Jean-Pierre Dijcks (Oracle):

The main components in the big data platform provide:

• Deep Analytics — a fully parallel, extensive and extensible toolbox full of advanced and novel statistical and data mining capabilities
• High Agility — the ability to create temporary analytics environments in an end-user driven, yet secure and scalable environment to deliver new and novel insights to the operational business
• Massive Scalability — the ability to scale analytics and sandboxes to previously unknown scales while leveraging previously untapped data potential
• Low Latency — the ability to instantly act based on these advanced analytics in your operational, production environments

If I would be picky, the only thing I’d change would be the order: 1) low latency; 2) massive scalability; 3) high agility; 4) deep analytics.

Original title and link: Big Data: Achieve the Impossible in Real-Time (NoSQL databases © myNoSQL)

via: http://blogs.oracle.com/datawarehousing/entry/big_data_achieve_the_impossible

Philip Russom:

Here are a few reasons:

• Big data yields gigantic statistical samples
• Analytic tools and databases can now handle big data
• There’s a lot to learn from messy data, as long as it’s big
• Big data is a special asset that merits leverage

The last two points simply mean “The Unreasonable Effectiveness of Data“.

Original title and link: Advanced Analytics and Big Data: Why put them together? (NoSQL databases © myNoSQL)

via: http://tdwi.org/blogs/philip-russom/2011/04/the-intersection-of-big-data-and-advanced-analytics.aspx

Mike Minelli: Working with big data can be classified into three basic categories […] One is information management, a second is business intelligence, and the third is advanced analytics

Information management captures and stores the information, BI analyzes data to see what has happened in the past, and advanced analytics is predictive, looking at what the data indicates for the future.

There’s also a list of tools for BigData: AsterData (acquired by Teradata), Datameer, Paraccel, IBM Netezza, Oracle Exadata, EMC Greenplum.

Original title and link: Types of Big Data Work (NoSQL databases © myNoSQL)

via: http://www.linuxinsider.com/story/71945.html

Maybe I’m over-simplifying it, but I’m reading Ketan Karia’s^[1] “11 BigData analytics predictions for 2011” as in:

• hardware will be pushing BigData analytics forward — software will just catch up and follow hardware lead
• 2011 will bring more BigData analytics adoption which also means analytics companies will be more profitable

Here are the original 11 predictions:

1. We’ll hark the chips, not the hardware

   Many companies keep throwing hardware (especially more servers) at the problem, and the chip industry’s enormous investment in computer performance continues to sit idly by.

2. Chip scale-out will date MPP and shrink big data networks.

   […] we are going to have the capability to run 256 or 512 cores on a single chip. If companies can do that, it will shrink the amount of hardware required to fuel the big data networks.

3. Memory will go RAM.

   As the core density of chips and the RAM size keeps rising dramatically, total in-memory data warehouses are now feasible.

4. Chip companies will spend more on R&D in 2011

   It will become more common place for engineers to stay in tune with chip technology advancements as they build out their solutions to manage and leverage huge volumes of business data.

5. Acceleration of analytics will support the agile enterprise

   Analytics technologies will help businesses be more agile and will become a key business differentiator in 2011 and beyond.

6. Businesses will sponsor their own analytical capabilities

7. Analytics gets more embedded into business applications

8. Open source moves to more hybrid models.

   Hybrid models are being used by companies including JasperSoft, SugarCRM, and Ingres (with our VectorWise database).

9. Subscriptions stack up by the hour.

10. Self-service BI gets more attention.

    In 2011, companies will have to accelerate report delivery.

11. Users want to be “in the moment” with data insights

What is the definition of “prediction”? A guess or a bet or an educated thought?

Original title and link: 11 Big-Data Analytics Predictions for 2011 (NoSQL databases © myNoSQL)

via: http://tdwi.org/articles/2011/03/16/Big-Data-Analytics-Predictions.aspx