Explosion <br />2<br />Growth Over the Next Decade:<br />Servers (Phys/VM): 10x<br />Data/Information: 50x<br />#Files: 75x<br />IT Professionals: <1.5x<br />Source: Revisited: The Rapid Growth in Unstructured Data « Wikibon Blog http://bit.ly/oRSdXm<br /><ul><li>Growing 9x in 5 yrs! (1.8 ZB in 2011)
    3. 3. > 90% unstructured data
    4. 4. End user and machine generated</li></ul>Source: Gantz, John and Reinsel, David, “Extracting Value from Chaos”, IDC IVIEW, June 2011, page 4.<br />
    5. 5. 3<br />The Earth Is Moving Beneath Traditional Applications (“Tech”tonic Shift)<br />High<br />Tier 1 BP<br />OLTP<br />“Decision Support”<br />High Speed <br />Analytics<br />Tier-2 BP <br />OLTP<br />“Enterprise Applications”<br /><ul><li>Shared, Virtualized Infrastructure
    6. 6. Integrated Data Protection
    7. 7. Secure Multi-Tenancy</li></ul>DSS/DW<br />Collaboration<br />No SQL<br />Columnar<br />DBs<br />Web Infra<br />App Dev<br />Data Structure<br />Sat Ground Stations<br />“Big Data”<br /><ul><li>High Bandwidth Throughput
    8. 8. Big Data Content</li></ul>IT Infra<br />FMV<br />DVS<br />Content <br />Repositories<br />Tech <br />Comp<br />HPC<br />Home Dirs<br />Low <br />Performance<br />Small Block, <br />Random I/O (100s KIOPS)<br />Large Block, <br />Sequential I/O<br />100s GB/sec<br />
    9. 9. Big Data Landscape – Industry View<br />4<br />Big<br />Analytics<br />Big<br />Content<br />ParAccel<br />Teradata<br />ExaSol<br />Greenplum<br />Netezza<br />PaaS<br />New Apps<br />StorageGrid<br />Isilon/Atmos,<br />StorNext.<br />Azure<br />GoogleApps<br />VMware<br />HDFS<br />NoSQL<br />FacebookHaystack<br />E-Series<br />Cassandra,<br />Hbase<br />MongoDB<br />ETL<br />Workflow<br />Hadoop<br />Cloudera<br />HortonWorks<br />MapR<br />
    10. 10. “Green” Containerized Data Centers<br />Mobile<br />Resilient (MIL-STD-810f Shock & Vibe)<br />Dense ( > 9 PB, 1000s of cores, > 15KW/Rack)<br />Energy Efficient (PUE ~ 1.2)<br />5<br />NetApp Confidential — Limited Use<br />
    11. 11. Ethernet’s Relentless March<br />6<br />
    12. 12. So What?<br />Impacts of Big Data In Large Enterprises:<br />Increased tendency toward distributed repositories<br />Network bandwidth will lag behind demand<br />Access data where it “lives” vs. from central repository, saving time, bandwidth and money<br />Metadata Management<br />Distributed repositories put more strain on the metadata layer<br />Shift away from traditional database / enterprise applications<br />Shift toward NoSQL / MapReduce based metadata solutions<br />Data Management<br />Shift toward self-describing objects and object-based storage, along with POSIX-compliant & CDMI access, to overcome inode limitations<br />Data Center Challenges<br />Denser (> 1.8 PB), heavier (> 3000 lb), power hungry (> 15 KW) racks<br />Containerized DCs more efficient cost effective than traditional DCs<br />7<br />
    13. 13. Thank You<br />8<br />Dale Wickizer<br />Chief Technology Officer<br />U.S. Public Sector, NetApp, Inc.<br />Dale.Wickizer@netapp.com<br />(703) 864-4805<br />

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