Welcome!

Microsoft Cloud Authors: Pat Romanski, Liz McMillan, Lori MacVittie, Elizabeth White, Yeshim Deniz

Related Topics: Java IoT, Microsoft Cloud, Perl, Python

Java IoT: Article

Big Data Kills 30-Year-Old Market

Applications need to go to “Big Data,” not the other way around

Data Services Journal

If you’ve got simply scads of data – and why wouldn’t you? – it’s doubling every 18 months – and are shuttling it to an application for analysis, you’re doing it wrong.

That’s so…so, well, 1980.

According to Aster Data, applications need to go to “Big Data,” not the other way around.

And to do that the company’s got a massively parallel data-application server that can embed applications inside a massively scalable MPP data warehouse and analyze petabytes of data – or terabytes, if that’s all you’ve got – ultra-fast.

Apps are automatically parallelized for scale; users can take their existing Java, C, C++, C#, .NET, Perl and Python applications, MapReduce-enable them and push them down into the data.

The widgetry runs on a cluster of commodity boxes. Figure five servers to start although parallelized applications can utilize terabytes of memory and thousands of CPU cores.

This is not the data warehouses, DBMSes and data analytics solutions of the last three decades that have separated data from applications, a technique Aster says results in massive data movement, latency and restricted analysis.

Traditional systems weren’t built to process billions of rows of data in seconds or handle chi-chi stuff like real-time fraud detection, customer behavior modeling, merchandising optimization, affinity marketing, trending and simulations, trading surveillance and customer calling patterns.

They were built for data sampling, an inexact science. They simply fail in today’s big data, analytics-intensive environments, Aster says.

The company’s Aster Data 4.0 brings data and applications together in one system, fully parallelizing both, to deliver ultra-fast analysis on massive data scales. And it’s got customers like comScore, Full Tilt Poker, Telefonica I+D, SAS and MySpace, with the big clutch of data of all, saying it’s right.

Aster’s Massively Parallel Data-Application Server 4.0, based on research done at Stanford University before commercialization started a couple of years ago, lets companies embed application logic in Aster’s MPP database, which includes MapReduce. It was Aster that brought MapReduce to SQL, a trick it’s now building on.

In Aster’s system data management lives independent of the application processing but – and this is important – the data and applications execute as first-class citizens, with their own respective data and application management services.

The Data-Application Server is responsible for managing and coordinating the cluster’s activities and resource sharing. It acts as a host for the application processing and the data managed inside the cluster.

As a data host, it manages incremental scaling, fault tolerance and heterogeneous hardware for application processing and it manages workloads via Aster’s new Dynamic Workload Management (WLM) capability.

Aster says WLM, described as the first dynamic workload management capability available on a MPP system to run on commodity hardware, can support hundreds of concurrent mixed workloads. It manages data storage, transactional correctness, online backups and information lifecycles (ILM).

The separation of data management and application processing is supposed to provide maximum application portability so a wide range of applications can be pushed down into the system.

Aster says this data analysis architecture distinguishes its solution from lightweight implementations of MapReduce, including what some vendors refer to as ‘In-Database MapReduce.

Richard Zwicky, president of Enquisite, the company that provides search optimization software and solutions, says that with Aster Data, response times for large queries has dropped from five minutes to five-10 seconds, and queries that previously weren’t possible now can be executed in 20-30 seconds.

Aster Data is backed by Sequoia Capital, Jafco Ventures, IVP and Cambrian Ventures, as well as Google’s first investor David Cheriton, Ron Conway and Rajeev Motwani.

More Stories By Maureen O'Gara

Maureen O'Gara the most read technology reporter for the past 20 years, is the Cloud Computing and Virtualization News Desk editor of SYS-CON Media. She is the publisher of famous "Billygrams" and the editor-in-chief of "Client/Server News" for more than a decade. One of the most respected technology reporters in the business, Maureen can be reached by email at maureen(at)sys-con.com or paperboy(at)g2news.com, and by phone at 516 759-7025. Twitter: @MaureenOGara

Comments (1) View Comments

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


Most Recent Comments
MarlenaFernandezBerkowitz 11/09/09 12:49:00 PM EST

Interesting post from Enquisite CEO Mark Hoffman (former
CEO and founder of Sybase) on how they're using Aster
Data to meet their pretty demanding scalability, always-
on needs...

IoT & Smart Cities Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...