Tag: sql

AsterData gets $30 mill in funding

From the press release, the maker of Map Reduce based BI software gets 30 mill $ as Series C funding. Given the valuation recently by IBM to Netezza, AsterData seems set to cross the Billion Dollar valuation within the next 18-24 months IMO

Aster Data Closes $30 Million Series C Financing

Explosive Growth and Market Leadership Attracts New and Existing Investors

San Carlos, CA – September 22, 2010 – Aster Data, a market leader in big data management and advanced analytics, today announced that it has closed a $30 million Series C round of financing led by both new and existing investors. The company will use the new funding to accelerate growth, scale operations, and expand its global market share in the $20 billion database market – a market that is experiencing rapid growth as a result of both the explosion in data volumes across organizations and the urgent need to deliver a new class of analytics and data-driven applications. The Series C round of funding includes previous investors Sequoia Capital, JAFCO Ventures, Institutional Venture Partners, Cambrian Ventures, as well as an additional new strategic investor.  Also investing in this round is early investor David Cheriton, who previously backed high-growth companies including Google and VMware, and co-founded several successful technology companies.

Today’s Series C funding announcement underscores a year of strong innovation, execution, and overall momentum for the analytic database company. Key milestones include:

Strong sales growth: Since 2008, Aster Data has doubled revenue year-over-year and secured key customers that leverage Aster Data’s platform to address the big data management problem including MySpace, comScore, Barnes & Noble, and Akamai. Like so many organizations today,
Aster Data’s customers are experiencing explosive data growth across their organizations and recognize the need for rich, advanced analytics that give them deeper insights from their data.

Key executive hires: Quentin Gallivan, former CEO of both PivotLink and Postini and EVP of worldwide sales at Verisign, recently joined the company as Chief Executive Officer. In addition, earlier this year, John Calonico, previously at Interwoven, BEA, and Autodesk, joined as Chief Financial Officer; and Nitin Donde, formerly an executive at EMC and 3PAR, joined as Executive Vice President Engineering.  The strength and experience of Aster Data’s management team helps further establish a strong operational foundation for growth in 2010 and beyond.

Industry recognition: Aster Data was positioned in the “Visionaries” Quadrant of Gartner, Inc.’s

Data Warehouse Database Management Systems Magic Quadrant, published 2010 *; was recently named 2011 Tech Pioneer by the World Economic Forum; was named “Company to Watch” in the Information Management category of TechWeb’s Intelligent Enterprise 2010 Editors’ Choice Awards; and was awarded the 2010 San Francisco Business Times Technology and Innovation Award in the Best Product and Services Category.

Product Innovation: Aster Data continues to deliver ground-breaking capabilities to address the big data management and advanced analytics market need. Its recent announcement of
Aster Data nCluster 4.6 includes a column data store, making it the first hybrid row and column MPP DBMS with a unified SQL and MapReduce analytic framework for advanced analytics on large data sets. This year, Aster Data also delivered the most extensive library of pre-packaged MapReduce analytics totaling over 1000 functions, to ease and accelerate delivery of highly advanced analytic applications.

Aster Data’s analytic database, also called a ‘Data-Analytics Server’ is specifically designed to enable organizations to cost effectively store and analyze massive volumes of data. Aster Data leverages the power of commodity, general-purpose hardware, to reduce the cost to scale to support large data volumes and uniquely allows analysis of all data ‘in-database’ enabling richer and faster processing of large data sets. Aster Data’s in-database analytics engine uses the power of MapReduce, a parallel processing framework created by Google.

”The funding we received in our Series C round is a strong endorsement of Aster Data’s market leadership position and the high growth potential of the big data market,” said Quentin Gallivan, Chief Executive Officer, Aster Data. “The Aster Data team has executed exceptionally well to-date and I am excited to have the resources to accelerate the growth of the company as we expand our operations and execute aggressively across all fronts.”

Windows Azure vs Amazon EC2 (and Google Storage)

Here is a comparison of Windows Azure instances vs Amazon compute instances

Compute Instance Sizes:

Developers have the ability to choose the size of VMs to run their application based on the applications resource requirements. Windows Azure compute instances come in four unique sizes to enable complex applications and workloads.

Compute Instance Size CPU Memory Instance Storage I/O Performance
Small 1.6 GHz 1.75 GB 225 GB Moderate
Medium 2 x 1.6 GHz 3.5 GB 490 GB High
Large 4 x 1.6 GHz 7 GB 1,000 GB High
Extra large 8 x 1.6 GHz 14 GB 2,040 GB High

Standard Rates:

Windows Azure

  • Compute
    • Small instance (default): $0.12 per hour
    • Medium instance: $0.24 per hour
    • Large instance: $0.48 per hour
    • Extra large instance: $0.96 per hour
  • Storage
    • $0.15 per GB stored per month
    • $0.01 per 10,000 storage transactions
  • Content Delivery Network (CDN)
    • $0.15 per GB for data transfers from European and North American locations*
    • $0.20 per GB for data transfers from other locations*
    • $0.01 per 10,000 transactions*

Source –

http://www.microsoft.com/windowsazure/offers/popup/popup.aspx?lang=en&locale=en-US&offer=MS-AZR-0001P

and

http://www.microsoft.com/windowsazure/windowsazure/

Amazon EC2 has more options though——————————-

http://aws.amazon.com/ec2/pricing/

Standard On-Demand Instances Linux/UNIX Usage Windows Usage
Small (Default) $0.085 per hour $0.12 per hour
Large $0.34 per hour $0.48 per hour
Extra Large $0.68 per hour $0.96 per hour
Micro On-Demand Instances Linux/UNIX Usage Windows Usage
Micro $0.02 per hour $0.03 per hour
High-Memory On-Demand Instances
Extra Large $0.50 per hour $0.62 per hour
Double Extra Large $1.00 per hour $1.24 per hour
Quadruple Extra Large $2.00 per hour $2.48 per hour
High-CPU On-Demand Instances
Medium $0.17 per hour $0.29 per hour
Extra Large $0.68 per hour $1.16 per hour
Cluster Compute Instances
Quadruple Extra Large $1.60 per hour N/A*
* Windows is not currently available for Cluster Compute Instances.

http://aws.amazon.com/ec2/instance-types/

Standard Instances

Instances of this family are well suited for most applications.

Small Instance – default*

1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage (150 GB plus 10 GB root partition)
32-bit platform
I/O Performance: Moderate
API name: m1.small

Large Instance

7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage (2×420 GB plus 10 GB root partition)
64-bit platform
I/O Performance: High
API name: m1.large

Extra Large Instance

15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage (4×420 GB plus 10 GB root partition)
64-bit platform
I/O Performance: High
API name: m1.xlarge

Micro Instances

Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPUcapacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.

Micro Instance

613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro

High-Memory Instances

Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.

High-Memory Extra Large Instance

17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge

High-Memory Double Extra Large Instance

34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge

High-Memory Quadruple Extra Large Instance

68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge

High-CPU Instances

Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.

High-CPU Medium Instance

1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium

High-CPU Extra Large Instance

7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge

Cluster Compute Instances

Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.

Cluster Compute Quadruple Extra Large Instance

23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge

Also http://www.microsoft.com/en-us/sqlazure/default.aspx

offers SQL Databases as a service with a free trial offer

If you are into .Net /SQL big time or too dependent on MS, Azure is a nice option to EC2 http://www.microsoft.com/windowsazure/offers/popup/popup.aspx?lang=en&locale=en-US&offer=COMPARE_PUBLIC

Updated- I just got approved for Google Storage so am adding their info- though they are in Preview (and its free right now) 🙂

https://code.google.com/apis/storage/docs/overview.html

Functionality

Google Storage for Developers offers a rich set of features and capabilities:

Basic Operations

  • Store and access data from anywhere on the Internet.
  • Range-gets for large objects.
  • Manage metadata.

Security and Sharing

  • User authentication using secret keys or Google account.
  • Authenticated downloads from a web browser for Google account holders.
  • Secure access using SSL.
  • Easy, powerful sharing and collaboration via ACLs for individuals and groups.

Performance and scalability

  • Up to 100 gigabytes per object and 1,000 buckets per account during the preview.
  • Strong data consistency—read-after-write consistency for all upload and delete operations.
  • Namespace for your domain—only you can create bucket URIs containing your domain name.
  • Data replicated in multiple data centers across the U.S. and within the same data center.

Tools

  • Web-based storage manager.
  • GSUtil, an open source command line tool.
  • Compatible with many existing cloud storage tools and libraries.

Read the Getting Started Guide to learn more about the service.

Note: Google Storage for Developers does not support Google Apps accounts that use your company domain name at this time.

Back to top

Pricing

Google Storage for Developers pricing is based on usage.

  • Storage—$0.17/gigabyte/month
  • Network
    • Upload data to Google
      • $0.10/gigabyte
    • Download data from Google
      • $0.15/gigabyte for Americas and EMEA
      • $0.30/gigabyte for Asia-Pacific
  • Requests
    • PUT, POST, LIST—$0.01 per 1,000 requests
    • GET, HEAD—$0.01 per 10,000 requests

KXEN Update

Update from a very good data mining software company, KXEN –

  1. Longtime Chairman and founder Roger Haddad is retiring but would be a Board Member. See his interview with Decisionstats here https://decisionstats.wordpress.com/2009/01/05/interview-roger-haddad-founder-of-kxen-automated-modeling-software/ (note images were hidden due to migration from .com to .wordpress.com )
  2. New Members of Leadership are as-
John Ball, CEOJohn Ball
Chief Executive Officer

John Ball brings 20 years of experience in enterprise software, deep expertise in business intelligence and CRM applications, and a proven track record of success driving rapid growth at highly innovative companies.

Prior to joining KXEN, Mr. Ball served in several executive roles at salesforce.com, the leading provider of SaaS applications. Most recently, John served as VP & General Manager, Analytics and Reporting Products, where he spearheaded salesforce.com’s foray into CRM analytics and business intelligence. John also served as VP & General Manager, Service and Support Applications at salesforce.com, where he successfully grew the business to become the second largest and fastest growing product line at salesforce.com. Before salesforce.com, Ball was founder and CEO of Netonomy, the leading provider of customer self-service solutions for the telecommunications industry. Ball also held a number of executive roles at Business Objects, including General Manager, Web Products, where delivered to market the first 3 versions of WebIntelligence. Ball has a master’s degree in electrical engineering from Georgia Tech and a master’s degree in electric

I hope John atleast helps build a KXEN Force.com application- there are only 2 data mining apps there on App Exchange. Also on the wish list  more social media presence, a Web SaaS/Amazon API for KXEN, greater presence in American/Asian conferences, and a solution for SME’s (which cannot afford the premium pricing of the flagship solution. An alliance with bigger BI vendors like Oracle, SAP or IBM  for selling the great social network analysis.

Bill Russell as Non Executive Chairman-

Bill Russell as Non-executive Chairman of the Board, effective July 16 2010. Russell has 30 years of operational experience in enterprise software, with a special focus on business intelligence, analytics, and databases.Russell held a number of senior-level positions in his more than 20 years at Hewlett-Packard, including Vice President and General Manager of the multi-billion dollar Enterprise Systems Group. He has served as Non-executive Chairman of the Board for Sylantro Systems Corporation, webMethods Inc., and Network Physics, Inc. and has served as a board director for Cognos Inc. In addition to KXEN, Russell currently serves on the boards of Saba, PROS Holdings Inc., Global 360, ParAccel Inc., and B.T. Mancini Company.

Xavier Haffreingue as senior vice president, worldwide professional services and solutions.
He has almost 20 years of international enterprise software experience gained in the CRM, BI, Web and database sectors. Haffreingue joins KXEN from software provider Axway where he was VP global support operations. Prior to Axway, he held various leadership roles in the software industry, including VP self service solutions at Comverse Technologies and VP professional services and support at Netonomy, where he successfully delivered multi-million dollar projects across Europe, Asia-Pacific and Africa. Before that he was with Business Objects and Sybase, where he ran support and services in southern Europe managing over 2,500 customers in more than 20 countries.

David Guercio  as senior vice president, Americas field operations. Guercio brings to the role more than 25 years experience of building and managing high-achieving sales teams in the data mining, business intelligence and CRM markets. Guercio comes to KXEN from product lifecycle management vendor Centric Software, where he was EVP sales and client services. Prior to Centric, he was SVP worldwide sales and client services at Inxight Software, where he was also Chairman and CEO of the company’s Federal Systems Group, a subsidiary of Inxight that saw success in the US Federal Government intelligence market. The success in sales growth and penetration into the federal government led to the acquisition of Inxight by Business Objects in 2007, where Guercio then led the Inxight sales organization until Business Objects was acquired by SAP. Guercio was also a key member of the management team and a co-founder at Neovista, an early pioneer in data mining and predictive analytics. Additionally, he held the positions of director of sales and VP of professional services at Metaphor Computer Systems, one of the first data extraction solutions companies, which was acquired by IBM. During his career, Guercio also held executive positions at Resonate and SiGen.

3) Venture Capital funding to fund expansion-

It has closed $8 million in series D funding to further accelerate its growth and international expansion. The round was led by NextStage and included participation from existing investors XAnge Capital, Sofinnova Ventures, Saints Capital and Motorola Ventures.

This was done after John Ball had joined as CEO.

4) Continued kudos from analysts and customers for it’s technical excellence.

KXEN was named a leader in predictive analytics and data mining by Forrester Research (1) and was rated highest for commercial deployments of social network analytics by Frost & Sullivan (2)

Also it became an alliance partner of Accenture- which is also a prominent SAS partner as well.

In Database Optimization-

In KXEN V5.1, a new data manipulation module (ADM) is provided in conjunction with scoring to optimize database workloads and provide full in-database model deployment. Some leading data mining vendors are only now beginning to offer this kind of functionality, and then with only one or two selected databases, giving KXEN a more than five-year head start. Some other vendors are only offering generic SQL generation, not optimized for each database, and do not provide the wealth of possible outputs for their scoring equations: For example, real operational applications require not only to generate scores, but decision probabilities, error bars, individual input contributions – used to derive reasons of decision and more, which are available in KXEN in-database scoring modules.

Since 2005, KXEN has leveraged databases as the data manipulation engine for analytical dataset generation. In 2008, the ADM (Analytical Data Management) module delivered a major enhancement by providing a very easy to use data manipulation environment with unmatched productivity and efficiency. ADM works as a generator of optimized database-specific SQL code and comes with an integrated layer for the management of meta-data for analytics.

KXEN Modeling Factory- (similar to SAS’s recent product Rapid Predictive Modeler http://www.sas.com/resources/product-brief/rapid-predictive-modeler-brief.pdf and http://jtonedm.com/2010/09/02/first-look-rapid-predictive-modeler/)

KXEN Modeling Factory (KMF) has been designed to automate the development and maintenance of predictive analytics-intensive systems, especially systems that include large numbers of models, vast amounts of data or require frequent model refreshes. Information about each project and model is monitored and disseminated to ensure complete management and oversight and to facilitate continual improvement in business performance.

Main Functions

Schedule: creation of the Analytic Data Set (ADS), setup of how and when to score, setup of when and how to perform model retraining and refreshes …

Report: Monitormodel execution over time, Track changes in model quality over time, see how useful one variable is by considering its multiple instance in models …

Notification: Rather than having to wade through pages of event logs, KMF Department allows users to manage by exception through notifications.

Other products from KXEN have been covered here before https://decisionstats.wordpress.com/tag/kxen/ , including Structural Risk Minimization- https://decisionstats.wordpress.com/2009/04/27/kxen-automated-regression-modeling/

Thats all for the KXEN update- all the best to the new management team and a splendid job done by Roger Haddad in creating what is France and Europe’s best known data mining company.

Note- Source – http://www.kxen.com


Interview Stephanie McReynolds Director Product Marketing, AsterData

Here is an interview with Stephanie McReynolds who works as as Director of Product Marketing with AsterData. I asked her a couple of questions about the new product releases from AsterData in analytics and MapReduce.

Ajay – How does the new Eclipse Plugin help people who are already working with huge datasets but are new to AsterData’s platform?

Stephanie- Aster Data Developer Express, our new SQL-MapReduce development plug-in for Eclipse, makes MapReduce applications easy to develop. With Aster Data Developer Express, developers can develop, test and deploy a complete SQL-MapReduce application in under an hour. This is a significant increase in productivity over the traditional analytic application development process for Big Data applications, which requires significant time coding applications in low-level code and testing applications on sample data.

Ajay – What are the various analytical functions that are introduced by you recently- list say the top 10.

Stephanie- At Aster Data, we have an intense focus on making the development process easier for SQL-MapReduce applications. Aster Developer Express is a part of this initiative, as is the release of pre-defined analytic functions. We recently launched both a suite of analytic modules and a partnership program dedicated to delivering pre-defined analytic functions for the Aster Data nCluster platform. Pre-defined analytic functions delivered by Aster Data’s engineering team are delivered as modules within the Aster Data Analytic Foundation offering and include analytics in the areas of pattern matching, clustering, statistics, and text analysis– just to name a few areas. Partners like Fuzzy Logix and Cobi Systems are extending this library by delivering industry-focused analytics like Monte Carlo Simulations for Financial Services and geospatial analytics for Public Sector– to give you a few examples.

Ajay – So okay I want to do a K Means Cluster on say a million rows (and say 200 columns) using the Aster method. How do I go about it using the new plug-in as well as your product.

Stephanie- The power of the Aster Data environment for analytic application development is in SQL-MapReduce. SQL is a powerful analytic query standard because it is a declarative language. MapReduce is a powerful programming framework because it can support high performance parallel processing of Big Data and extreme expressiveness, by supporting a wide variety of programming languages, including Java, C/C#/C++, .Net, Python, etc. Aster Data has taken the performance and expressiveness of MapReduce and combined it with the familiar declarativeness of SQL. This unique combination ensures that anyone who knows standard SQL can access advanced analytic functions programmed for Big Data analysis using MapReduce techniques.

kMeans is a good example of an analytic function that we pre-package for developers as part of the Aster Data Analytic Foundation. What does that mean? It means that the MapReduce portion of the development cycle has been completed for you. Each pre-packaged Aster Data function can be called using standard SQL, and executes the defined analytic in a fully parallelized manner in the Aster Data database using MapReduce techniques. The result? High performance analytics with the expressiveness of low-level languages accessed through declarative SQL.

Ajay – I see an an increasing focus on Analytics. Is this part of your product strategy and how do you see yourself competing with pure analytics vendors.

Stephanie – Aster Data is an infrastructure provider. Our core product is a massively parallel processing database called nCluster that performs at or beyond the capabilities of any other analytic database in the market today. We developed our analytics strategy as a response to demand from our customers who were looking beyond the price/performance wars being fought today and wanted support for richer analytics from their database provider. Aster Data analytics are delivered in nCluster to enable analytic applications that are not possible in more traditional database architectures.

Ajay – Name some recent case studies in Analytics of implementation of MR-SQL with Analytical functions

Stephanie – There are three new classes of applications that Aster Data Express and Aster Analytic Foundation support: iterative analytics, prediction and optimization, and ad hoc analysis.

Aster Data customers are uncovering critical business patterns in Big Data by performing hypothesis-driven, iterative analytics. They are exploring interactively massive volumes of data—terabytes to petabytes—in a top-down deductive manner. ComScore, an Aster Data customer that performs website experience analysis is a good example of an Aster Data customer performing this type of analysis.

Other Aster Data customers are building applications for prediction and optimization that discover trends, patterns, and outliers in data sets. Examples of these types of applications are propensity to churn in telecommunications, proactive product and service recommendations in retail, and pricing and retention strategies in financial services. Full Tilt Poker, who is using Aster Data for fraud prevention is a good example of a customer in this space.

The final class of application that I would like to highlight is ad hoc analysis. Examples of ad hoc analysis that can be performed includes social network analysis, advanced click stream analysis, graph analysis, cluster analysis and a wide variety of mathematical, trigonometry, and statistical functions. LinkedIn, whose analysts and data scientists have access to all of their customer data in Aster Data are a good example of a customer using the system in this manner.

While Aster Data customers are using nCluster in a number of other ways, these three new classes of applications are areas in which we are seeing particularly innovative application development.

Biography-

Stephanie McReynolds is Director of Product Marketing at Aster Data, where she is an evangelist for Aster Data’s massively parallel data-analytics server product. Stephanie has over a decade of experience in product management and marketing for business intelligence, data warehouse, and complex event processing products at companies such as Oracle, Peoplesoft, and Business Objects. She holds both a master’s and undergraduate degree from Stanford University.

Dryad- Microsoft's answer to MR

While reading across the internet I came across Microsoft’s version to MapReduce called Dryad- which has been around for some time, but has not generated quite the buzz that Hadoop or MapReduce are doing.

http://research.microsoft.com/en-us/projects/dryadlinq/

DryadLINQ

DryadLINQ is a simple, powerful, and elegant programming environment for writing large-scale data parallel applications running on large PC clusters.

Overview

New! An academic release of Dryad/DryadLINQ is now available for public download.

The goal of DryadLINQ is to make distributed computing on large compute cluster simple enough for every programmers. DryadLINQ combines two important pieces of Microsoft technology: the Dryad distributed execution engine and the .NET Language Integrated Query (LINQ).

Dryad provides reliable, distributed computing on thousands of servers for large-scale data parallel applications. LINQ enables developers to write and debug their applications in a SQL-like query language, relying on the entire .NET library and using Visual Studio.

DryadLINQ translates LINQ programs into distributed Dryad computations:

  • C# and LINQ data objects become distributed partitioned files.
  • LINQ queries become distributed Dryad jobs.
  • C# methods become code running on the vertices of a Dryad job.

DryadLINQ has the following features:

  • Declarative programming: computations are expressed in a high-level language similar to SQL
  • Automatic parallelization: from sequential declarative code the DryadLINQ compiler generates highly parallel query plans spanning large computer clusters. For exploiting multi-core parallelism on each machine DryadLINQ relies on the PLINQ parallelization framework.
  • Integration with Visual Studio: programmers in DryadLINQ take advantage of the comprehensive VS set of tools: Intellisense, code refactoring, integrated debugging, build, source code management.
  • Integration with .Net: all .Net libraries, including Visual Basic, and dynamic languages are available.
  • and
  • Conciseness: the following line of code is a complete implementation of the Map-Reduce computation framework in DryadLINQ:
      • public static IQueryable<R>
      MapReduce<S,M,K,R>(this IQueryable<S> source,
      Expression<Func<S,IEnumerable<M>>> mapper,
      Expression<Func<M,K>> keySelector,
      Expression<Func<K,IEnumerable<M>,R>> reducer)
      {
      return source.SelectMany(mapper).GroupBy(keySelector, reducer);
      }

    and http://research.microsoft.com/en-us/projects/dryad/

    Dryad

    The Dryad Project is investigating programming models for writing parallel and distributed programs to scale from a small cluster to a large data-center.

    Overview

    New! An academic release of DryadLINQ is now available for public download.

    Dryad is an infrastructure which allows a programmer to use the resources of a computer cluster or a data center for running data-parallel programs. A Dryad programmer can use thousands of machines, each of them with multiple processors or cores, without knowing anything about concurrent programming.

    The Structure of Dryad Jobs

    A Dryad programmer writes several sequential programs and connects them using one-way channels. The computation is structured as a directed graph: programs are graph vertices, while the channels are graph edges. A Dryad job is a graph generator which can synthesize any directed acyclic graph. These graphs can even change during execution, in response to important events in the computation.

    Dryad is quite expressive. It completely subsumes other computation frameworks, such as Google’s map-reduce, or the relational algebra. Moreover, Dryad handles job creation and management, resource management, job monitoring and visualization, fault tolerance, re-execution, scheduling, and accounting.

    The Dryad Software Stack

    As a proof of Dryad’s versatility, a rich software ecosystem has been built on top Dryad:

    • SSIS on Dryad executes many instances of SQL server, each in a separate Dryad vertex, taking advantage of Dryad’s fault tolerance and scheduling. This system is currently deployed in a live production system as part of one of Microsoft’s AdCenter log processing pipelines.
    • DryadLINQ generates Dryad computations from the LINQ Language-Integrated Query extensions to C#.
    • The distributed shell is a generalization of the pipe concept from the Unix shell in three ways. If Unix pipes allow the construction of one-dimensional (1-D) process structures, the distributed shell allows the programmer to build 2-D structures in a scripting language. The distributed shell generalizes Unix pipes in three ways:
      1. It allows processes to easily connect multiple file descriptors of each process — hence the 2-D aspect.
      2. It allows the construction of pipes spanning multiple machines, across a cluster.
      3. It virtualizes the pipelines, allowing the execution of pipelines with many more processes than available machines, by time-multiplexing processors and buffering results.
    • Several languages are compiled to distributed shell processes. PSQL is an early version, recently replaced with Scope.

    Publications

    Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks
    Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly
    European Conference on Computer Systems (EuroSys), Lisbon, Portugal, March 21-23, 2007

    Video of a presentation on Dryad at the Google Campus, given by Michael Isard, Nov 1, 2007.

    Also interesting to read-

    Why does Dryad use a DAG?

    he basic computational model we decided to adopt for Dryad is the directed-acyclic graph (DAG). Each node in the graph is a computation, and each edge in the graph is a stream of data traveling in the direction of the edge. The amount of data on any given edge is assumed to be finite, the computations are assumed to be deterministic, and the inputs are assumed to be immutable. This isn’t by any means a new way of structuring a distributed computation (for example Condor had DAGMan long before Dryad came along), but it seemed like a sweet spot in the design space given our other constraints.

    So, why is this a sweet spot? A DAG is very convenient because it induces an ordering on the nodes in the graph. That makes it easy to design scheduling policies, since you can define a node to be ready when its inputs are available, and at any time you can choose to schedule as many ready nodes as you like in whatever order you like, and as long as you always have at least one scheduled you will continue to make progress and never deadlock. It also makes fault-tolerance easy, since given our determinism and immutability assumptions you can backtrack as far as you want in the DAG and re-execute as many nodes as you like to regenerate intermediate data that has been lost or is unavailable due to cluster failures.

    from

    http://blogs.msdn.com/b/dryad/archive/2010/07/23/why-does-dryad-use-a-dag.aspx

      MapReduce Analytics Apps- AsterData's Developer Express Plugin

      AsterData continues to wow with it’s efforts on bridging MapReduce and Analytics, with it’s new Developer Express plug-in for Eclipse. As any Eclipse user knows, that greatly improves ability to write code or develop ( similar to creating Android apps if you have tried to). I did my winter internship at AsterData last December last year in San Carlos, and its an amazing place with giga-level bright people.

      Here are some details ( Note I plan to play a bit more on the plugin on my currently downUbuntu on this and let you know)

      http://marketplace.eclipse.org/content/aster-data-developer-express-plug-eclipse

      Aster Data Developer Express provides an integrated set of tools for development of SQL and MapReduce analytics for Aster Data nCluster, a massively parallel database with an integrated analytics engine.

      The Aster Data Developer Express plug-in for Eclipse enables developers to easily create new analytic application projects with the help of an intuitive set of wizards, immediately test their applications on their desktop, and push down their applications into the nCluster database with a single click.

      Using Developer Express, analysts can significantly reduce the complexity and time needed to create advanced analytic applications so that they can more rapidly deliver deeper and richer analytic insights from their data.

      and from the Press Release

      Now, any developer or analyst that is familiar with the Java programming language can complete a rich analytic application in under an hour using the simple yet powerful Aster Data Developer Express environment in Eclipse. Aster Data Developer Express delivers both rapid development and local testing of advanced analytic applications for any project, regardless of size.

      The free, downloadable Aster Data Developer Express IDE now brings the power of SQL-MapReduce to any organization that is looking to build richer analytic applications that can leverage massive data volumes. Much of the MapReduce coding, including programming concepts like parallelization and distributed data analysis, is addressed by the IDE without the developer or analyst needing to have expertise in these areas. This simplification makes it much easier for developers to be successful quickly and eliminates the need for them to have any deep knowledge of the MapReduce parallel processing framework. Google first published MapReduce in 2004 for parallel processing of big data sets. Aster Data has coupled SQL with MapReduce and brought SQL-MapReduce to market, making it significantly easier for any organization to leverage the power of MapReduce. The Aster Developer Express IDE simplifies application development even further with an intuitive point-and-click development environment that speeds development of rich analytic applications. Applications can be validated locally on the desktop or ultimately within Aster Data nCluster, a massive parallel processing (MPP) database with a fully integrated analytics engine that is powered by MapReduce—known as a data-analytics server.

      Rich analytic applications that can be easily built with Aster Data’s downloadable IDE include:

      Iterative Analytics: Uncovering critical business patterns in your data requires hypothesis-driven, iterative analysis.  This class of applications is defined by the exploratory navigation of massive volumes of data in a top-down, deductive manner.  Aster Data’s IDE makes this easy to develop and to validate the algorithms and functions required to deliver these advanced analytic applications.

      Prediction and Optimization: For this class of applications, the process is inductive. Rather than starting with a hypothesis, developers and analysts can easily build analytic applications that discover the trends, patterns, and outliers in data sets.  Examples include propensity to churn in telecommunications, proactive product and service recommendations in retail, and pricing and retention strategies in financial services.

      Ad Hoc Analysis: Examples of ad hoc analysis that can be performed includes social network analysis, advanced click stream analysis, graph analysis, cluster analysis, and a wide variety of mathematical, trigonometry, and statistical functions.

      “Aster Data’s IDE and SQL-MapReduce significantly eases development of advanced analytic applications on big data. We have now built over 350 analytic functions in SQL-MapReduce on Aster Data nCluster that are available for customers to purchase,” said Partha Sen, CEO and Founder of Fuzzy Logix. “Aster Data’s implementation of MapReduce with SQL-MapReduce goes beyond the capabilities of general analytic development APIs and provides us with the excellent control and flexibility needed to implement even the most complex analytic algorithms.”

      Richer analytics on big data volumes is the new competitive frontier. Organizations have always generated reports to guide their decision-making. Although reports are important, they are historical sets of information generally arranged around predefined metrics and generated on a periodic basis.

      Advanced analytics begins where reporting leaves off. Reporting often answers historical questions such as “what happened?” However, analytics addresses “why it happened” and, increasingly, “what will happen next?” To that end, solutions like Aster Data Developer Express ease the development of powerful ad hoc, predictive analytics and enables analysts to quickly and deeply explore terabytes to petabytes of data.
      “We are in the midst of a new age in analytics. Organizations today can harness the power of big data regardless of scale or complexity”, said Don Watters, Chief Data Architect for MySpace. “Solutions like the Aster Data Developer Express visual development environment make it even easier by enabling us to automate aspects of development that currently take days, allowing us to build rich analytic applications significantly faster. Making Developer Express openly available for download opens the power of MapReduce to a broader audience, making big data analytics much faster and easier than ever before.”

      “Our delivery of SQL coupled with MapReduce has clearly made it easier for customers to build highly advanced analytic applications that leverage the power of MapReduce. The visual IDE, Aster Data Developer Express, introduced earlier this year, made application development even easier and the great response we have had to it has driven us to make this open and freely available to any organization looking to build rich analytic applications,” said Tasso Argyros, Founder and CTO, Aster Data. “We are excited about today’s announcement as it allows companies of all sizes who need richer analytics to easily build powerful analytic applications and experience the power of MapReduce without having to learn any new skills.”

      You can have a look here at http://www.asterdata.com/download_developer_express/

      R Oracle Data Mining

      Here is a new package called R ODM and it is an interface to do Data Mining via Oracle Tables through R. You can read more here http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html and here http://cran.fhcrc.org/web/packages/RODM/RODM.pdf . Also there is a contest for creative use of R and ODM.

      R Interface to Oracle Data Mining

      The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. R-ODM provides a powerful environment for prototyping data analysis and data mining methodologies.

      R-ODM is especially useful for:

      • Quick prototyping of vertical or domain-based applications where the Oracle Database supports the application
      • Scripting of “production” data mining methodologies
      • Customizing graphics of ODM data mining results (examples: classification, regression, anomaly detection)

      The R-ODM interface allows R users to mine data using Oracle Data Mining from the R programming environment. It consists of a set of function wrappers written in source R language that pass data and parameters from the R environment to the Oracle RDBMS enterprise edition as standard user PL/SQL queries via an ODBC interface. The R-ODM interface code is a thin layer of logic and SQL that calls through an ODBC interface. R-ODM does not use or expose any Oracle product code as it is completely an external interface and not part of any Oracle product. R-ODM is similar to the example scripts (e.g., the PL/SQL demo code) that illustrates the use of Oracle Data Mining, for example, how to create Data Mining models, pass arguments, retrieve results etc.

      R-ODM is packaged as a standard R source package and is distributed freely as part of the R environment’s Comprehensive R Archive Network ( CRAN). For information about the R environment, R packages and CRAN, see www.r-project.org.

      and

      Present and win an Apple iPod Touch!
      The BI, Warehousing and Analytics (BIWA) SIG is giving an Apple iPOD Touch to the best new presenter. Be part of the TechCast series and get a chance to win!

      Consider highlighting a creative use of R and ODM.

      BIWA invites all Oracle professionals (experts, end users, managers, DBAs, developers, data analysts, ISVs, partners, etc.) to submit abstracts for 45 minute technical webcasts to our Oracle BIWA (IOUG SIG) Community in our Wednesday TechCast series. Note that the contest is limited to new presenters to encourage fresh participation by the BIWA community.

      Also an interview with Oracle Data Mining head, Charlie Berger https://decisionstats.wordpress.com/2009/09/02/oracle/