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  • Dell eyes $50 million savings; Standardizes data platforms and business intelligence tools

    Dell eyes $50 million savings; Standardizes data platforms and business intelligence tools

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    Dell is currently consolidating data platforms and BI tools while giving business users more options for self-service and deep analytics.

    “When we looked at that $70 million BI spend in the business, 70% of it — or about $50 million — was spent taking data out of the IT environment and putting it back together in their own databases and customer-support systems. We looked at that and asked, ‘how can we reduce that big, $70 million chunk?’”: Rob Schmidt – Executive Director for IT, Analytics, and Business Intelligence

    Dell announced its BI plan two years ago. It assessed cost centers of business units under IT wing by surveying more than 130 business executives. The study found that more than $50 million was being spent annually on its data warehousing, BI and analytics tools. It also found that an additional amount of almost $70 million was being spent on independent data management, BI and analytics initiatives.

    One way of reducing this amount is to build a developer-friendly architecture around centralized. The centralized resources include 200-plus TB Enterprise Data Warehouse (EDW) and SAS analytics environment. The idea was to

    • give better access to data
    • get more development and analysis capabilities
    • get more autonomy on IT supported centralized framework

    For this purpose, a three-tier architecture was launched 18 months back.

    “We’re moving to a platform where the business develops its own applications, but they migrate them up into our second tier, where we automate and put processes around the applications. The third tier is our production data warehouse, which the business can’t touch.”

    This initiative has not changed the technologies drastically. Teradata, SAS, MicroStrategy (production reporting) and Tableau Software (ad-hoc data exploration) were already in use. Business process re-engineering was the biggest challenge as business units signing on to take part would no longer be able to extract data and go their own way.

    “We’re teaching and certifying each business unit to develop against our data platforms. And there’s a formal agreement between myself and a VP in each business that lays out what you can and can’t do on the platform.”

    “Thou shalt not” rules limit data extraction from the warehouse and querying against certain tables that will bring the database down. Certified DBAs and developers within each business unit know that Schmidt can ‘take away access in a heartbeat’ if something impacts the production environment. According to Schmidt, business units get access to more data and better capabilities than they ever had in their independent reporting and analytical environments in exchange for giving up their silos. A Hadoop cluster for high-scale, low-cost storage and a Teradata Aster database for rapid analytical modeling are some of the new capabilities. Social network data, clickstreams from the Dell.com site and machine sensor data from Dell equipment in the field are currently semi-structured.

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    “We used to run customer scoring in batch, and we would update it every two or three months, but now we’re doing near-real-time scoring.”

    According to Schmidt, Dell can support faster, more-frequent, and more-pervasive analysis using these new tools. For example, a marketing customer-propensity-score analysis that previously required pages of SQL and 16 days of data prep on Teradata takes less than five minutes on Aster. Aster is also taking on a complex, high-scale analysis used to spot customers with expiring warranties. That task used to be outsourced to a third-party service company.

    “We paid that vendor a significant amount of money, yet we were only able to do the analysis for the Americas. With these savings alone we have paid for the new environment and we’ve rolled out to all of our regions.”

    Dell has also completed a social media proof-of-concept (POC) project whereby social media data captured through Salesforce.com Radian6 social media monitoring environment is used to develop a single view of Dell customers and prospects, tying transactional records with social media handles and email addresses. According to Schmidt, this effort will reduce the cost of fragmented data-retention and analysis efforts across various departments.

    Waves of POC projects are aimed at improving customer service through machine data analysis. For example, the customer experience on Dell.com is being optimized by analyzing clickstream data that reveals site navigation patterns and dead ends on the site that turned into service phone calls. A predictive maintenance POC project is aimed at spotting failing hard drives and other equipment before it becomes a problem for customers. This ‘Internet-of-Things’ style deployment will tap into sensor data from Dell hardware in use in the field.

    “We’re seeing a massive reduction in data leaving the data warehouse, and we’re also seeing a massive increase in the amount of analytics that can be done. Because the data is shared across the company, there’s very little that I have to do expand this to other groups.”

    Shift to a centralized approach has not been hassle free. Dell is expected to layoff more than 1000 employees in the attempt to consolidate redundant organizations and maintaining fewer independent databases and software instances. A Dell spokesman later claimed that the employees were relocated in order to avoid layoff. So far Dell’s marketing, sales, and service organizations are all on board. Schmidt is looking to certify more groups and get to that $50 million savings goal within the next year.

  • Yellow Pages – New to the ‘Online’ way

    Yellow Pages – New to the ‘Online’ way

    Yellow Pages – Advertisements

    Montreal’s Yellow Pages Group is well known for advertising, especially in print media. It is a competitor in a segment which is expected to be dominated by Google and other online sources. However, despite the competition, today it has around 276,000 Canadian advertisers out of a potential pool of 1.2 million.

    Yellow Pages recently opened itself to data science. This marks the end of outsourcing its big data applications. The company looked at strategic cost implications of in-house big data application to get higher profit margin. It has spent millions to consolidate its 18 data centers into 3 and brought its outsourced business intelligence application in-house by constructing Hadoop clusters to house it.

    The company has two analytics applications:

    • Anametrix, used internally
    • Yellow Pages Analytics, used by advertisers

    Advertisers use Yellow Pages Analytics to crunch metrics like visitors, page views, KPIs and to calculate potential ROI. This is done using big data – one table has 52 billion records with millisecond response times.

    Richard Langlois is the current IT director of Yellow Pages. He was hired in 2012 to transform the company by accelerating the move to digital. When he was hired Yellow Pages Analytics was outsourced to a California firm. Strategic cost implications led the company to bring the company into the digital age by bringing the application in-house. Langlois was given a blank cheque and 18 months to do an infrastructure overhaul, part of a four year plan.

    Langlois appeared in a big data conference in Toronto in June, 2014. He claimed that there is no need to re-invent the wheel for big data applications.

    “What you know about business intelligence, data center architecture, development, strategic planning … we use all of that and big data.”

    Yellow Pages is transitioning from print medium to online. There are subtle differences between these media and Yellow Pages is looking for ways to leverage these differences and get maximum returns.

    “We need to provide location-based content so you bring business to the advertiser.”

    Langlois presented the flow chart of Yellow Pages Analytics at the conference. There are 16 servers and 2 primary nodes for the Hadoop clusters. Also, there are 5 data base machines in production for availability and load balancing. There will be an analytics services layer for business intelligence dashboards to make things easier for small and medium size companies to do analytics and to relieve the IT department of extract, transformation and load (ETL) operations. This does not include improving the collection of data, creating new applications such as unified tracking tool and methodology.

    Despite the complexity of the flow chart, Langlois maintains big data roadmap is no different from an enterprise architecture roadmap. He also added that it is important to decouple architecture from projects so that changes in projects don’t affect the architecture.

  • Hadoop : ex Yahoo CTO’s perspective

    Hadoop : ex Yahoo CTO’s perspective

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    Raymie Stata is the co-founder of Altiscale and ex-CTO of Yahoo. Altiscale is a big data service provider to companies which cannot manage their own big data infrastructure. Stata appeared in Structure Show podcast recently to talk about Hadoop, Altiscale’s approach to Hadoop and Hadoop as a Service. Here are the highlights of the conversation.

    Why Hadoop is best consumed as a service?

    “At some point, somebody will say, ‘Well, how many nodes do I get?’ And that’s when we say, ‘OK, great. Think about it. Why do you care?’ You shouldn’t be thinking about the number of nodes. That’s what it means to have Hadoop as a service.”

    Unlike the typical cloud, where the clients are charged for number of nodes, Stata’s Altiscale bills users based on computing time and storage on a monthly basis.

    Hortonworks and Cloudera aren’t arguing over nothing

    “When you think about a market, first of all, there won’t be one vendor, there will be multiple ones, and, second of all, they’re going to be fiercely competitive.”

    “I think ages ago, in the SQL wars, it was primarily kind of the field — sales and marketing — that would get in these kind of bloody battles.”

    “With the open source element of Hadoop, I think that has brought the competition to the engineering level.”

    “I think the trial by fire that you get in those environments where Hadoop is operating at scale is very valuable for people contributing to the Hadoop code base. … There’s a realization that theory and practice often diverge, and as systems scale up and become more complicated, that happens more and more. And so taking a bit more a data-driven approach to making improvements — versus just ‘Hey, I’ve got an idea’ and hacking away for days at a time and then contributing it and saying, ‘Hey, isn’t this better?’ — there’s just a certain respect, if you will, for the complexity of the system.”

    Companies like Cloudera, Hortonworks, MapR and Pivotal are the biggest players in Hadoop space. However, in Stata’s opinion, relational databases advancements may nullify this competition.

    Why everyone loves Spark?

    “From a performance perspective, because it’s an in-memory solution, obviously, it’s a lot faster than old-style MapReduce, where after every iteration of your algorithm you have to write out to HDFS. With Spark, you’re just kind of updating in place, in memory, so you can do many, many iterations of your iterative algorithms very, very quickly. Those kind of iterative algorithms are very common in machine learning.”

    In recent times, recursive and iterative methods have been replaced by methods of complexity equal to ‘n*log(n)’ and sometimes even ‘n’. Spark might someday replace MapReduce as the de facto processing framework on Hadoop clusters.

    Applications are still the linchpin for big data adoption

    “We distinguish what we call applications from tools. Tools are still horizontal. A Platfora, a Tableau — those are still horizontal tools. They certainly raise the level of abstraction versus Java, but they don’t have any what we call domain specificity. So an application to us is something that actually solves a domain-specific problem. … When you say ‘attribution analysis,’ that’s where that domain-specificity comes in that that’s to me what qualifies as a real solution.”

    Stata acknowledges the fact that there is a gap between the solution and the task it is meant to perform even in Hadoop sector.

    Innovating search in the mold of Moore’s law

    “There’s continuous innovation and more-disruptive innovation, and I think that continuous innovation is real innovation and often is the most scientific. If you think about Moore’s law and what it took to kind of maintain Moore’s law, that’s continuous innovation, but it’s deep, deep work and has been enormously important. So, when I look at classic [algorithmic] search, I would put it in that category of continuous innovation. I think there’s probably some metric where they’re doubling it every 18 to 24 months — some metric of relevance — and maintaing that level of improvement is important because there’s more and more noise out there, so you have to crank up capabilities. But at the same time, it just kind of fades into the background because it just kind of changes.”

    Raymie-StataIn Stata’s opinion, web search business has been evolving at a pace similar to the advances in microprocessors under Moore’s law. He continued in detail:

    “I do think that if you back up, if you look at the bigger picture of people wanting to inform themselves for various purposes, it does seem like there are more disruptive innovations waiting to happen.”

    It’s hard to make computer science a family business

    “It’s not like a restaurant, where from 4 years old on you’re inside doing that”

    “The thing about high-tech is that you can’t really participate until you’re fairly old. And I’m seeing that with my kids, by the way. I’ve got kids, and at 4 years old they didn’t want to do software testing. Imagine that!”

    Stata acknowledges his father’s role in creating entrepreneurial spirit in him, but he also claims that he was not destined or trained to be a technologist. His first exposure to the industry was through an internship in analog devices when he was in grad school.

  • Operating Systems – Internals and Design Principles by William Stallings

     

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    Publication Date: March 10, 2011

    ISBN-10: 013230998X

    ISBN-13: 978-0132309981

    Edition: 7th

     

    Operating Systems: Internals and Design Principles provides a comprehensive and unified introduction to operating systems topics. Stallings emphasizes both design issues and fundamental principles in contemporary systems and gives readers a solid understanding of the key structures and mechanisms of operating systems. He discusses design trade-offs and the practical decisions affecting design, performance and security. The book illustrates and reinforces design concepts and ties them to real-world design choices through the use of case studies in UNIX and Windows.

    Operating Systems: Internals and Design Principles, 6e received the 2009 Textbook Excellence Award from the Text and Academic Authors Association.

     

    Download 7th Edition Now

  • Database System Concepts, Sixth Edition

     

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    Author: Avi Silberschatz, Henry F. Korth, S. Sudarshan

    ISBN: 0-07-352332-1, 978-0-07-352332-3

    Edition: 6th

    “Including Instruction manual”

     

    Description

    Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 6th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible.

    The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.

    Download 6th Edition with Instruction Manual