High Performance Analytics

Marry Big Data Analytics to High Performance Computing, and you get the buzzword of this season- High Performance Analytics.

It basically consists of Parallelized code to run in parallel on custom hardware, in -database analytics for speed, and cloud computing /high performance computing environments. On an operational level, it consists of software (as in analytics) partnering with software (as in databases, Map reduce, Hadoop) plus some hardware (HP or IBM mostly). It is considered a high margin , highly profitable, business with small number of deals compared to say desktop licenses.

As per HPC Wire- which is a great tool/newsletter to keep updated on HPC , SAS Institute has been busy on this front partnering with EMC Greenplum and TeraData (who also acquired  SAS Partner AsterData to gain a much needed foot in the MR/SQL space) Continue reading “High Performance Analytics”

AsterData partners with Tableau

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Tableau which has been making waves recntly with its great new data visualization tool announced a partner with my old friends at AsterData. Its really cool piece of data vis and very very fast on the desktop- so I can imagine what speed it can help with AsterData’s MPP Row and Column Zingbang AND Parallel Analytical Functions

Tableau and AsterData also share the common Stanfordian connection (but it seems software is divided quite equally between Stanford, Hardvard Dropouts and North Carolina )

It remains to be seen in this announcement how much each company  can leverage the partnership or whether it turns like the SAS Institute- AsterData partnership last year or whether it is just to announce connectors in their software to talk to each other.

See a Tableau vis at


AsterData remains the guys with the potential but I would be wrong to say MapReduceSQL is as hot in December 2010 as it was in June 2009- and the elephant in the room would be Hadoop. That and Google’s continued shyness from encashing its principal comptency of handling Big Data (but hush – I signed a NDA with the Google Prediction API– so things maaaay change very rapidly on ahem that cloud)

Disclaimer- AsterData was my internship sponsor during my winter training while at Univ of  Tenn.


Interesting Interview with Quentin G,AsterData

Here is an interesting interview with Quentin G, CEO AsterData, Marketing trumpeting aside apart-the insights on the whats next vision thing are quite good.


As you look down the road, what are the three major challenges you see for vendors who keep trying to solve big data and other “now” problems with old tools?

Old tools and traditional architectures cannot scale effectively to handle massive data volumes that reach 100’s of terabytes nor can they effectively process large data volumes in a high performance manner. Further, they are restricted to what SQL querying allows. The three challenges I have noted are:

First, performance, specifically, poor performance on large data volumes and heavy workloads: The pre-existing systems rely on storing data in a traditional DBMS or data warehouse and then extracting a sample of data to a separate processing tier. This greatly restricts data insights and analytics as only a sample of data is analyzed and understood.  As more data is stored in these systems they suffer from performance degradation as more users try to access the system concurrently. Additionally moving masses of data out of the traditional DBMS to a separate processing tier adds latency and slows down analytics and response times. This pre-existing architecture greatly limits performance especially as data sizes grow.

Second, limited analytics: Pre-existing systems rely mostly on SQL for data querying and analysis. SQL poses several limitations and is not suited for ad hoc querying, deep data exploration and a range of other analytics. MapReduce overcomes the limitations of SQL and SQL-MapReduce in particular opens up a new class of analytics that cannot be achieved with SQL alone.

And, third, limitations of types of data that can be stored and analyzed: Traditional systems are not designed for non-relational or unstructured data. New solutions such as Aster Data’s are designed from the ground up to handle both relational and non-relational data. Organizations want to store and process a range of data types and do this in a single platform. New solutions allow for different data types to be handled in a single platform whereas pre-existing architectures and solutions are specialized around a single data type or format – this restricts the diversity of analytics that can be performed on these systems.

Read the whole interview at –http://www.arnoldit.com/search-wizards-speak/aster-data.html

Speaking of which- there is a new webinar by Merv Adrian (interview on Decisionstats) and Colin White-



and from the famous AOL website- a profile of AsterData’s money flow which kind of hints at an IPO two years onwards-


AsterData releases nCluster 4.6

From the press release

Aster Data nCluster 4.6, which includes a column data store, making Aster Data nCluster 4.6 the first platform with a unified SQL-MapReduce analytic framework on a hybrid row and column massively parallel processing (MPP) database management system (DBMS). The unified SQL-MapReduce analytic framework and Aster Data’s suite of 1000+ MapReduce-ready analytic functions, delivers a substantial breakthrough in richer, high performance analytics on large data volumes where data can be stored in either a row or column format.

With Aster Data nCluster 4.6, customers can choose the data format best suited to their needs and benefit from the power of Aster Data’s SQL-MapReduce analytic capabilities, providing maximum query performance by leveraging row-only, column-only, or hybrid storage strategies. Aster Data makes selection of the appropriate storage strategy easy with the new Data Model Express tool that determines the optimal data model based on a customer’s query workloads.  Both row and column stores in Aster Data nCluster 4.6 benefit from platform-level services including Online Precision Scaling™ on commodity hardware, dynamic workload management, and always-on availability, all of which now operate on both row and column stores. All 1000+ MapReduce-ready analytic functions released previously through Aster Data Analytic Foundation — a powerful suite of pre-built MapReduce analytic software building blocks — now run on a hybrid row and column architecture.  Aster Data nCluster 4.6 also includes new pre-built analytic functions, including decision trees and histograms. For custom analytic application development, the Aster Data IDE, Aster Data Developer Express, also fully and seamlessly supports the hybrid row and column store in Aster DatanCluster 4.6.

More advanced analytics infrastructure.