This document discusses MalStone and MalGen, benchmarks for evaluating large-scale data analytics frameworks. MalStone models log file analysis, generating synthetic event data with sites, entities, timestamps and marks. It measures the time to compute statistics on this data in MapReduce. MalStone B uses billions of records and took Hadoop 799 minutes to complete. A specialized system completed MalStone B in just 68 minutes, showing the impact of data locality optimizations. The document provides details on the MalStone schema and benchmarks to help evaluate different distributed computing architectures and their suitability for analytics workloads.
GeoMesa presentation from LocationTech Tour - DC - November, 14th 2013. Presented by Anthony Fox (@algoriffic) of CCRi.
GeoMesa is an open source project providing spatio-temporal indexing, querying, and visualizing capabilities to Accumulo. Learn more at http://geomesa.github.io/
Slides of the Apache Omid presentation at Hadoop Summit 2016 in San Jose, CA. Omid is a flexible, reliable, high performant and scalable transaction manager for HBase.
This is a slide deck that I have been using to present on GeoTrellis for various meetings and workshops. The information is speaks to GeoTrellis pre-1.0 release in Q4 of 2016.
These slides were designed for Apache Hadoop + Apache Apex workshop (University program).
Audience was mainly from third year engineering students from Computer, IT, Electronics and telecom disciplines.
I tried to keep it simple for beginners to understand. Some of the examples are using context from India. But, in general this would be good starting point for the beginners.
Advanced users/experts may not find this relevant.
GeoMesa presentation from LocationTech Tour - DC - November, 14th 2013. Presented by Anthony Fox (@algoriffic) of CCRi.
GeoMesa is an open source project providing spatio-temporal indexing, querying, and visualizing capabilities to Accumulo. Learn more at http://geomesa.github.io/
Slides of the Apache Omid presentation at Hadoop Summit 2016 in San Jose, CA. Omid is a flexible, reliable, high performant and scalable transaction manager for HBase.
This is a slide deck that I have been using to present on GeoTrellis for various meetings and workshops. The information is speaks to GeoTrellis pre-1.0 release in Q4 of 2016.
These slides were designed for Apache Hadoop + Apache Apex workshop (University program).
Audience was mainly from third year engineering students from Computer, IT, Electronics and telecom disciplines.
I tried to keep it simple for beginners to understand. Some of the examples are using context from India. But, in general this would be good starting point for the beginners.
Advanced users/experts may not find this relevant.
CRDT stands for Conflict-free Replicated Data Types. They help to build eventual consistent distributed systems. Databases like Cassandra and Riak use them intensively. Application programmers can use them to build semi-offline applications.
DSD-INT 2017 The use of big data for dredging - De BoerDeltares
Presentation by Gerben de Boer (van Oord) at the Symposium Earth Observation and Data Science, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.
CRDT stands for Conflict-free Replicated Data Types. They help to build eventual consistent distributed systems. Databases like Cassandra and Riak use them intensively. Application programmers can use them to build semi-offline applications.
DSD-INT 2017 The use of big data for dredging - De BoerDeltares
Presentation by Gerben de Boer (van Oord) at the Symposium Earth Observation and Data Science, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
Parallel In-Memory Processing and Data Virtualization Redefine Analytics Arch...Denodo
To watch full webinar, follow this link: https://goo.gl/3s9hRG
The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized repository. But this approach is slow and expensive, and sometimes not even feasible, because some data sources are too big to be replicated, and data is often too distributed such as those found in cloud data sources to make a “full centralization” strategy successful.
Attend this webinar to learn:
• Why Logical architectures are the best option when integrating Big Data.
• How Denodo’s parallel in-memory capabilities with dynamic query optimization redefine analytics architectures.
• How IT can meet business demands for data much faster with Data Virtualization.
Agenda:
• Challenges with traditional approaches for analytics architectures.
• Overview of Denodo's parallel in-memory capabilities.
• Product Demo of parallel in-memory capabilities accelerating analytics performance.
• Q&A.
To watch all webinars in Denodo's Packed Lunch Webinar Series, follow this link: https://goo.gl/4xL9wM
Strata Singapore: GearpumpReal time DAG-Processing with Akka at ScaleSean Zhong
Gearpump is a Akka based realtime streaming engine, it use Actor to model everything. It has super performance and flexibility. It has performance of 18000000 messages/second and latency of 8ms on a cluster of 4 machines.
Maximizing Data Lake ROI with Data Virtualization: A Technical DemonstrationDenodo
Watch full webinar here: https://bit.ly/3ohtRqm
Companies with corporate data lakes also need a strategy for how to best integrate them with their overall data fabric. To take full advantage of a data lake, data architects must determine what data belongs in the Lake vs. other sources, how end users are going to find and connect to the data they need as well as the best way to leverage the processing power of the data lake. This webinar will provide you with a deep dive look at how the Denodo Platform for data virtualization enables companies to maximize their investment in their corporate data lake.
Watch on-demand this webinar to learn:
- How to create a logical data fabric with Denodo
- How to leverage the a data lake for MPP Acceleration and Summary Views
- How to leverage Presto with Denodo for file based data lakes (ie. S3, ADLS, HDFS, etc.)
The hidden engineering behind machine learning products at HelixaAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The hidden engineering behind machine learning products at Helixa
Gianmario Spacagna, (Helixa)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
SF Big Analytics & SF Machine Learning Meetup: Machine Learning at the Limit ...Chester Chen
Machine Learning at the Limit
John Canny, UC Berkeley
How fast can machine learning and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. This can lead to dramatic improvements: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to achieve two- to three-orders of magnitude improvements over other toolkits on single machines. These speedups are larger than have been reported for *cluster* systems (e.g. Spark/MLLib, Powergraph) running on hundreds of nodes, and BIDMach with a GPU outperforms these systems for most common machine learning tasks. For algorithms (e.g. graph algorithms) which do require cluster computing, we have developed a rooflined network primitive called "Kylix". We can show that Kylix approaches the rooline limits for sparse Allreduce, and empirically holds the record for distributed Pagerank. Beyond rooflining, we believe there are great opportunities from deep algorithm/hardware codesign. Gibbs Sampling (GS) is a very general tool for inference, but is typically much slower than alternatives. SAME (State Augmentation for Marginal Estimation) is a variation of GS which was developed for marginal parameter estimation. We show that it has high parallelism, and a fast GPU implementation. Using SAME, we developed a GS implementation of Latent Dirichlet Allocation whose running time is 100x faster than other samplers, and within 3x of the fastest symbolic methods. We are extending this approach to general graphical models, an area where there is currently a void of (practically) fast tools. It seems at least plausible that a general-purpose solution based on these techniques can closely approach the performance of custom algorithms.
Bio
John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley's new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, Quantcast and Microsoft. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.
How to Achieve Fast Data Performance in Big Data, Logical Data Warehouse, and...Denodo
Performance is a key consideration for organizations looking to implement big data, logical data warehouse, and operational use cases. In this presentation, the technology expert demonstrates the performance aspects of using data virtualization to accelerate the delivery of fast data to end consumers.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/YMPhvE.
Who: Karthik Ramasamy (@karthikz)
Date: September 20, 2016
Event: #TwitterRealTime
This slide deck consists of presentations from various teams about Twitter's real time infrastructure, the components it uses, and how they function. It includes presentations from David Rusek (@davidrusek), Maosong Fu (@Louis_Fumaosong), Sandy Strong (@st5are), and Yimin Tan (@YiminTan_Kevin).
Hadoop Online Training : kelly technologies is the bestHadoop online Training Institutes in Bangalore. ProvidingHadoop online Training by real time faculty in Bangalore.
Keynote talk at the International Conference on Supercoming 2009, at IBM Yorktown in New York. This is a major update of a talk first given in New Zealand last January. The abstract follows.
The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.
Some Frameworks for Improving Analytic Operations at Your CompanyRobert Grossman
I review three frameworks for analytic operations that are designed to improve the value obtained when deploying analytic models into products, services and internal operations.
This a talk that I gave at BioIT World West on March 12, 2019. The talk was called: A Gen3 Perspective of Disparate Data:From Pipelines in Data Commons to AI in Data Ecosystems.
Crossing the Analytics Chasm and Getting the Models You Developed DeployedRobert Grossman
There are two cultures in data science and analytics - those that develop analytic models and those that deploy analytic models into operational systems. In this talk, we review the life cycle of analytic models and provide an overview of some of the approaches that have been developed for managing analytic models and workflows and for deploying them, including using analytic engines and analytic containers . We give a quick overview of languages for analytic models (PMML) and analytic workflows (PFA). We also describe the emerging discipline of AnalyticOps that has borrowed some of the techniques of DevOps.
This is an overview of the Data Biosphere Project, its goals, its architecture, and the three core projects that form its foundation. We also discuss data commons.
What is Data Commons and How Can Your Organization Build One?Robert Grossman
This is a talk that I gave at the Molecular Medicine Tri Conference on data commons and data sharing to accelerate research discoveries and improve patient outcomes. It also covers how your organization can build a data commons using the Open Commons Consortium's Data Commons Framework and the University of Chicago's Gen3 data commons platform.
Architectures for Data Commons (XLDB 15 Lightning Talk)Robert Grossman
These are the slides from a 5 minute Lightning Talk that I gave at XLDB 2015 on May 19, 2015 at Stanford. It is based in part on our experiences developing the NCI Genomic Data Commons (GDC).
Practical Methods for Identifying Anomalies That Matter in Large DatasetsRobert Grossman
Robert L. Grossman, Practical Methods for Identifying Anomalies That Matter in Large Datasets, O’Reilly, Strata + Hadoop World, San Jose, California, February 20, 2015.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
Malstone KDD 2010
1. MalStone and MalGen Robert GrossmanOpen Data GroupOpen Cloud Consortium Joint work with Collin Bennett, David Locke, Jonathan Seidman and Steve Vejcik
2. Part 1. Other Communities are not Afraid of Benchmarks
4. Hadoop cluster with 910 nodes Sorted 1 TB of data consisting of 10 billion 100-byte records and writing results to disk Each node has 2 quad core 2.0 GHZ Xeons 8 GB RAM per node 40 nodes per rack 8 Gbps Ethernet uplinks from rack to switch
5. Why Is This Important? Helpful when designing out of memory algorithms. Helpful when porting applications to MapReduce and similar environments. Helpful when benchmarking different rack architectures. Helpful to those designing large data clouds to understand trade off space.
6. MapReduceTerasort The job used 1800 maps and 1800 reduces Hadoop pre-0.18 with optimization patches so intermediate results not written to disk Allocated enough memory buffers to hold intermediate data in memory Code checked in as Hadoop example by Hadoop team
11. A new sort algorithm, called AlphaSort, demonstrates that commodity processors and disks can handle commercial batch workloads. Using commodity processors, memory, and arrays of SCSI disks, AlphaSort runs the industry-standard sort benchmark in seven seconds. This beats the best published record on a 32-CPU 32-disk Hypercube by 8:1. On another benchmark, AlphaSort sorted more than a gigabyte in a minute. AlphaSort is a cache-sensitive memory-intensive sort algorithm. We argue that modern architectures require algorithm designers to re-examine their use of the memory hierarchy. AlphaSort uses clustered data structures to get good cache locality. It uses file striping to get high disk bandwidth. It uses QuickSort to generate runs and uses replacement-selection to merge the runs. It uses shared memory multiprocessors to break the sort into subsort chores. Source: Abstract from AlphaSort: A Cache-Sensitive Parallel External Sort, Chris Nyberg, Tom Barclay, ZarkaCvetanovic, Jim Gray, Dave Lomet
15. Log Files Are Everywhere Advertising systems Analyzing system logs Health and status monitoring
16. What are the Common Elements? Time stamps Sites e.g. Web sites, computers, network devices Entities e.g. visitors, users, flows Log files fill disks, many, many disks Behavior occurs at all scales Want to identify phenomena at all scales Need to group “similar behavior” Need to do statistics (not just sorting)
18. MalStone Schema Event ID Time stamp Site ID Entity ID Mark (categorical variable) Fit into 100 bytes
19. Toy Example reduce map/shuffle Events collected by device or processor in time order Map events by site For each site, compute counts and ratios of events by type 17
20. Distributions Tens of millions of sites Hundreds of millions of entities Billions of events Most sites have a few number of events Some sites have many events Most entities visit a few sites Some visitors visit many sites
22. The Mark Model Some sites are marked (percent of mark is a parameter and type of sites marked is a draw from a distribution) Some entities become marked after visiting a marked site (this is a draw from a distribution) There is a delay between the visit and the when the entity becomes marked (this is a draw from a distribution) There is a background process that marks some entities independent of visit (this adds noise to problem)
24. Notation Fix a site s[j] Let A[j] be entities that transact during ExpWin and if entity is marked, then visit occurs before mark Let B[j] be all entities in A[j] that become marked sometime during the MonWin Subsequent proportion of marks is r[j] = | B[j] | / | A[j] |
25. ExpWin MonWin 1 MonWin 2 B[j, t] are entities that become marked during MonWin[j] r[j, t] = | B[j, t] | / | A[j] | dk-2 dk-1 dk time 23
26. Part 3. MalStone Benchmarks code.google.com/p/malgen/ MalGen and MalStone implementations are open source
27. MalStone Benchmark Benchmark developed by Open Cloud Consortium for clouds supporting data intensive computing. Code to generate synthetic data required is available from code.google.com/p/malgen Stylized analytic computation that is easy to implement in MapReduce and its generalizations. 25
29. MalStone B running on 10 Billion 100 byte records Hadoop version 0.18.3 20 nodes in the Open Cloud Testbed MapReduce required 799 minutes Hadoop streams required 142 minutes
30. 68 minutes running MalStone B Benchmark 4 AMD 8435 processors with 24 cores running at 2.6 GHZ 64 Gigabytes of Memory RAID file system with of 5 SATA drives Source: cs.pervasive.com/blogs/datarush/archive/2010/03/05/cluster-on-a-chip.asp March 5, 2010