Welcome!

Weblogic Authors: Yeshim Deniz, Elizabeth White, Michael Meiner, Michael Bushong, Avi Rosenthal

Related Topics: @CloudExpo, Open Source Cloud, Apache

@CloudExpo: Article

Hadoop and Realtime Cloud Computing

Architectures such as MapReduce and Hadoop are good for batch processing of big data, but bad for realtime processing

Big data is creating a massive disruption for the IT industry. Faced with exponentially growing data volumes in every area of business and the web, companies around the world are looking beyond their current databases and data warehouses for new ways to handle this data deluge.

Taking a lead from Google, a number of organizations have been exploring the potential of MapReduce, and its open source clone Hadoop, for big data processing. The MapReduce/Hadoop approach is based around the idea that what's needed is not database processing with SQL queries, but rather dataflow computing with simple parallel programming primitives such as map and reduce.

As Google and others have shown, this kind of basic dataflow programming model can be implemented as a coarse-grain set of parallel tasks that can be run across hundreds or thousands of machines, to carry out large-scale batch processing on massive data sets.

Google themselves have been using MapReduce for batch processing for over six years, and others, such as Facebook, eBay and Yahoo have been using Hadoop for the same kind of batch processing for several years now. So today, parallel dataflow is firmly established as an alternative to databases and data warehouses for offline batch processing of big data. But now the game is changing again...

In recent months, Google has realized that the web is now entering a new era, the realtime era, and that batch processing systems such as MapReduce and Hadoop cannot deliver performance anywhere near the speed required for new realtime services such as Google Instant. Google noted that

  • "MapReduce isn't suited to calculations that need to occur in near real-time"

and that

  • "You can't do anything with it that takes a relatively short amount of time, so we got rid of it"

Other industry leaders, such as Jeff Jonas, Chief Scientist for Analytics at IBM, have made similar remarks in recent weeks. In his recent video "Big Thoughts on Big Data", Jonas notes that with only batch processing tools to handle it, organizations grappling with a relentless avalanche of realtime data will get dumber over time rather than getting smarter.

  • "The idea of waiting for a batch job to run doesn't cut it. Instead, how can an organization make sense of what it knows, as a transaction is happening, so that it can do something about it right then"
  • "I'm not a big fan of batch processes... I've never seen a batch system grow up an become a realtime streaming system, but you can take a realtime streaming system and make it eat batches all day long"
  • "I like Hadoop but it's meant for batch activities. That's not the kind of back-end you would use for realtime sense-making systems"

So coarse-grain dataflow architectures such as Hadoop are good for batch, but bad for realtime.

To power realtime big data apps we need a completely new type of fine-grain dataflow architecture. An architecture that can, for example, continuously analyze a stream of events at a rate of say one million events per second per server, and deliver results with a maximum latency of five seconds between data in and analytics out. At Cloudscale we set out to crack this major technical problem, and to build the world's first "realtime data warehouse". The linearly scalable Cloudscale parallel dataflow architecture not only delivers game-changing realtime performance on commodity hardware, but also, as Jeff Jonas notes above "can eat batches all day long" like a traditional MapReduce or Hadoop architecture. There isn't really an established name yet for such a system. I guess we could call it a "Redoop" architecture (Realtime Dataflow on Ordinary Processors, or Realtime Hadoop).

More Stories By Bill McColl

Bill McColl left Oxford University to found Cloudscale. At Oxford he was Professor of Computer Science, Head of the Parallel Computing Research Center, and Chairman of the Computer Science Faculty. Along with Les Valiant of Harvard, he developed the BSP approach to parallel programming. He has led research, product, and business teams, in a number of areas: massively parallel algorithms and architectures, parallel programming languages and tools, datacenter virtualization, realtime stream processing, big data analytics, and cloud computing. He lives in Palo Alto, CA.

IoT & Smart Cities Stories
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
Digital Transformation is much more than a buzzword. The radical shift to digital mechanisms for almost every process is evident across all industries and verticals. This is often especially true in financial services, where the legacy environment is many times unable to keep up with the rapidly shifting demands of the consumer. The constant pressure to provide complete, omnichannel delivery of customer-facing solutions to meet both regulatory and customer demands is putting enormous pressure on...
IoT is rapidly becoming mainstream as more and more investments are made into the platforms and technology. As this movement continues to expand and gain momentum it creates a massive wall of noise that can be difficult to sift through. Unfortunately, this inevitably makes IoT less approachable for people to get started with and can hamper efforts to integrate this key technology into your own portfolio. There are so many connected products already in place today with many hundreds more on the h...
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
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...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
To Really Work for Enterprises, MultiCloud Adoption Requires Far Better and Inclusive Cloud Monitoring and Cost Management … But How? Overwhelmingly, even as enterprises have adopted cloud computing and are expanding to multi-cloud computing, IT leaders remain concerned about how to monitor, manage and control costs across hybrid and multi-cloud deployments. It’s clear that traditional IT monitoring and management approaches, designed after all for on-premises data centers, are falling short in ...
In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. Manish Dixit is VP of Product and Engineering at Dice. As the leader of the Product, Engineering and Data Sciences team at D...
Dynatrace is an application performance management software company with products for the information technology departments and digital business owners of medium and large businesses. Building the Future of Monitoring with Artificial Intelligence. Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more busine...