Lecture 2: Cluster Computing and MapReduce

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The Right Way to Dodge Scams, Plus Learn How Robo-Investing Works 29:56

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Learn how to dodge scams to protect your money, then understand how to compare robo vs. traditional investment risks. What should you do if your credit card is compromised in a scam? Are robo-advisors riskier than traditional brokerage accounts? Hosts Sean Pyles and Elizabeth Ayoola discuss how to spot and respond to identity theft and dig into how robo-advisors stack up to traditional investing platforms to help you protect your financial life. They kick off Smart Money’s new Scam Stories series by welcoming guest Scramble Hughes, a circus performer and scam victim, who shares a real-life experience with credit card fraud. They discuss tips and tricks on recognizing red flags like mass spam messages, acting fast by calling the number on your card (not clicking links), and filing credit freezes with all three credit bureaus. Then, investing Nerd Bella Avila joins Sean and Elizabeth to discuss how robo-advisors compare to traditional brokerage accounts. They discuss risk levels in automated portfolios, SIPC insurance protections, and key factors to consider when choosing a platform like account minimums, platform stability, and user experience. See NerdWallet’s top picks for the best robo-advisors of 2025 here: https://www.nerdwallet.com/best/investing/robo-advisors Want us to review your budget? Fill out this form — completely anonymously if you want — and we might feature your budget in a future segment! https://docs.google.com/forms/d/e/1FAIpQLScK53yAufsc4v5UpghhVfxtk2MoyooHzlSIRBnRxUPl3hKBig/viewform?usp=header In their conversation, the Nerds discuss: credit card fraud, how to report identity theft, robo advisor vs brokerage account, SIPC insurance limits, credit freeze Experian, how to freeze your credit, credit card scams TikTok, how to know if a text is a scam, what is a robo advisor, tax loss harvesting robo advisor, ETF risk robo advisor, ETF diversification, FDIC vs SIPC, how to block spam texts, freeze credit TransUnion, safest robo advisors 2025, best robo advisor for ETFs, hacked credit card reader, RFID credit card theft, how to recover from identity theft, difference between SIPC and FDIC, scams targeting small business owners, how to secure your investment accounts, how to protect credit card information, email spam after identity theft, what to do after credit card theft, how long do fraud refunds take, when to freeze credit, best practices after identity theft, and comparing investment platform safety. To send the Nerds your money questions, call or text the Nerd hotline at 901-730-6373 or email podcast@nerdwallet.com . Like what you hear? Please leave us a review and tell a friend. Learn more about your ad choices. Visit megaphone.fm/adchoices…

לפני 18 שנים

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Lecture 2: Cluster Computing and MapReduce

Scale Cast – A podcast about big data, distributed systems, and scalability

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published לפני 18 שנים

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MP4•בית הפרקים

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9 פרקים

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כל הפרקים

1
An Introduction to ZooKeeper Video

לפני 17 years

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In 2006 we were building distributed applications that needed a master, aka coordinator, aka controller to manage the sub processes of the applications. It was a scenario that we had encountered before and something that we saw repeated over and over again inside and outside of Yahoo!. For example, we have an application that consists of a bunch of processes. Each process needs be aware of other processes in the system. The processes need to know how requests are partitioned among the processes. They need to be aware of configuration changes and failures. Generally an application specific central control process manages these needs, but generally these control programs are specific to applications and thus represent a recurring development cost for each distributed application. Because each control program is rewritten it doesn’t get the investment of development time to become truly robust, making it an unreliable single point of failure. link to podcast…

1
More Optimal Bloom Filters

לפני 17 years

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The Bloom filter, conceived by Burton H. Bloom in 1970, is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. False positives are possible, but false negatives are not. Elements can be added to the set, but not removed (though this can be addressed with a counting filter). The more elements that are added to the set, the larger the probability of false positives. For example, one might use a Bloom filter to do spell-checking in a space-efficient way. A Bloom filter to which a dictionary of correct words has been added will accept all words in the dictionary and reject almost all words which are not, which is good enough in some cases. Depending on the false positive rate, the resulting data structure can require as little as a byte per dictionary word. In the last few years Bloom filter become hot topic again and there were several modifications and improvements. In this talk I will present my last few improvements in this topic. Speaker: Ely Porat Ely Porat received his Doctorate from Bar-Ilan University in 2000. Following that, he fulfilled his military service and, in parallel, worked as a faculty member at Bar-Ilan University. Having spent the spring 2007 semester as a Visiting Scientist in Google, he is now back at Bar-Ilan University. The main body of Ely Porat’s work concerns matching problems: string matching, pattern matching, subset matching. He also worked on the nearest pair problem in high-dimensional spaces as well as sketching and edit distance. link…

1
An Overview of High Performance Computing and Challenges for the Future

לפני 17 years

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In this talk we examine how high performance computing has changed over the last 10-year and look toward the future in terms of trends. These changes have had and will continue to have a major impact on our software. A new generation of software libraries and algorithms are needed for the effective and reliable use of (wide area) dynamic, distributed and parallel environments. Some of the software and algorithm challenges have already been encountered, such as management of communication and memory hierarchies through a combination of compile–time and run–time techniques, but the increased scale of computation, depth of memory hierarchies, range of latencies, and increased run–time environment variability will make these problems much harder. Link to video…

1
Disk-Based Parallel Computation, Rubik’s Cube, and Checkpointin

לפני 17 years

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This talk takes us on a journey through three varied, but interconnected topics. First, our research lab has engaged in a series of disk-based computations extending over five years. Disks have traditionally been used for filesystems, for virtual memory, and for databases. Disk-based computation opens up an important fourth use: an abstraction for multiple disks that allows parallel programs to treat them in a manner similar to RAM. The key observation is that 50 disks have approximately the same parallel bandwidth as a _single_ RAM subsystem. This leaves latency as the primary concern. A second key is the use of techniques like delayed duplicate detection to avoid latency link to video…

1
Lecture 5: Cluster Computing and MapReduce

לפני 18 years

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1
Lecture 3: Cluster Computing and MapReduce

לפני 18 years

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1
Lecture 4: Cluster Computing and MapReduce

לפני 18 years

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1
Lecture 2: Cluster Computing and MapReduce

לפני 18 years

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1
Lecture 1: Cluster Computing and MapReduce

לפני 18 years

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Lecture 1 in a five part series introducing mapreduce and cluster computing. See http://code.google.com/edu/&#8230 ; for slides and other resources. Link to video

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Scale Cast – A podcast about big data, distributed systems, and scalability « » Lecture 2: Cluster Computing and MapReduce

Lecture 2: Cluster Computing and MapReduce

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Lecture 2: Cluster Computing and MapReduce