Dataiku - for Data Geek Paris@Criteo - Close the Data Circle

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The document discusses paradoxes related to data and analytics. It presents five paradoxes: 1) simplicity and patterns, 2) self-perception as a data scientist versus data cleaner, 3) distributed value of data being worth millions while also being sent to the cloud, 4) the size of data fitting in a lake despite living in big data, and 5) the role of machines versus humans with a focus on reports. It also shows closing the data circle between IT and business with predictive tools, applications, and a data science studio using various data sources.

Technology Spiritual

Dataiku = Data + Haiku

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3 verses Poem

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Paradoxal

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Reference Season

PARADOX #5 (at last)
WHERE IS THE HUMAN ?

Close the Data Circle

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Lots
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Data Science Studio

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Hadoop

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SpreadSheets

The Briefing Room with Dr. Robin Bloor, Trifacta and Zoomdata Live Webcast March 10, 2015 Watch the Archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=dd9fed3c7c476ae3a0f881ae6b53dcc5 Square pegs and round holes don't get along, which is one reason why traditional data management approaches simply won't work for Big Data. The variety and velocity of data types flying at us today require a new strategy for identifying, streamlining and utilizing information assets and processes. Decades-old technology won’t cut it – a combination of new tools and techniques must be used to enable effective discovery of insights in a timely fashion. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why today's data landscape calls for a much different data management approach. He'll be briefed by Trifacta and Zoomdata, who will show how their technologies use a range of functionality – including machine learning – to help companies "wrangle" their data. They'll also demonstrate the optimal step-by-step process of working with new data types. Visit InsideAnalysis.com for more information.

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What is Big Data? What is Data Science? What are the benefits? How will they evolve in my organisation? Built around the premise that the investment in big data is far less than the cost of not having it, this presentation made at a tech media industry event, this presentation will unveil and explore the nuances of Big Data and Data Science and their synergy forming Big Data Science. It highlights the benefits of investing in it and defines a path to their evolution within most organisations.

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The data science process seeks to transform and empower organizations by finding and exploiting market inefficiencies and potentially hidden opportunities, but this is often an expensive, tedious process. However, many steps can be automated to provide a streamlined experience for data scientists. Eduardo Arino de la Rubia explores the tools being created by the open source community to free data scientists from tedium, enabling them to work on the high-value aspects of insight creation and impact validation. The promise of the automated statistician is almost as old as statistics itself. From the creations of vast tables, which saved the labor of calculation, to modern tools which automatically mine datasets for correlations, there has been a considerable amount of advancement in this field. Eduardo compares and contrasts a number of open source tools, including TPOT and auto-sklearn for automated model generation and scikit-feature for feature generation and other aspects of the data science workflow, evaluates their results, and discusses their place in the modern data science workflow. Along the way, Eduardo outlines the pitfalls of automated data science and applications of the “no free lunch” theorem and dives into alternate approaches, such as end-to-end deep learning, which seek to leverage massive-scale computing and architectures to handle automatic generation of features and advanced models.

Data science e machine learning

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The Briefing Room with Mark Madsen and Trifacta Live Webcast September 1, 2015 Watch the archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=eb655874d04ba7d560be87a9d906dd2fd Like all enterprise software solutions, Hadoop must deliver business value in order to be a success. Much of the innovation around the big data industry these days therefore addresses usability. While there will always be a technical side to the Hadoop equation, the need for user-friendly tools to manage the data will continue to focus on business users. That’s why self-service data preparation or "data wrangling" is a serious and growing trend, one which promises to move Hadoop beyond the early adopter phase and more into the mainstream of business. Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why business users will play an increasingly important role in the evolution of big data. He’ll be briefed by Trifacta's Will Davis and Alon Bartur, who will demonstrate how Trifacta's solution empowers business users to “wrangle" data of all shapes and sizes faster and easier than ever before. They’ll discuss why a new approach to accessing and preparing diverse data is required and how it can accelerate and broaden the use of big data within organizations. Visit InsideAnalysis.com for more information.

Big Data and the Art of Data Science

Andrew Gardner

Everybody has heard of Big Data, and its promise as the next great frontier for innovation. However, Big Data is neither new nor easily defined. What are the key drivers that make Big Data so critically important today? What is the single idea behind Big Data that promises such game changing outcomes for capable organizations? Who are the skilled talent that deliver Big Data results? This presentation briefly reviews the opportunities, motivation and trends that are driving Big Data disruption. Data science is introduced as the enabling engine for Big Data transformation via the creation of new Data Products. The data scientist is defined and his tools, workflow and challenges are reviewed. Finally, practical tips are presented for approaching data product development. Key takeaways include: - Big Data disruption is driven by four megatrends - Data is the essential raw material for creating valuable Data Products - Data scientists are heterogeneous by role & skill set, but share common tools, workflows and challenges - Data science talent is more important than raw data for Big Data success These slides are modified from an invited presentation for the Gwinnett Chamber of Commerce on March 18, 2014. An excerpt was presented at the Georgia Pacific Social Media Working Session on March 19, 2014.

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Deep Learning and the new wave of AI are inevitably coming to your business area. If you are a manager and if you are trying to make sense of all the buzzwords, this session is four you. We will show you what is Deep Learning in a way that you will understand how it works and how can you apply it. We then expand the scope and apply the deep learning and AI techniques in the Big Data context. You will learn about things that don't work out so well, the risks and challenges in both applying and developing with deep learning and AI technologies. We conclude with practical guidance on how to add the exciting deep learning and AI capabilities to your next project. Outline: - The path to Deep Learning - From machine learning to Deep Learning - But how does it work? - Deep Learning architectures - Deep Learning applications - Deep Learning at scale - Running AI at scale - Deep learning at Scale using Spark - The trouble with AI - Application challenges - Development challenges - How to start your first Deep Learning project

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Q: Can I simply hire one rockstar data scientist to cover all this kind of work? A: No, interdisciplinary work requires teams A: Hire leads who can speak the lingo of each required discipline A: Hire individual contributors who cover 2+ roles, when possible Statistical Thinking – Solve the Whole Problem BONUS: Meta Organization – Integration with Adjacent Teams Co-authors Allen Day @allenday and Paco Nathan @pacoid

Software Engineering for Data Scientists

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Although both disciplines are unique in their own ways, Software Engineering and Data Science make heavy use of programing languages to do their respective jobs. Data Science is a relatively new discipline and many of its practitioners have not previously been professional software engineers. There are a few techniques that Data Scientists can leverage from Software Engineering in order to make their tooling and environments, faster to design, more easily debugged and most importantly, clearer to read. This talk will be going over some practical tips that anyone can use to help better understand their code; give clarity around cloud environments, their uses and drawbacks and finally briefly touching on the Software Development Lifecycle.

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[Presented to the 7th China R Users Conference, Beijing, May 2014.] Adoption of the R language has grown rapidly in the last few years, and is ranked as the number-one data science language in several surveys. This accelerating R adoption curve has been driven by the Big Data revolution, and the fact that so many data scientists — having learned R at university — are actively unlocking the secrets hidden in these new, vast data troves. In more than 6 years of writing for the Revolutions blog, I’ve discovered hundreds of applications of R in business, in government, and in the non-profit sector. Sometimes the use of R is obvious, and sometimes it takes a little bit of detective work to learn how R is operating behind the scenes. In this talk, I’ll begin by presenting some recent statistics on the growth of R. Then I’ll recount some of my favourite applications of R, and show how R is behind some amazing innovations in today’s world.

Lessons from building a stream-first metadata platform | Shirshanka Das, Stealth

HostedbyConfluent

"For data-driven enterprises, the most important objective is unlocking the value of their data. To enable this, data scientists are increasingly turning towards data discovery tools (also known as data catalogs) that can help them locate the right dataset or insight and use it correctly. But are all data catalogs the same? In this talk, I describe how a stream-first architecture was a critical design element that benefited the implementation of our data catalog. We follow the evolution of LinkedIn DataHub’s architecture over the past few years from a simple search tool to a streaming metadata platform that drives productivity and governance workflows across the company. Join this talk to learn: * How different data discovery / catalog tools are architected and the tradeoffs in each kind of architecture * How streaming architectures can benefit metadata * How event-driven metadata architectures can supercharge your data productivity and governance workflows at your company"

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BDS14 Big Data Analytics to the masses

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Slides from my talk at Big Data Spain 2014 in Madrid. In this talk, we will discuss our approach to bring large scale deep analytics to the masses. R is an extremely popular numerical computer environment, but scientific data processing frequently hits its memory limits. On the other hand, system to execute data intensive tasks like Hadoop or Stratosphere are not popular among R users because writing programs using these paradigms is cumbersome. We present an innovative approach to overcome these limitations using the Stratosphere/Apache Flink big data platform by means of a R package and ready-to-use distributed algorithm. This solution allows the user, with small modifications in the R code, to easily execute distributed scenarios using popular machine learning techniques. We will cover the implementation details of the proposed solution including the architecture of the system, the functionality implemented and working examples. In addition, we will cover what are the differences between our approach and other solutions that integrate R with Hadoop or other large-scale analytics systems. Finally, the results of the performance tests show that this solution is competitive with the already existing R implementations for small amounts of data and able to scale-up to gigabyte level.

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The talk presents the evolution of Big-Data systems from single-purpose MapReduce frameworks to fully general computational infrastructures. In particular, I will follow the evolution of Hadoop, and show the benefits and challenges of a new architectural paradigm that decouples the resource management component (YARN) from the specifics of the application frameworks (e.g., MapReduce, Tez, REEF, Giraph, Naiad, Dryad, Spark,...). We argue that beside the primary goals of increasing scalability and programming model flexibility, this transformation dramatically facilitates innovation. In this context, I will present some of our contributions to the evolution of Hadoop (namely: work-preserving preemption, and predictable resource allocation), and comment on the fascinating experience of working on open- source technologies from within Microsoft. The current Hadoop APIs (HDFS and YARN) provide the cluster equivalent of an OS API. With this as a backdrop, I will present our attempt to create the equivalent of stdlib for the cluster: the REEF project. Carlo A. Curino received a PhD from Politecnico di Milano, and spent two years as Post Doc Associate at CSAIL MIT leading the relational cloud project. He worked at Yahoo! Research as Research Scientist focusing on mobile/cloud platforms and entity deduplication at scale. Carlo is currently a Senior Scientist at Microsoft in the Cloud and Information Services Lab (CISL) where he is working on big-data platforms and cloud computing.

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Beyond Predictive Analytics : Deploying apps to production and keep them improving Some smart companies have been putting predictive application in production for decades. Still, either because of lack of sharing or lack of generality, there is still no single and obvious way to put a predictive application in production today. As a consequence, for most companies, transitioning analytics from development to production is still “the next frontier”. Behind the single word "production” lays a great number of questions like: what exactly do you put in production: data, model, code all three ? Who is responsible for maintenance and quality check over time : business, tech or both ? How can I make my predictive app continuously improve and check that it delivers the promised business value over time ? What are the best practice for maintenance and updates by the way ? Will my data scientists keep working after first development or should I lay half of them off ? etc… Let’s make a small analogy with the development of web sites in the 90’s and early 00’s : Back then, the winners where not necessarily the web sites with an amazing design, but a winner had clearly made the necessary efforts and had a robust way to put their web site reliabily in production Today, every web developper can enjoy the confort of Heroku, Amazon, Github, docker, Angular, bootstrap … and so we forget. How much time before we get the same confort for the predictive world ?

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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.

GraphRAG is All You need? LLM & Knowledge Graph

Guy 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/

State of ICS and IoT Cyber Threat Landscape Report 2024 preview

Prayukth 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/

Securing your Kubernetes cluster_ a step-by-step guide to success !

KatiaHIMEUR1

Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster. However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks. In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.

National Security Agency - NSA mobile device best practices

Quotidiano Piemontese

Essentials of Automations: The Art of Triggers and Actions in FME

Safe Software

In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation. We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios. Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Aggregage

Climate Impact of Software Testing at Nordic Testing Days

Kari Kakkonen

My slides at Nordic Testing Days 6.6.2024 Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.

20240607 QFM018 Elixir Reading List May 2024

Matthew Sinclair

The Future of Platform Engineering

Jemma Hussein Allen

PHP Frameworks: I want to break free (IPC Berlin 2024)

Ralf Eggert

In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development. This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.

UiPath Test Automation using UiPath Test Suite series, part 4

DianaGray10

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

Transcript: Selling digital books in 2024: Insights from industry leaders - T...

BookNet Canada

The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more. Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/ Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.