- A modern big data analytics platform like Greenplum's Unified Analytics Platform facilitates self-service, collaborative analytics across structured and unstructured data. This allows organizations to innovate through exploration and experimentation with all their data.
- Traditional data warehouses are inefficient for big data analytics, taking days to load and query data. Greenplum's platform optimizes performance for both structured and unstructured data queries.
- Greenplum's platform and productivity tools like Chorus empower data scientists to quickly explore, visualize, model and share insights from big data in order to uncover new value for organizations.
Big Data Trends and Challenges Report - WhitepaperVasu S
In this whitepaper read How companies address common big data trends & challenges to gain greater value from their data.
https://www.qubole.com/resources/report/big-data-trends-and-challenges-report
Big Data Trends and Challenges Report - WhitepaperVasu S
In this whitepaper read How companies address common big data trends & challenges to gain greater value from their data.
https://www.qubole.com/resources/report/big-data-trends-and-challenges-report
These are the slides from the Gramener webinar conducted on 16-Jan-2020.
- What skills & roles will help you deliver your analytics and data visualization projects?
- What skills do most teams miss to hire for?
In a Gartner survey, CIOs reported 'team skills' as their biggest barrier ⚠️ to data science. They have trouble deciding the skill mix ⚗️needed or in finding the right people for the job.
This webinar will show the skills and roles you must plan for. You will learn how to tailor this based on your organization's data maturity. It will help you decide whether to upskill teams or hire externally. The session will show you how and where to find talent.
Throughout the webinar you will learn:
- Critical skills & roles needed in your data science team?
- Tips for data science hiring. What aspirants should know about the jobs?
- Insights presented using real-world examples
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
O'Reilly ebook: Machine Learning at Enterprise Scale | QuboleVasu S
Real-world data science practitioners offer perspectives and advice on six common Machine Learning problems
https://www.qubole.com/resources/ebooks/oreilly-ebook-machine-learning-at-enterprise-scale
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
EMC Isilon: A Scalable Storage Platform for Big DataEMC
This white paper provides insights into EMC Isilon's shared storage approach, covering a wide range of desired characteristics including increased efficiency and reduced total cost.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
CIO Applications Magazine Names Bardess One of the Top 25 ML Solution Providerschrishems1
The investment Bardess has made in Tangent Works InstantML is critical to the industry to address what we see as a current weakness with all other AutoML technologies which is they rely on brute force (heavy compute effort) to select the best algorithm and hyper parameters, at the expense of rapid results.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Understanding Big Data: Strategies to Re-envision Decision-Making
Amy Mayer, Vice President, Capgemini
Oracle Analytics Leader, North America
Presented at Oracle OpenWorld 2012
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Analytics 3.0 Measurable business impact from analytics & big dataMicrosoft
Presentación del evento de Harvard Business Review sobre Analítica y Big Data
(15 de Octubre 2013)
"Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants" 
These are the slides from the Gramener webinar conducted on 16-Jan-2020.
- What skills & roles will help you deliver your analytics and data visualization projects?
- What skills do most teams miss to hire for?
In a Gartner survey, CIOs reported 'team skills' as their biggest barrier ⚠️ to data science. They have trouble deciding the skill mix ⚗️needed or in finding the right people for the job.
This webinar will show the skills and roles you must plan for. You will learn how to tailor this based on your organization's data maturity. It will help you decide whether to upskill teams or hire externally. The session will show you how and where to find talent.
Throughout the webinar you will learn:
- Critical skills & roles needed in your data science team?
- Tips for data science hiring. What aspirants should know about the jobs?
- Insights presented using real-world examples
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
O'Reilly ebook: Machine Learning at Enterprise Scale | QuboleVasu S
Real-world data science practitioners offer perspectives and advice on six common Machine Learning problems
https://www.qubole.com/resources/ebooks/oreilly-ebook-machine-learning-at-enterprise-scale
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
EMC Isilon: A Scalable Storage Platform for Big DataEMC
This white paper provides insights into EMC Isilon's shared storage approach, covering a wide range of desired characteristics including increased efficiency and reduced total cost.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
CIO Applications Magazine Names Bardess One of the Top 25 ML Solution Providerschrishems1
The investment Bardess has made in Tangent Works InstantML is critical to the industry to address what we see as a current weakness with all other AutoML technologies which is they rely on brute force (heavy compute effort) to select the best algorithm and hyper parameters, at the expense of rapid results.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Understanding Big Data: Strategies to Re-envision Decision-Making
Amy Mayer, Vice President, Capgemini
Oracle Analytics Leader, North America
Presented at Oracle OpenWorld 2012
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Analytics 3.0 Measurable business impact from analytics & big dataMicrosoft
Presentación del evento de Harvard Business Review sobre Analítica y Big Data
(15 de Octubre 2013)
"Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants" 
All of material inside is un-licence, kindly use it for educational only but please do not to commercialize it.
Based on 'ilman nafi'an, hopefully this file beneficially for you.
Thank you.
SBIC Report : Transforming Information Security: Future-Proofing ProcessesEMC
This report from the Security for Business Innovation Council (SBIC), sponsored by RSA, contends that keeping pace with cyber threats requires an overhaul of information-security processes and provides actionable guidance for change.
The Big Data solution from EMC provides market-leading scale-out storage, a unified analytics platform, and business process and application development tools. Together, these allow organizations to draw deeper insights and become a more predictive organization.
CTO Radshow Hamburg17 - Keynote - The CxO responsibilities in Big Data and AI...Santiago Cabrera-Naranjo
When talking about how the future of Big Data will look like, this conversation often turns straight to Artificial Intelligence and Deep Learning. However, today data science is all too often a process where new insights and models get developed as a one-time effort or deployed to production on an ad-hoc basis i.e. they commonly require regular babysitting for monitoring and updating.
According to Gartner, the number of useless Data Lakes will be of 90% in 2018. Furthermore, only 15% of Big Data Products are mature enough to be deployed into Production - Who is responsible to make Big Data successful and Business relevant within an enterprise?
Activating Big Data: The Key To Success with Machine Learning Advanced Analyt...Vasu S
A whitepaper of Qubole that How to make all of your data available to users for a multitude of use cases, ranging from analytics to machine learning and artificial intelligence.
https://www.qubole.com/resources/white-papers/activating-big-data-the-key-to-success-with-machine-learning-advanced-analytics
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and
integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to
seamlessly handle and scale very large amount of unstructured and structured data from diversified and
heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing
component; 3) the ability to automatically select the most appropriate libraries and tools to compute and
accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high
learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different
application problem domains, with high accuracy, robustness, and scalability. This paper highlights the
research methodologies and research activities that we propose to be conducted by the Big Data
researchers and practitioners in order to develop and support seamless automation and integration of
machine learning capabilities for Big Data analytics.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to seamlessly handle and scale very large amount of unstructured and structured data from diversified and heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing component; 3) the ability to automatically select the most appropriate libraries and tools to compute and accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different application problem domains, with high accuracy, robustness, and scalability. This paper highlights the research methodologies and research activities that we propose to be conducted by the Big Data researchers and practitioners in order to develop and support seamless automation and integration of machine learning capabilities for Big Data analytics.
All business sizes can benefit from better use of their data to gain insights, how the cloud can help overcome common data challenges and accelerate transformation with the cloud technology
https://www.rapyder.com/cloud-data-analytics-services/
Operationalizing Big Data to Reduce Risk of High Consequence Decisions in Com...OAG Analytics
This white paper presents compelling alternatives to bivariate analysis, i.e. XY or scatter plots, for generating data-driven insights that can reduce risk in complex systems. It explores under what conditions businesses maximize value by relying on computers to make decisions versus using computers to help humans make better and/or faster decisions. The main body of the paper attempts to create a holistic view of why and how to use contemporary data technologies to create actionable insights from large and complex data. The Technical Appendix elaborates on the requisite capabilities of an end-to-end workflow to transform raw data into actionable insights using advanced analytics.
Go from data to decision in one unified platform.pdfwebmaster553228
According to IDC’s January 2022 Worldwide CEO Survey, 65% of organizations are using at least 10 different data engineering and intelligence tools to integrate data.
Right First Time: the importance of "working the portfolio" onceStatPro Group
Automating process and using data just once - but for many purposes - is increasingly seen as the best way for asset managers to respond to the demand for 24/7 reporting capability.
Activate Your Data Lakehouse with an Enterprise Knowledge GraphDATAVERSITY
Rapid innovation and disruption in the data management space is helping organizations unlock value from data available both inside and outside the enterprise. Organizations operating across physical and digital boundaries are finding new opportunities to serve customers in the way they want to be served. These organizations have done so by harnessing the power of all the data at their disposal. In other words, creating a data-driven culture that looks to democratize data across all aspects of their business functions and operations for richer, faster insights that turn into actionable intelligence at the speed of business.
An Enterprise Knowledge Graph fills that critical gap in existing data management tech stacks. It fits nicely between where data is stored, cataloged, and consumed to eliminate data access barriers, add meaning to data through semantic models, and promote a culture of self-service and self-sufficiency.
Join our session to learn about Enterprise Knowledge Graphs and how they:
- Streamline access to your data with virtualization
- Enrich your data with business meaning using semantic standards
- Identify new connections and insights through inference
- Deliver better data to your existing analytics tools
INDUSTRY-LEADING TECHNOLOGY FOR LONG TERM RETENTION OF BACKUPS IN THE CLOUDEMC
CloudBoost is a cloud-enabling solution from EMC
Facilitates secure, automatic, efficient data transfer to private and public clouds for Long-Term Retention (LTR) of backups. Seamlessly extends existing data protection solutions to elastic, resilient, scale-out cloud storage
Transforming Desktop Virtualization with Citrix XenDesktop and EMC XtremIOEMC
With EMC XtremIO all-flash array, improve
1) your competitive agility with real-time analytics & development
2) your infrastructure agility with elastic provisioning for performance & capacity
3) your TCO with 50% lower capex and opex and double the storage lifecycle.
• Citrix & EMC XtremIO: Better Together
• XtremIO Design Fundamentals for VDI
• Citrix XenDesktop & XtremIO
-- Image Management & Storage
-- Demonstrations
-- XtremIO XenDesktop Integration
EMC FORUM RESEARCH GLOBAL RESULTS - 10,451 RESPONSES ACROSS 33 COUNTRIES EMC
Explore findings from the EMC Forum IT Study and learn how cloud computing, social, mobile, and big data megatrends are shaping IT as a business driver globally.
Reference architecture with MIRANTIS OPENSTACK PLATFORM.The changes that are going on in IT with disruptions from technology, business and culture and so IT to solve the issues has to change from moving from traditional models to broker provider model.
Force Cyber Criminals to Shop Elsewhere
Learn the value of having an Identity Management and Governance solution and how retailers today are benefiting by strengthening their defenses and bolstering their Identity Management capabilities.
Container-based technology has experienced a recent revival and is becoming adopted at an explosive rate. For those that are new to the conversation, containers offer a way to virtualize an operating system. This virtualization isolates processes, providing limited visibility and resource utilization to each, such that the processes appear to be running on separate machines. In short, allowing more applications to run on a single machine. Here is a brief timeline of key moments in container history.
This white paper provides an overview of EMC's data protection solutions for the data lake - an active repository to manage varied and complex Big Data workloads
This infographic highlights key stats and messages from the analyst report from J.Gold Associates that addresses the growing economic impact of mobile cybercrime and fraud.
This white paper describes how an intelligence-driven governance, risk management, and compliance (GRC) model can create an efficient, collaborative enterprise GRC strategy across IT, Finance, Operations, and Legal areas.
The Trust Paradox: Access Management and Trust in an Insecure AgeEMC
This white paper discusses the results of a CIO UK survey on a“Trust Paradox,” defined as employees and business partners being both the weakest link in an organization’s security as well as trusted agents in achieving the company’s goals.
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
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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:
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. BIG DATA,
BIG INNOVATIONS
COLLABORATIVE, SELF-SERVICE ANALYTICS
DELIVERS UNPRECEDENTED VALUE
Forward-looking enterprises know there’s more to big data than storing and
managing large volumes of information. Big data presents an opportunity to
leverage analytics and experiment with all available data to derive value never
before possible with traditional business intelligence and data warehouse
platforms. Through a modern, big data platform that facilitates self-service and
collaborative analytics across all data, organizations become more agile and
are able to innovate in new ways.
“One question that enterprises are often faced with is, ‘Now that I have
big data, what do I do with it?’” says Mike Maxey, senior director of strategy
and corporate development at EMC Greenplum. “Gaining value from big data
requires a new set of tools and processes that promote self-service data
exploration and analysis, along with social features embedded into every step
of the analytical process. Only through collaboration and knowledge sharing
around data sets, predictive model building, results, and best practices can
data science teams evolve to uncover new insights from big data.”
Traditional data warehouse platforms break down
With a traditional data warehouse powering big data, it’s not unusual for
data loads and complex queries to run for days, which hinders the analytical
process. Plus, these data warehouse environments are often designed to
analyze structured data only, and not valuable unstructured data generated
from new external sources such as social media and mobile computing. For
example, Twitter sentiment, GPS location data, web logs, sensor information,
2. WHITE PAPER | BIG DATA, BIG INNOVATIONS 2
and many other forms of unstructured data all add to the
growing body of data that can be used to better understand
customers, manage risk, optimize operations and innovate.
Big data requires a modern platform that is optimized
for high-performance analytics across both structured and
unstructured data. A query that takes 24 hours on a traditional
data warehouse will take seconds on a modern analytics plat-
form. Moreover, a modern analytics platform will have built-in
advanced analytics and data mining services, removing the
lengthy process of copying data from a data warehouse into
a specialized analytics database or desktop application. As
a result, a modern analytics platform delivers more accurate
and timely insight to decision makers who have a direct impact Æ Havas Digital Helps Companies Increase Sales
to the business. With EMC Big Data
At information security company McAfee, providing Hear how EMC big data fundamentally changes the way Havas Digital
customers with timely information on potential security threats is analyzes data to provide better attribution for their clients’ marketing
essential to the business. McAfee’s Security-as-a-Service email efforts. http://youtu.be/kqzrIghWCJk
and web-filtering solution, powered by EMC Greenplum, relies
on capturing and analyzing huge quantities of data and making it
available to customers in a timely manner. Before using this plat- analysts are ready to perform the analysis, the supplied data
form, it would take customer service representatives upwards of is outdated. Additionally, analysts experimenting with data and
30 minutes to be able to answer a customer’s inquiry to find out building predictive models typically work in isolation with frag-
what happened to a certain email. “Now, with EMC Greenplum, mented data sets, without tools that centralize and document
we have the ability to provide that service to a customer directly; insights to facilitate collaboration and knowledge sharing with
they no longer have to call in. They can go to our web portal peers, IT and the business. As a result, predictive models are
and identify within seconds what happened to [the email] as it not fully optimized and valuable insight is hidden and cannot
was flowing through our system,” says Keaton Adams, principal be reused across the organization.
enterprise data engineer at McAfee. This not only improves “If someone in marketing built a predictive model for product
customer service for McAfee clients, but also allows the provider recommendations, this insight and knowledge most likely is not
to automate tasks that used to require human intervention. shared with, let’s say, operations, which could leverage these
best practices and methodologies to build a predictive model to
The advent of agile analytics detect fraud on the network,” EMC’s Maxey says.
Big data also demands an approach to analytics that is flexible, To maximize the value of big data and impact business,
accessible and fast. Traditional business intelligence tools provide analysts need a productivity tool to quickly provision their own
sophisticated analytics and data mining capabilities; however, sandboxes, and use their tool of choice to perform analysis,
these tools tend to be rigid and hinder the analytical process. collaborate and share insights, and iterate on the entire
With traditional tools, analysts must request from IT the process continuously.
desired data sets needed to answer a question, and IT must Havas Digital Global, a media planning and buying company,
create a reporting environment in which the analysis can be relies on EMC Greenplum to deliver innovative products and
performed–a long and inflexible procedure. By the time the services in a highly competitive industry. Being able to quickly
“Groups of data scientists in multiple countries are able to come together
on the development of Artemis, Havas Digital’s analytics platform built on
EMC Greenplum. This enables faster provisioning of sandboxes and better
collaboration around testing and refinement of analysis and new analytics.”
— Katrin Ribant, EVP, Data Platforms, Havas Digital Global
3. WHITE PAPER | BIG DATA, BIG INNOVATIONS 3
Greenplum Unified
Analytics Platform
(UAP)
Greenplum Unified Analytics Platform
combines the co-processing of
structured and unstructured data with
a productivity engine that enables
collaboration among your data
science team.
experiment with data and collaborate around results allows services with EMC Greenplum.
Havas Digital Global to better optimize marketing efforts Because big data is an emerging field, there is a shortage of
across the web. data scientists. Fortunately, there are several education courses
“Groups of data scientists in multiple countries are able to offered by big data technology vendors and academic institu-
come together on the development of Artemis, Havas Digital’s tions to help expand the pool of available data scientists. What’s
analytics platform built on EMC Greenplum. This enables faster more impactful is an analytics platform that facilitates growth of
provisioning of sandboxes and better collaboration around the data scientist community through knowledge sharing and
testing and refinement of analysis and new analytics,” says collaboration that will ultimately bridge the talent gap.
Katrin Ribant, EVP for data platforms at Havas Digital Global.
EMC’s answer to big data analytics
Disruptive data science With the right platforms, tools and expertise in place, enterprises
While having the right platform and tools in place to perform can develop big data analytics strategies that deliver results.
big data analytics is important, so is securing the right people. EMC Greenplum has nearly a decade of experience in devel-
Big data and the technologies that support it require a new oping products and services for data-driven organizations that
breed of professionals called data scientists, who possess skills rely on high-performance analytics for business advantage. Big
and expertise that go beyond business intelligence. A data data analytics is simply an extension to what EMC Greenplum
scientist has a diverse skill set that combines statistical knowl- has always focused on, integrating new technologies around
edge and programming expertise with business acumen and data science and Apache Hadoop to address big data analytics
communication capabilities. But most important, a data scien- challenges so you can unlock insight from all your data sources.
tist is a change agent with a passion for working with different
stakeholders across the organization to solve problems–made Greenplum Unified Analytics
possible by gaining insights from new data sources. Platform (UAP)
“The data scientist is someone who can take raw forms Greenplum UAP provides the first and only modern analytics
of structured and unstructured data, internally and/or exter- platform that unifies all your data, with a productivity layer that
nally sourced, and apply advanced analytical techniques to facilitates self-service and collaboration among data science
uncover monetize-able and operationalize-able insights,” teams. Through the integration of the following three compo-
says Annika Jimenez, senior director of data science nents, data scientists can quickly start experimenting with both
“A major part of big data analytics is getting teams productive early in the
process followed by rapid iterations. Greenplum delivers freedom of tool selec-
tion combined with Chorus for collaboration to enable rapid analytics at scale.”
— Josh Klahr, VP of product management, EMC Greenplum
4. WHITE PAPER | BIG DATA, BIG INNOVATIONS 4
structured and unstructured data, linking the data sets together
to find insights never before possible.
• Greenplum Database for structured data —an MPP
database built for large-scale analytics processing and
data loading, allowing for the consolidation and analysis of
structured data stored in relational databases.
• Greenplum HD for unstructured data—provides a
complete and enterprise-ready version of Apache Hadoop,
resulting in faster deployment, management and analysis
of unstructured data.
• Greenplum Chorus for agility—a productivity layer that Æ The Greenplum Unified Analytics Platform
streamlines and centralizes the analytics process. Data Big data demands an approach to analytics that is flexible, accessible
science teams are able to quickly search, explore, visualize and fast. J. Klahr, VP of products at EMC Greenplum, presents on deliv-
ering agile analytics with Greenplum UAP. He also explains how Chorus
and import data from anywhere, including Twitter data
empowers data scientist to easily collaborate.
through Gnip integration. It provides rich social network http://youtu.be/KVlbjbOiMB0
features to connect with peers and expert Kaggle data
scientists, empowering everyone who works with data to
more easily collaborate and derive new insight from data.
Conclusion
Greenplum UAP goes a step further with data science produc- Big data offers the opportunity to change the way enterprises
tivity through an open data access and query layer, enabling think about their business today. But getting to the true benefits
analysts to use the language and tool of choice–including SQL, requires more than simply collecting and storing new and
MapReduce, R, SAS, MicroStrategy and Informatica, to name a different types of information; it demands a fresh approach to
few. “A major part of big data analytics is getting teams produc- analysis that is fast, self-service and collaborative. It also needs
tive early in the process followed by rapid iterations. Greenplum a team of data scientists who are empowered, and have the
delivers freedom of tool selection combined with Chorus for vision and commitment to work with business leaders and IT to
collaboration to enable rapid analytics at scale,” says Josh Klahr, successfully deliver on the value of big data.
VP of product management for EMC Greenplum.
In addition to Greenplum Chorus facilitating the growth of Next Steps
the data science community, EMC offers services and training To learn more about EMC’s big data and analytics solutions, and
solutions to help organizations address their skills gap in an how they can help transform business, please visit:
efficient and cost-effective manner:
www.EMC.com/BigData
• Greenplum Analytics Lab brings Greenplum data scien- www.Greenplum.com
tists on-site to help develop an analytics roadmap and kick-
start analytics projects. By combining services, training and,
in some cases, hardware and software, these unique labs
partner with analysts, data platform administrators and busi-
ness leadership to solve top business challenges and find
new opportunities in data, all on an accelerated schedule.
• The EMC Data Science Curriculum is a five-day data
science and big data analytics training and certification
designed to enable immediate and effective participation
in big data and other analytics projects. Developed by
data scientists and practitioners who have worked with
a breadth of tools ranging from open source to vendor-
specific tools, students learn how to approach the analytics
lifecycle using common tools in the marketplace.