You are not Facebook or Google? Why you should still care about Big Data and ...Kai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data.
This session goes beyond the well-known examples of huge companies such as Facebook or Google with millions of users. Instead, this session explains the "big" paradigm and technology shift for your company. See several use cases how big data enables small / medium-sized companies to gain insight into new business opportunities (and threats) and how big data stands to transform much of what the modern enterprise is today.
Learn about solving the unique challenges of big data without an own research lab or several big data experts in your company. Learn how to implement the relevant use cases for your company with low costs and efforts by using open source frameworks, which simplify working with big data a lot.
Informatica Becomes Part of the Business Data Lake EcosystemCapgemini
Informatica is now part of the Business Data Lake ecosystem developed by Capgemini and Pivotal. Customers worldwide will now be able to leverage Informatica’s data integration software in addition to Pivotal’s advanced big data, analytics and application software, and Capgemini’s industry and implementation expertise. Informatica will deliver certified technologies for Data Integration, Data Quality and Master Data Management (MDM) to help enterprises distill raw data into actionable insights.
http://www.capgemini.com/resources/the-business-data-lake-delivering-the-speed-and-accuracy-to-solve-your-big-data-problems
This document provides information about IBM's Business Analytics software. It discusses how the volume, variety and velocity of data is growing exponentially creating opportunities and challenges for organizations. It highlights IBM's investments in analytics, big data, and acquisitions to help clients gain insights from both structured and unstructured data. Examples are given of how IBM is helping clients in industries like healthcare, retail, telecommunications, and government to solve complex problems and make smarter data-driven decisions.
Optimize your cloud strategy for machine learning and analyticsCloudera, Inc.
Join industry superstars Mike Olson (Cloudera CSO and co-founder) and Jim Curtis (451 Research senior analyst) as they outline the best practices for cloud-based machine learning and analytics in this “can’t miss” webinar.
Hot topics include:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Success stories, cautionary tales, and lessons learned
James will share 451 Research findings and offer insights learned from surveying both the vendor landscape and enterprise practitioners.
.
Mike will regale you with his vision for the future of multi-disciplinary machine learning and analytics in hybrid- and multi-cloud environments
3 things to learn:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Overview of analytics and big data in practiceVivek Murugesan
Intended to give an overview of analytics and big data in practice. With set of industry use cases from different domains. Would be useful for someone who is trying to understand Analytics and Big Data.
ADV Slides: The Impact of Machine Learning on the Enterprise TodayDATAVERSITY
Despite the dramatic changes we have seen in business recently, another level of change looms.
We are headed toward a future permeated with artificial intelligence and machine learning (ML), where machines take on more of the work people have traditionally done, and then some. The potential for ML is enormous. We are at the dawn of a whole new era of intelligent devices that will revolutionize our business and personal worlds.
Corporations wishing to lead with AI/ML should make plans now to establish their initiatives and their technology framework and nurture the necessary skills.
The document discusses HP's HAVEn big data platform. HAVEn integrates HP technologies like Vertica, Autonomy IDOL, and ArcSight to ingest, analyze, and understand both machine and human data at scale. The platform is designed to process both structured and unstructured data from various sources and provide analytics and visualization capabilities. Examples of companies using HAVEn solutions for log analysis, sensor data analysis, and early warning systems are also presented.
You are not Facebook or Google? Why you should still care about Big Data and ...Kai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data.
This session goes beyond the well-known examples of huge companies such as Facebook or Google with millions of users. Instead, this session explains the "big" paradigm and technology shift for your company. See several use cases how big data enables small / medium-sized companies to gain insight into new business opportunities (and threats) and how big data stands to transform much of what the modern enterprise is today.
Learn about solving the unique challenges of big data without an own research lab or several big data experts in your company. Learn how to implement the relevant use cases for your company with low costs and efforts by using open source frameworks, which simplify working with big data a lot.
Informatica Becomes Part of the Business Data Lake EcosystemCapgemini
Informatica is now part of the Business Data Lake ecosystem developed by Capgemini and Pivotal. Customers worldwide will now be able to leverage Informatica’s data integration software in addition to Pivotal’s advanced big data, analytics and application software, and Capgemini’s industry and implementation expertise. Informatica will deliver certified technologies for Data Integration, Data Quality and Master Data Management (MDM) to help enterprises distill raw data into actionable insights.
http://www.capgemini.com/resources/the-business-data-lake-delivering-the-speed-and-accuracy-to-solve-your-big-data-problems
This document provides information about IBM's Business Analytics software. It discusses how the volume, variety and velocity of data is growing exponentially creating opportunities and challenges for organizations. It highlights IBM's investments in analytics, big data, and acquisitions to help clients gain insights from both structured and unstructured data. Examples are given of how IBM is helping clients in industries like healthcare, retail, telecommunications, and government to solve complex problems and make smarter data-driven decisions.
Optimize your cloud strategy for machine learning and analyticsCloudera, Inc.
Join industry superstars Mike Olson (Cloudera CSO and co-founder) and Jim Curtis (451 Research senior analyst) as they outline the best practices for cloud-based machine learning and analytics in this “can’t miss” webinar.
Hot topics include:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Success stories, cautionary tales, and lessons learned
James will share 451 Research findings and offer insights learned from surveying both the vendor landscape and enterprise practitioners.
.
Mike will regale you with his vision for the future of multi-disciplinary machine learning and analytics in hybrid- and multi-cloud environments
3 things to learn:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Overview of analytics and big data in practiceVivek Murugesan
Intended to give an overview of analytics and big data in practice. With set of industry use cases from different domains. Would be useful for someone who is trying to understand Analytics and Big Data.
ADV Slides: The Impact of Machine Learning on the Enterprise TodayDATAVERSITY
Despite the dramatic changes we have seen in business recently, another level of change looms.
We are headed toward a future permeated with artificial intelligence and machine learning (ML), where machines take on more of the work people have traditionally done, and then some. The potential for ML is enormous. We are at the dawn of a whole new era of intelligent devices that will revolutionize our business and personal worlds.
Corporations wishing to lead with AI/ML should make plans now to establish their initiatives and their technology framework and nurture the necessary skills.
The document discusses HP's HAVEn big data platform. HAVEn integrates HP technologies like Vertica, Autonomy IDOL, and ArcSight to ingest, analyze, and understand both machine and human data at scale. The platform is designed to process both structured and unstructured data from various sources and provide analytics and visualization capabilities. Examples of companies using HAVEn solutions for log analysis, sensor data analysis, and early warning systems are also presented.
Introduction to Segment, Analytics API and Customer Data Platform. (Demo: Segment, AWS Redshift, Redash, Segment and GTM Alternatives) (Frontend Fighters Edition)
Recommended links:
https://segment.com/ - Analytics API and Customer Data Platform
https://open.segment.com/ - Open Source Projects of Segment
https://segment.com/docs/ - Documentation of Segment
https://redash.io/ - Open Sorce Data Dashboard
https://aws.amazon.com/redshift/ - Data Warehouse Solution
https://quicksight.aws/ - Business Analytics Service
https://www.ghostery.com/ - Tracker Detector
Keywords: business agility, tag managers, data-driven
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
IBM Governed data lake is a value-driven big data platform journey. The journey starts by ingesting wide variety of data, governing it, applying data science and machine learning on it to produce actionable insights.
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional BI systems have limitations in handling big data as they are not designed for unstructured data and have data latency issues. A business data lake provides a new approach by storing all raw structured and unstructured data in a single environment at low cost. This allows for near real-time analysis on any data from any source to gain insights.
Why Infrastructure Matters for Big Data & AnalyticsRick Perret
This document discusses how infrastructure is important for big data and analytics. It provides examples of how access, speed, and availability of infrastructure impact organizations' ability to gain insights from data. Specifically, it discusses how IBM's infrastructure capabilities such as data optimization, parallel processing, low latency, and scalability help companies like Bank of Quanzhou, Coca Cola Bottling, and Sui Southern Gas Company optimize access to data, accelerate insights, and maximize availability of information.
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...Capgemini
Rip and replace isn't a good approach to IT change. When looking at Hadoop, MPP, in-memory and predictive analytics the challenge is making them co-exist with current solutions.
Learn how Capgemini’s Pivotal CoE utilizes Cloud Foundry and PivotalOne to help businesses adopt new technologies without losing the value of current investments.
Presented by Michael Wood of Pivotal and Steve Jones, Global Director, Strategy, Big Data and Analytics, Capgemini, at EMC World 2014.
Case Study - Spotad: Rebuilding And Optimizing Real-Time Mobile Adverting Bid...Vasu S
Find out how Qubole helped Spotad, Inc's mobile advertising platform, save 50 percent in its operating costs almost instantly after their migration.
https://www.qubole.com/resources/case-study/spotad
This document discusses choosing the right data architecture for big data projects. It begins by acknowledging big data comes in many types, from structured transactional data to unstructured text data. It then presents several big data architectures and platforms that are suitable for different data types and use cases, such as relational databases, NoSQL databases, data grids, and distributed file systems. The document emphasizes that one size does not fit all and the right choice depends on the specific data and business needs.
The document discusses an upcoming tech summit hosted by Bois Capital, an investment bank focusing on the technology sector. Bois Capital's managing partners have extensive experience in the telecom big data analytics sector. The summit will provide an overview of the telco analytics market and applications across various stakeholders. Recent M&A transactions in the space are also analyzed, with revenue multiples typically between 3-5x for companies under $100m in revenue. The document concludes with a case study of Bois Capital advising a Swiss mobile analytics company in its sale to Gemalto.
Enabling digital business with governed data lakeKaran Sachdeva
Digital business is enabled by Artificial intelligence, Machine learning, and data science. Artificial intelligence and machine learning are dependent on right Information architecture and data foundation. Governed data lake infused with governance and data science platform gives you the power to take the organization in the digital transformation and AI journey.
The Big Picture: Real-time Data is Defining Intelligent OffersCloudera, Inc.
New research shows that 57% of the buying cycle is completed before a prospect even speaks to a company. Marketers already know this, Ninety-six percent (96%) of organizations believe that email personalization can improve email marketing performance. But where do we get this increasingly personal direction? The answer is likely in your customer data. In order to understand your customer needs contextualized in the moment they feel the need to act you will require a platform that can leverage real-time data. Apache Kudu is a Cloudera component that makes dealing with quickly changing data fast and easy. Companies are leveraging next generation data stores like Kudu to build data applications that deliver smart promotions, real-time offers, and personalized marketing. Join us as we discuss modern approaches to real-time application development and highlight key Cloudera use cases being powered by Cloudera’s operational database.
Monitizing Big Data at Telecom Service ProvidersDataWorks Summit
Hadoop enables telecom service providers to gain valuable insights from large volumes of network and customer data. It provides a cost-effective way to store and analyze this data at scale. Specific use cases discussed include using Hadoop to optimize network infrastructure investments based on usage patterns, identify network nodes responsible for most customer issues to prioritize maintenance, and help diagnose network performance problems while handling large volumes of monitoring data.
Big Data Expo 2015 - Pentaho The Future of AnalyticsBigDataExpo
Leer hoe Pentaho kan helpen om zowel legacy data en ongestructureerde (Big) data van verschillende bronnen te blenden en te verrijken om zo waarde te creeeren voor uw organisatie. Praktische voorbeelden illustreren hoe Pentaho dit al bij vele organisaties heeft weten te bereiken.
Zie hoe organisaties Pentaho onder andere inzetten om:
• problemen met te lange ETL jobs op te lossen waardoor Data Warehouse loads weer doorgaan,
• de kosten van data-integratie te verlagen,
• het overlopen van traditionele Data Warehouses en bijkomende kosten doet voorkomen,
• Data Quality en Data Governence in uw process inbrengt en
• hoe dit vervolgens embedded in uw applicaties kan worden geanalyseerd.
The document provides an overview of IBM's big data and analytics capabilities. It discusses what big data is, the characteristics of big data including volume, velocity, variety and veracity. It then covers IBM's big data platform which includes products like InfoSphere Data Explorer, InfoSphere BigInsights, IBM PureData Systems and InfoSphere Streams. Example use cases of big data are also presented.
Unlocking data science in the enterprise - with Oracle and ClouderaCloudera, Inc.
This document discusses unlocking data science in the enterprise with Cloudera Data Science Workbench. It introduces Cloudera Data Science Workbench as a tool that accelerates data science from development to production. It allows data scientists to use R, Python, or Scala from a web browser to directly access and analyze data stored in Hadoop clusters. Cloudera Data Science Workbench provides secure, self-service environments for data scientists while also giving IT control over security and compliance. The document includes a demo of Cloudera Data Science Workbench's features.
IBM InfoSphere Data Replication for Big DataIBM Analytics
How do you balance the need for business agility against the real-time availability of essential big data insights – without impacting your mission critical systems? Review this slideshare and learn how InfoSphere Data Replication can help enable your big data environment.
No fewer than 80% have digital transformation at the centre of their corporate strategy with the aim of improving efficiency, driving innovation and becoming more agile. Though it's clear that insight into the data they hold is going to help them get there, many organisations find themselves at a crossroads. Big data, machine learning, data science: these are all initiatives every company knows they should take on in order to evolve their business, yet few know how to tackle the projects for successful outcomes.
Stream Computing is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources. The solution can handle very high data throughput rates, up to millions of events or messages per second.
This document discusses the economics of cloud computing. While cost reduction is often seen as a primary benefit of cloud adoption, public cloud is not always cheaper than traditional hosting. There are factors to consider like total cost of ownership, transformation costs, and an organization's position in the IT lifecycle. Public cloud costs more per usage unit due to short commitments, but exploiting variable demand patterns through scaling can reduce costs. The optimal approach depends on an application's demand graph shape - periodic, spiked, or cyclical usage may benefit most from a hybrid cloud model.
It covers the basic of analytics, types of analytics, tools, and techniques of analytics, and a briefcase study to demonstrate the predictive analytics with decision tree algorithm of machine learning
Who is the next target proactive approaches to data securityUlf Mattsson
The landscape of threats to sensitive data is changing. New technologies bring with them new vulnerabilities, and organizations like Target are failing to react properly to the shifts around them. What's needed is an approach equal to the persistent, advanced attacks companies face every day. The sooner we start adopting the same proactive thinking hackers are using to get at our data, the better we will be able to protect it.
S ba0881 big-data-use-cases-pearson-edge2015-v7Tony Pearson
IBM is a market leader in big data and analytics solutions. This session explains the basics of Big Data, with actual use cases of clients who have benefited from IBM solutions in this space, followed by architectures with IBM BigInsights, BigSQL, Platform Symphony and Spectrum Scale.
Introduction to Segment, Analytics API and Customer Data Platform. (Demo: Segment, AWS Redshift, Redash, Segment and GTM Alternatives) (Frontend Fighters Edition)
Recommended links:
https://segment.com/ - Analytics API and Customer Data Platform
https://open.segment.com/ - Open Source Projects of Segment
https://segment.com/docs/ - Documentation of Segment
https://redash.io/ - Open Sorce Data Dashboard
https://aws.amazon.com/redshift/ - Data Warehouse Solution
https://quicksight.aws/ - Business Analytics Service
https://www.ghostery.com/ - Tracker Detector
Keywords: business agility, tag managers, data-driven
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
IBM Governed data lake is a value-driven big data platform journey. The journey starts by ingesting wide variety of data, governing it, applying data science and machine learning on it to produce actionable insights.
Traditional BI vs. Business Data Lake – A ComparisonCapgemini
Traditional BI systems have limitations in handling big data as they are not designed for unstructured data and have data latency issues. A business data lake provides a new approach by storing all raw structured and unstructured data in a single environment at low cost. This allows for near real-time analysis on any data from any source to gain insights.
Why Infrastructure Matters for Big Data & AnalyticsRick Perret
This document discusses how infrastructure is important for big data and analytics. It provides examples of how access, speed, and availability of infrastructure impact organizations' ability to gain insights from data. Specifically, it discusses how IBM's infrastructure capabilities such as data optimization, parallel processing, low latency, and scalability help companies like Bank of Quanzhou, Coca Cola Bottling, and Sui Southern Gas Company optimize access to data, accelerate insights, and maximize availability of information.
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...Capgemini
Rip and replace isn't a good approach to IT change. When looking at Hadoop, MPP, in-memory and predictive analytics the challenge is making them co-exist with current solutions.
Learn how Capgemini’s Pivotal CoE utilizes Cloud Foundry and PivotalOne to help businesses adopt new technologies without losing the value of current investments.
Presented by Michael Wood of Pivotal and Steve Jones, Global Director, Strategy, Big Data and Analytics, Capgemini, at EMC World 2014.
Case Study - Spotad: Rebuilding And Optimizing Real-Time Mobile Adverting Bid...Vasu S
Find out how Qubole helped Spotad, Inc's mobile advertising platform, save 50 percent in its operating costs almost instantly after their migration.
https://www.qubole.com/resources/case-study/spotad
This document discusses choosing the right data architecture for big data projects. It begins by acknowledging big data comes in many types, from structured transactional data to unstructured text data. It then presents several big data architectures and platforms that are suitable for different data types and use cases, such as relational databases, NoSQL databases, data grids, and distributed file systems. The document emphasizes that one size does not fit all and the right choice depends on the specific data and business needs.
The document discusses an upcoming tech summit hosted by Bois Capital, an investment bank focusing on the technology sector. Bois Capital's managing partners have extensive experience in the telecom big data analytics sector. The summit will provide an overview of the telco analytics market and applications across various stakeholders. Recent M&A transactions in the space are also analyzed, with revenue multiples typically between 3-5x for companies under $100m in revenue. The document concludes with a case study of Bois Capital advising a Swiss mobile analytics company in its sale to Gemalto.
Enabling digital business with governed data lakeKaran Sachdeva
Digital business is enabled by Artificial intelligence, Machine learning, and data science. Artificial intelligence and machine learning are dependent on right Information architecture and data foundation. Governed data lake infused with governance and data science platform gives you the power to take the organization in the digital transformation and AI journey.
The Big Picture: Real-time Data is Defining Intelligent OffersCloudera, Inc.
New research shows that 57% of the buying cycle is completed before a prospect even speaks to a company. Marketers already know this, Ninety-six percent (96%) of organizations believe that email personalization can improve email marketing performance. But where do we get this increasingly personal direction? The answer is likely in your customer data. In order to understand your customer needs contextualized in the moment they feel the need to act you will require a platform that can leverage real-time data. Apache Kudu is a Cloudera component that makes dealing with quickly changing data fast and easy. Companies are leveraging next generation data stores like Kudu to build data applications that deliver smart promotions, real-time offers, and personalized marketing. Join us as we discuss modern approaches to real-time application development and highlight key Cloudera use cases being powered by Cloudera’s operational database.
Monitizing Big Data at Telecom Service ProvidersDataWorks Summit
Hadoop enables telecom service providers to gain valuable insights from large volumes of network and customer data. It provides a cost-effective way to store and analyze this data at scale. Specific use cases discussed include using Hadoop to optimize network infrastructure investments based on usage patterns, identify network nodes responsible for most customer issues to prioritize maintenance, and help diagnose network performance problems while handling large volumes of monitoring data.
Big Data Expo 2015 - Pentaho The Future of AnalyticsBigDataExpo
Leer hoe Pentaho kan helpen om zowel legacy data en ongestructureerde (Big) data van verschillende bronnen te blenden en te verrijken om zo waarde te creeeren voor uw organisatie. Praktische voorbeelden illustreren hoe Pentaho dit al bij vele organisaties heeft weten te bereiken.
Zie hoe organisaties Pentaho onder andere inzetten om:
• problemen met te lange ETL jobs op te lossen waardoor Data Warehouse loads weer doorgaan,
• de kosten van data-integratie te verlagen,
• het overlopen van traditionele Data Warehouses en bijkomende kosten doet voorkomen,
• Data Quality en Data Governence in uw process inbrengt en
• hoe dit vervolgens embedded in uw applicaties kan worden geanalyseerd.
The document provides an overview of IBM's big data and analytics capabilities. It discusses what big data is, the characteristics of big data including volume, velocity, variety and veracity. It then covers IBM's big data platform which includes products like InfoSphere Data Explorer, InfoSphere BigInsights, IBM PureData Systems and InfoSphere Streams. Example use cases of big data are also presented.
Unlocking data science in the enterprise - with Oracle and ClouderaCloudera, Inc.
This document discusses unlocking data science in the enterprise with Cloudera Data Science Workbench. It introduces Cloudera Data Science Workbench as a tool that accelerates data science from development to production. It allows data scientists to use R, Python, or Scala from a web browser to directly access and analyze data stored in Hadoop clusters. Cloudera Data Science Workbench provides secure, self-service environments for data scientists while also giving IT control over security and compliance. The document includes a demo of Cloudera Data Science Workbench's features.
IBM InfoSphere Data Replication for Big DataIBM Analytics
How do you balance the need for business agility against the real-time availability of essential big data insights – without impacting your mission critical systems? Review this slideshare and learn how InfoSphere Data Replication can help enable your big data environment.
No fewer than 80% have digital transformation at the centre of their corporate strategy with the aim of improving efficiency, driving innovation and becoming more agile. Though it's clear that insight into the data they hold is going to help them get there, many organisations find themselves at a crossroads. Big data, machine learning, data science: these are all initiatives every company knows they should take on in order to evolve their business, yet few know how to tackle the projects for successful outcomes.
Stream Computing is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources. The solution can handle very high data throughput rates, up to millions of events or messages per second.
This document discusses the economics of cloud computing. While cost reduction is often seen as a primary benefit of cloud adoption, public cloud is not always cheaper than traditional hosting. There are factors to consider like total cost of ownership, transformation costs, and an organization's position in the IT lifecycle. Public cloud costs more per usage unit due to short commitments, but exploiting variable demand patterns through scaling can reduce costs. The optimal approach depends on an application's demand graph shape - periodic, spiked, or cyclical usage may benefit most from a hybrid cloud model.
It covers the basic of analytics, types of analytics, tools, and techniques of analytics, and a briefcase study to demonstrate the predictive analytics with decision tree algorithm of machine learning
Who is the next target proactive approaches to data securityUlf Mattsson
The landscape of threats to sensitive data is changing. New technologies bring with them new vulnerabilities, and organizations like Target are failing to react properly to the shifts around them. What's needed is an approach equal to the persistent, advanced attacks companies face every day. The sooner we start adopting the same proactive thinking hackers are using to get at our data, the better we will be able to protect it.
S ba0881 big-data-use-cases-pearson-edge2015-v7Tony Pearson
IBM is a market leader in big data and analytics solutions. This session explains the basics of Big Data, with actual use cases of clients who have benefited from IBM solutions in this space, followed by architectures with IBM BigInsights, BigSQL, Platform Symphony and Spectrum Scale.
This document discusses using big data sources in official statistics and provides three case studies:
1. Using sentiment analysis of social media messages to create an indicator for the business cycle, with Facebook sentiment correlating most strongly.
2. Analyzing mobile phone metadata to estimate daytime populations and track tourism, finding commuter patterns in Almere and many foreigners at Schiphol airport.
3. Examining traffic loop data to create transport and traffic statistics, and finding ways to correct for selectivity in big data sources.
Human Information is made up of ideas, is diverse, and has context.
Ideas don’t exactly match like data does; they have distance.
Human Information is not static – it’s dynamic and lives everywhere.
Details on applications
HAVEn is integrated to costumers architecture through other n Apps
HP has started modifying our existing application portfolio to use HAVEn
And HP is building new applications that leverage power of HAVEn
Many customers are already building applications that use multiple HAVEn
This presentation introduces concepts of Big Data in a layman's language. Author does not claim the originality of the content. The presentation is made by compiling from various sources. Author does not claim copyrights or privacy issues.
Big data is exponentially rising in today's age of information and digital shrinkage. This presentation potentially clears the concept and revolving hype around it.
This document provides an introduction to big data and Hadoop. It discusses the three V's of big data: volume, variety, and velocity. Examples are given of the large amounts of data generated daily from various sources. The growth and market opportunity for big data technologies is also discussed. Common use cases for big data in different industries are outlined. The document then covers Hadoop components and how Hadoop HDFS and MapReduce work. Other Hadoop technologies like Hive, Pig, and Zookeeper are introduced. Benefits of Hadoop and commercial Hadoop distributions are summarized. Finally, technologies alternative to Hadoop like HPCC and SAP HANA are briefly described.
This document discusses big data and Hadoop training. It provides links to read a complete article on 5 big data use cases and to learn more about IBM Certified big data and Hadoop training. Clicking the links would take the reader to more information on common big data uses and certification programs.
Security Trends in the Retail IndustryIBM Security
View on demand webinar: https://securityintelligence.com/events/security-trends-in-the-retail-industry/
In 2014, significant threats and massive breaches made front-page news on a regular basis, and those that hit retailers seemed to be the ones that jumped to mind first. This may have been due, in part, to a sizable uptick in the number of cyber attacks against US retailers versus the prior year. In 2015 however, the cybercrime focus has shifted to online retailers and smaller businesses. With large retailers tightening security controls and safer chip cards coming into use, hackers are turning their sights to online transactions and smaller retail targets to capture consumer credit card data.
Join us as Nick Bradley, Practice Leader of the Threat Research Group at IBM Security, and Michelle Alvarez, Threat Researcher and Editor for IBM Managed Security Services, discuss findings from two recently-published reports on the threat landscape in the retail industry: IBM 2015 Cyber Security Intelligence Index for Retail, and Security trends in the retail industry, an IBM X-Force Research Managed Security Services report. This webinar will cover:
- An overview of security events, attacks, and incidents in the retail industry
- Attack trends over Black Friday-Cyber Monday, including 2015 data
- Who the attackers are, where the attacks are happening and what types of attacks are most commonly used
- The number of records compromised, and where the weak points are in retailer networks
- How cyber criminals are responding to the introduction of chip cards
This document discusses big data and use cases. It begins by reviewing the history and evolution of big data and advanced analytics. It then explains how technologies like Hadoop, stream processing, and in-memory computing support big data solutions. The document presents two use cases - analyzing credit risk by examining customer transaction data to improve credit offers, and detecting fraud by analyzing financial transactions for unusual patterns that could indicate suspicious activity. It describes how these use cases leverage technologies like Oracle R Connector for Hadoop to run analytics and machine learning algorithms on large datasets.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
Hack the Hackers 2012: Client Side Hacking – Targeting the UserNew Horizons Bulgaria
This document summarizes a presentation given by Sean Hanna on client side hacking. The presentation discussed how hacking has evolved from hobbyists to security research companies to organized criminal gangs producing crimeware. It noted how governments are now developing cyber warfare capabilities in a growing arms race. The presentation demonstrated hacking tools and warned that client systems are increasingly being targeted, and that future threats will be even more advanced as hacking continues to evolve.
This Big Data case study outlines the Hadoop infrastructure deployment for a Fortune 100 media and telecommunications company.
Hadoop adoption in this company had grown organically across multiple different teams, starting with “science projects” and lab initiatives that quickly grew and expanded. Going forward, some of the options they considered for their Big Data deployment included expanding their on-premises infrastructure and using a Hadoop-as-a-Service cloud offering.
Fortunately, they realized that there is a third option: providing the benefits of Hadoop-as-a-Service with on-premises infrastructure. They selected the BlueData EPIC software platform to virtualize their Hadoop infrastructure and provide on-demand access to virtual Hadoop clusters in a secure, multi-tenant model.
Learn more about this case study in the blog post at: http://www.bluedata.com/blog/2015/05/big-data-case-study-hadoop-infrastructure
Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
Jump start into 2013 by exploring how Big Data can transform your business. Listen to Infochimps Director of Product, Tim Gasper, cover the leading use cases for 2013, sharing where the data comes from, how the systems are architected and most importantly, how they drive business insights for data-driven decisions.
This document discusses Okta's use of AWS KMS for encryption key management. It provides background on Okta as a company and describes their requirements for encryption. It then details Okta's implementation of AWS KMS for encrypting user data, including how they structure encryption keys and handle failures. The document also addresses authorization, auditing, performance tuning and rollout considerations for using AWS KMS.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Big data 4 4 the art of the possible 4-en-webRick Bouter
This document discusses the potential of big data and how organizations can tap into it. It covers:
- Big data's potential through combining internal and external structured and unstructured data from different sectors like healthcare. This allows for new insights and services.
- Organizations are at different stages of realizing big data's potential. Studies have examined how organizations are developing their capabilities and what factors influence adoption.
- Realizing big data's full potential requires both technological expertise and changes to organizational structures and processes. It also requires integrating new and existing data sources and systems.
- Ten key questions are discussed that organizations should consider to help understand their big data potential and how to develop the necessary strategies, skills and partnerships to
This document summarizes a presentation on using analytics for better decision making at nonprofit organizations. The presentation discusses how nonprofits currently use some basic analytics like budgets and dashboards but have untapped potential to use data more extensively. It identifies common challenges to data utilization as collecting quality data, lacking expertise, technology and prioritizing time and money for analytics. The presentation provides examples of how benchmarking reveals data gaps and inconsistencies between systems. It emphasizes the value of tracking program and outcome data and client information.
The document discusses building a big data analytics strategy in 3 main steps: 1) Gather requirements and objectives to determine a candidate strategy, 2) Select appropriate tools and technology to implement the strategy, and 3) Implement the strategy through operational readiness. It also covers key concepts like the 3V's model of big data, the big data analytics lifecycle, and strategy considerations at each phase like volume, variety and velocity of data. Example case studies of social media analytics on Hadoop are provided.
Improve Efficiency & Reduce Costs through BI in Fertilizer SectorDhiren Gala
Efficiency Improvement & Cost Reduction through Business Intelligence (BI) in Fertilizer Sector. - A presentation by Sanjay Mehta, CEO, MAIA Intelligence at The Fertilizer Association of India (FAI) WORKSHOP ON ICT FOR IMPROVING EFFICIENCY IN FERTILISER AND AGRICULTURE SECTORS held from March 16-19, 2009 at Manali.
This document discusses how machine learning can be applied in various industries like insurance, financial services, and healthcare. It presents use cases of machine learning in areas like cost reduction, risk management, fraud detection, marketing, and improved customer service. It then describes the key aspects of automated machine learning as developing many models at scale, deploying them easily, and using the models for day-to-day business decisions. The document promotes an automated machine learning platform as making machine learning scalable, pervasive and inclusive.
This document describes an integrated cloud-based cybersecurity platform called iSecureCyber that was created to address gaps in the market for small and medium businesses (SMBs). It automates cybersecurity tasks for non-technical SMB users. The platform has expanded its features since launching in 2020. It aims to become the leading cybersecurity solution for SMBs worldwide, with a total addressable market of 400 million SMBs and $2 trillion. Testimonials praise its ease of use and flexibility. The founders are seeking a $5 million Series A investment to further develop the platform and expand marketing and operations.
This document discusses the value and risks of big data. It begins with defining big data as large and complex data sets that require new technologies to manage and analyze. The document then discusses how big data is used for marketing, recommendations, analytics, and other purposes. It notes both the benefits but also risks of poor data quality and limited governance of big data projects. The document also provides overviews of technologies like Hadoop, MapReduce, Pig, Hive, and NoSQL that support big data. It questions whether social data should be considered a corporate asset and discusses the complexity of understanding big data risks. Overall, the document aims to highlight both the opportunities and governance challenges presented by big data.
This document contains a summary of a webinar discussing tips for selecting a threat and vulnerability management solution. It includes an introduction of the speakers and their backgrounds. The webinar then covers 10 tips for selecting such a solution, including allowing access to underlying data, assisting operational flexibility, delivering a knowledge base, enhancing security context, facilitating integration and automation, operating as a force multiplier, producing metrics and reporting, ensuring scalability and performance, supporting data compartmentalization, and simplifying collaboration. It concludes with an overview of RiskSense's platform and scoring model, and next steps.
Lotusphere Id601 - Understanding the marketplace advantages for IBM Lotus sol...Ed Brill
The document discusses IBM Lotus collaboration software solutions and their advantages over competitors. It highlights IBM's leadership position across key collaboration categories like enterprise email, portals, and business process management. The document also summarizes IBM's product strategy, roadmap, and focus on open standards and integration. It addresses questions customers may have around migration costs when evaluating switching from existing solutions.
Agile Tour Taichung 201601 從趨勢科技的agile之旅談改變的導入AgileTaichung
The document discusses Trend Micro's journey towards adopting Agile practices. It began in 2007 with 10 projects adopting Agile and has since grown to 49 projects, 7 of which have continued for multiple iterations. Surveys in 2010 found improvements in processes. Key aspects of the transition included developing an Agile mentality, establishing communities to share knowledge, and making continuous improvements. The presentation emphasizes that change is an ongoing journey and provides tips for introducing change, such as clarifying objectives and benefits, starting with early adopters, and expanding influence over time.
1) The document discusses big data analytics and introduces Greenplum, a massively parallel processing (MPP) database for big data analytics.
2) Greenplum allows for integrated analysis of structured and unstructured data at scale through its SQL database and Hadoop integration.
3) The architecture provides linear scalability, flexibility to handle various data types and schemas, and rich language support for analytics.
This document discusses big data and the challenges of integrating structured and unstructured data sources. It provides examples of big data use cases in various industries. It then introduces Greenplum as a platform for big data analytics that can handle high volumes, varieties and velocities of data using its massively parallel processing architecture. Greenplum allows for both SQL and MapReduce processing to enable real-time insights from large and diverse datasets.
Migrating BI Systems? Failure Is Not An OptionSenturus
Learn how migrations have changed over time and the common phases and steps in most migrations. You’ll also get the common pitfalls that can trip you up as well as the tips and tricks to help ensure a smooth transition. View the video recording and download this deck at:
Senturus, a business analytics consulting firm, has a resource library with hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Data, Interconnectedness & The Internet of Things Software AG
Innovation World 2013 presentation.
The key to deriving value from fast data is being able to access, analyze and respond to it in real-time. Robin Gilthorpe explores the deep capabilities and synergies of Real-Time Analytics (Apama) and In-Memory (Terracotta) Platforms, sharing a breadth of insights around use cases and customer successes.
Speaker:
Robin Gilthorpe
CEO, Terracotta
Presentation delivered by Craig Smith at Fusion in Sydney, Australia in September 2012.
When XP and Scrum were devised over 10 years ago, they were created to improve the delivery of software development projects. As many enterprises have matured in the Agile adoption, many of the business users on IT projects are now attempting to use Agile approaches on their own non-IT projects.
In this session we will cover using Agile in a non-IT environment and demonstrate how the original XP practices map extremely well over to business processes. And how those in SD can help your business counterparts.
This document outlines Ingersoll Rand's lean deployment strategy presented at a lean summit in Shanghai. It discusses using lean principles in business strategy and goal deployment to drive operational excellence. Key aspects of the strategy include top leadership commitment, coaching to solve problems, and building a culture of continuous improvement through tools like A3 thinking and a mission control board. The goal is to increase speed, flow, and alignment across the value chain.
Leveraging Analytics to achieve your Customer Experience ObjectivesJj HanXue
Presented by Graham Cobb, European Industry Leader for Banking and Financial Markets, Business Analytics at IBM
For the complete presentation, see http://bit.ly/NOsWDA.
Alternatively, please visit http://www.customerexperiencefinance.com/share.
This document discusses how the cloud is well suited to address the challenges of big data. It notes that big data sets are getting larger and more complex, requiring new tools and approaches. The cloud optimizes precious IT resources by enabling elastic scaling, global accessibility, easy experimentation, and reducing costs. The cloud empowers users to balance costs and time. Several real-world examples are provided, such as banks using the cloud to perform Monte Carlo simulations and retailers using it for targeted recommendations and click stream analysis.
Similar to Big Data Use Cases for Different Verticals and Adoption Patterns - Impetus Webinar (20)
Future-Proof Your Streaming Analytics Architecture- StreamAnalytix WebinarImpetus Technologies
Future-Proof Your Streaming Analytics Architecture- StreamAnalytix Webinar
View the webcast on http://bit.ly/1HFD8YR
The speakers from Forrester and Impetus talk about the options and optimal architecture to incorporate real-time insights into your apps that provisions benefitting from future innovation also.
Impetus White Paper- Handling Data Corruption in ElasticsearchImpetus Technologies
This white paper focuses on handling data corruption in Elasticsearch. It describes how to recover data from corrupted indices of Elasticsearch and re-index that data in a new index. The paper also guides you about Lucene’s index terminology
Real-world Applications of Streaming Analytics- StreamAnalytix WebinarImpetus Technologies
This document summarizes a webinar on real-world applications of streaming analytics. It discusses case studies of companies in various industries using the StreamAnalytix platform for real-time analytics on large data streams. Examples include classifying 250 million messages per day for an intelligence company and monitoring response times for a healthcare application. The webinar focuses on business problems solved through streaming analytics and the StreamAnalytix product capabilities.
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
SPARK USE CASE- Distributed Reinforcement Learning for Electricity Market Bi...Impetus Technologies
SPARK SUMMIT SESSION -
A majority of the electricity in the U.S. is traded in independent system operator (ISO) based wholesale markets. ISO-based markets typically function in a two-step settlement process with day-ahead (DA) financial settlements followed by physical real-time (spot) market settlements for electricity. In this work, we focus on obtaining equilibrium bidding strategies for electricity generators in DA markets. Electricity prices in DA markets are determined by the ISO, which matches competing supply offers from power generators with demand bids from load serving entities. Since there are multiple generators competing with one another to supply power, this can be modeled as a competitive Markov decision problem, which we solve using a reinforcement learning approach. For power networks of realistic sizes, the state-action space could explode, making the RL procedure computationally intensive. This has motivated us to solve the above problem over Spark. The talk provides the following takeaways:
1. Modeling the day-ahead market as a Markov decision process
2. Code sketches to show the markov decision process solution over Spark and Mahout over Apache Tez
3. Performance results comparing Mahout over Apache Tez and Spark.
The document discusses the growing dominance of Android in the mobile operating system market and the challenges of managing Android devices in an enterprise setting. It proposes an enterprise-ready Android solution involving an on-device agent, device administration console, and enterprise Android platform to enable features like multiple enterprise users, remote commands and policy management, security management and customization. A sample deployment with Nexus 7 tablets is offered to pilot test the solution.
Real-time Streaming Analytics: Business Value, Use Cases and Architectural Co...Impetus Technologies
Impetus webcast ‘Real-time Streaming Analytics: Business Value, Use Cases and Architectural Considerations’ available at http://bit.ly/1i6OrwR
The webinar talks about-
• How business value is preserved and enhanced using Real-time Streaming Analytics with numerous use-cases in different industry verticals
• Technical considerations for IT leaders and implementation teams looking to integrate Real-time Streaming Analytics into enterprise architecture roadmap
• Recommendations for making Real-time Streaming Analytics – real – in your enterprise
• Impetus StreamAnalytix – an enterprise ready platform for Real-time Streaming Analytics
Leveraging NoSQL Database Technology to Implement Real-time Data Architecture...Impetus Technologies
Impetus webcast "Leveraging NoSQL Database Technology to Implement Real-time Data Architectures” available at http://bit.ly/1g6Eaj4
This webcast:
• Presents trade-offs of using different approaches to achieve a real-time architecture
• Closely examines an implementation of a NoSQL based real-time architecture
• Shares specific capabilities offered by NoSQL Databases that enable cost and reliability advantages over other techniques
Maturity of Mobile Test Automation: Approaches and Future Trends- Impetus Web...Impetus Technologies
Impetus webcast " Maturity of Mobile Test Automation: Approaches and Future Trends " available at http://lf1.me/Pxb/
This Impetus webcast talks about:
• Mobile test automation challenges
• Evolution of test automation challenges from Unit tests to image based and object comparison methods
• What next?
• Impetus solution approach for comprehensive mobile testing automation
Webinar maturity of mobile test automation- approaches and future trendsImpetus Technologies
Comprehensive mobile application testing is crucial for business success but presents challenges for multi-platform testing that can impact quality, timelines, and profits. This webinar will discuss the evolution of mobile test automation techniques from unit to image-based and object tests. Attendees can learn about current approaches and future trends in automation, challenges in testing across platforms, and Impetus Technologies' solution for comprehensive mobile testing.
This document provides an overview of next generation analytics with YARN, Spark and GraphLab. It discusses how YARN addressed limitations of Hadoop 1.0 like scalability, locality awareness and shared cluster utilization. It also describes the Berkeley Data Analytics Stack (BDAS) which includes Spark, and how companies like Ooyala and Conviva use it for tasks like iterative machine learning. GraphLab is presented as ideal for processing natural graphs and the PowerGraph framework partitions such graphs for better parallelism. PMML is introduced as a standard for defining predictive models, and how a Naive Bayes model can be defined and scored using PMML with Spark and Storm.
The Shared Elephant - Hadoop as a Shared Service for Multiple Departments – I...Impetus Technologies
For Impetus’ White Papers archive, visit- http://lf1.me/drb/
This white paper talks about the design considerations for enterprises to run Hadoop as a shared service for multiple departments.
As Hadoop becomes more mainstream and indispensable to enterprises, it is imperative that they build, operate and scale shared Hadoop clusters. The design considerations discussed in this paper will help enterprises accomplish the essential mission of running multi-tenant, multi-use Hadoop clusters at scale.
The white paper talks about Identity, Security, Resource Sharing, Monitoring and Operations on the Central Service.
For Impetus’ White Papers archive, visit- http://lf1.me/drb/
Performance Testing of Big Data Applications - Impetus WebcastImpetus Technologies
Impetus webcast "Performance Testing of Big Data Applications" available at http://lf1.me/cqb/
This Impetus webcast talks about:
• A solution approach to measure performance and throughput of Big Data applications
• Insights into areas to focus for increasing the effectiveness of Big Data performance testing
• Tools available to address Big Data specific performance related challenges
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
About Impetus TechnologiesEnterprise and Partners leverage the thought leadership of our advisors, the experience of our architects, and our ability to create applications for accelerated business growth. Impetus, Innovation Architected.
NEED –ComScore, Web 2.0, Advertising industryThere are two answers to this – COST side and REVENUE side
Folks – I tried to draw a bar chart of this Traditional vs. Hadoop – the hadoop piece doesn’t even show on the scale – we are pretty much talking – close to zero in comparison with traditional numbers.
Talking pointsThe Y axis is really non-linearIntuition doesn’t begin at zero on the y axisMore measurement – better management
“big data strategy is a journey, not a destination. It’s not a product you’re going to buy; it’s not something you’re going to stand up there and be done with.”
"We analyzed very early that the problem in Democratic politics was you had databases all over the place," said one of the officials. "None of them talked to each other."
When we look at Big Data use-cases
Our own customer Neustar was able to eliminate 48 Oracle licenses with next-gen technologiesSimilarly we have many other customers whose first use case in Big Data was to replace commercial RDBMS licenses with HadoopELT/ ETL replacement – One of the Tier 1 investment banks is working on Enterprise wide replacement of a major commercial ETL product with Hadoop based applicationsM & M - Splunk and similar point solutions or tailored use-cases like Splunk – we have helped many companies implement Security: Zions bank - http://www.darkreading.com/security-monitoring/167901086/security/news/232602339/a-case-study-in-security-big-data-analysis.htmlNeeded months or years of data to train ML algo’s to become effective; @ 3TB / week – that could be 100s of TBs and SIEM tools couldn’t handle it. In fact they used to take a day just to load the data. With a fast and effective infrastructure set up and running, Zions uses the data for dozens of purposes. Database logs, firewall, antivirus, IDS logs, plus industry-specific logs like wire ACS deposit applications and credit data are all pulled together into a centralized syslog server.
Our own customer Neustar was able to eliminate 48 Oracle licenses with next-gen technologiesSimilarly we have many other customers whose first use case in Big Data was to replace commercial RDBMS licenses with HadoopELT/ ETL replacement – One of the Tier 1 investment banks is working on Enterprise wide replacement of a major commercial ETL product with Hadoop based applicationsM & M - Splunk and similar point solutions or tailored use-cases like Splunk – we have helped many companies implement Security: Zions bank - http://www.darkreading.com/security-monitoring/167901086/security/news/232602339/a-case-study-in-security-big-data-analysis.htmlNeeded months or years of data to train ML algo’s to become effective; @ 3TB / week – that could be 100s of TBs and SIEM tools couldn’t handle it. In fact they used to take a day just to load the data. With a fast and effective infrastructure set up and running, Zions uses the data for dozens of purposes. Database logs, firewall, antivirus, IDS logs, plus industry-specific logs like wire ACS deposit applications and credit data are all pulled together into a centralized syslog server.
Our own customer Neustar was able to eliminate 48 Oracle licenses with next-gen technologiesSimilarly we have many other customers whose first use case in Big Data was to replace commercial RDBMS licenses with HadoopELT/ ETL replacement – One of the Tier 1 investment banks is working on Enterprise wide replacement of a major commercial ETL product with Hadoop based applicationsM & M - Splunk and similar point solutions or tailored use-cases like Splunk – we have helped many companies implement Security: Zions bank - http://www.darkreading.com/security-monitoring/167901086/security/news/232602339/a-case-study-in-security-big-data-analysis.htmlNeeded months or years of data to train ML algo’s to become effective; @ 3TB / week – that could be 100s of TBs and SIEM tools couldn’t handle it. In fact they used to take a day just to load the data. With a fast and effective infrastructure set up and running, Zions uses the data for dozens of purposes. Database logs, firewall, antivirus, IDS logs, plus industry-specific logs like wire ACS deposit applications and credit data are all pulled together into a centralized syslog server.
Our own customer Neustar was able to eliminate 48 Oracle licenses with next-gen technologiesSimilarly we have many other customers whose first use case in Big Data was to replace commercial RDBMS licenses with HadoopELT/ ETL replacement – One of the Tier 1 investment banks is working on Enterprise wide replacement of a major commercial ETL product with Hadoop based applicationsM & M - Splunk and similar point solutions or tailored use-cases like Splunk – we have helped many companies implement Security: Zions bank - http://www.darkreading.com/security-monitoring/167901086/security/news/232602339/a-case-study-in-security-big-data-analysis.htmlNeeded months or years of data to train ML algo’s to become effective; @ 3TB / week – that could be 100s of TBs and SIEM tools couldn’t handle it. In fact they used to take a day just to load the data. With a fast and effective infrastructure set up and running, Zions uses the data for dozens of purposes. Database logs, firewall, antivirus, IDS logs, plus industry-specific logs like wire ACS deposit applications and credit data are all pulled together into a centralized syslog server.
Our own customer Neustar was able to eliminate 48 Oracle licenses with next-gen technologiesSimilarly we have many other customers whose first use case in Big Data was to replace commercial RDBMS licenses with HadoopELT/ ETL replacement – One of the Tier 1 investment banks is working on Enterprise wide replacement of a major commercial ETL product with Hadoop based applicationsM & M - Splunk and similar point solutions or tailored use-cases like Splunk – we have helped many companies implement Security: Zions bank - http://www.darkreading.com/security-monitoring/167901086/security/news/232602339/a-case-study-in-security-big-data-analysis.htmlNeeded months or years of data to train ML algo’s to become effective; @ 3TB / week – that could be 100s of TBs and SIEM tools couldn’t handle it. In fact they used to take a day just to load the data. With a fast and effective infrastructure set up and running, Zions uses the data for dozens of purposes. Database logs, firewall, antivirus, IDS logs, plus industry-specific logs like wire ACS deposit applications and credit data are all pulled together into a centralized syslog server.
Our own customer Neustar was able to eliminate 48 Oracle licenses with next-gen technologiesSimilarly we have many other customers whose first use case in Big Data was to replace commercial RDBMS licenses with HadoopELT/ ETL replacement – One of the Tier 1 investment banks is working on Enterprise wide replacement of a major commercial ETL product with Hadoop based applicationsM & M - Splunk and similar point solutions or tailored use-cases like Splunk – we have helped many companies implement Security: Zions bank - http://www.darkreading.com/security-monitoring/167901086/security/news/232602339/a-case-study-in-security-big-data-analysis.htmlNeeded months or years of data to train ML algo’s to become effective; @ 3TB / week – that could be 100s of TBs and SIEM tools couldn’t handle it. In fact they used to take a day just to load the data. With a fast and effective infrastructure set up and running, Zions uses the data for dozens of purposes. Database logs, firewall, antivirus, IDS logs, plus industry-specific logs like wire ACS deposit applications and credit data are all pulled together into a centralized syslog server.
Our own customer Neustar was able to eliminate 48 Oracle licenses with next-gen technologiesSimilarly we have many other customers whose first use case in Big Data was to replace commercial RDBMS licenses with HadoopELT/ ETL replacement – One of the Tier 1 investment banks is working on Enterprise wide replacement of a major commercial ETL product with Hadoop based applicationsM & M - Splunk and similar point solutions or tailored use-cases like Splunk – we have helped many companies implement Security: Zions bank - http://www.darkreading.com/security-monitoring/167901086/security/news/232602339/a-case-study-in-security-big-data-analysis.htmlNeeded months or years of data to train ML algo’s to become effective; @ 3TB / week – that could be 100s of TBs and SIEM tools couldn’t handle it. In fact they used to take a day just to load the data. With a fast and effective infrastructure set up and running, Zions uses the data for dozens of purposes. Database logs, firewall, antivirus, IDS logs, plus industry-specific logs like wire ACS deposit applications and credit data are all pulled together into a centralized syslog server.
One who wont
One who wont
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