The document discusses using Hadoop as a data hub. It describes how a data hub allows organizations to source data once and reuse it many times, eliminating the need for complex and costly ETL processes. Key benefits of a Hadoop data hub include making all data available with low latency, empowering business users with self-service access, and reducing IT costs through a "source once, reuse many times" approach. The document also provides an overview of how Sears has implemented a Hadoop data hub to modernize their legacy systems and analytics capabilities.
Extracting value from Big Data is not easy. The field of technologies and vendors is fragmented and rapidly evolving. End-to-end, general purpose solutions that work out of the box don’t exist yet, and Hadoop is no exception. And most companies lack Big Data specialists. The key to unlocking real value /// extracting the gold nuggets at the end of the rainbow (???) /// lies with mapping the business requirements smartly against the emerging and imperfect ecosystem of technology and vendor choices.
There is a long list of crucial questions to think about. How fast is the data flying at you? Are your Big Data analyses tightly integrated with existing systems? Or parallel and complex? Can you tolerate a minute of latency? Do you accept data loss or generous SLAs? Is imperfect security good enough?
The answer to Big Data ROI lies somewhere between the herd and nerd mentality. Thinking hard and being smart about each use case as early as possible avoids costly mistakes.
This talk will illustrate how Deutsche Telekom follows this segmentation approach to make sure every individual use case drives architecture design and technology selection.
Hadoop Analytics + Enterprise Class Storage: One-Stop Solution From EMC for H...EMC
Using Greenplum HD, Isilon Scale-Out NAS and EMC services, learn how you can quickly and easily deploy a powerful, yet worry-free Hadoop-based analytics engine. If you ever desired to take the plunge with Hadoop or wanted the confidence to grow your Hadoop deployment for full-scale production, learn how EMC can provide you the tested solution to do so.
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Extracting value from Big Data is not easy. The field of technologies and vendors is fragmented and rapidly evolving. End-to-end, general purpose solutions that work out of the box don’t exist yet, and Hadoop is no exception. And most companies lack Big Data specialists. The key to unlocking real value /// extracting the gold nuggets at the end of the rainbow (???) /// lies with mapping the business requirements smartly against the emerging and imperfect ecosystem of technology and vendor choices.
There is a long list of crucial questions to think about. How fast is the data flying at you? Are your Big Data analyses tightly integrated with existing systems? Or parallel and complex? Can you tolerate a minute of latency? Do you accept data loss or generous SLAs? Is imperfect security good enough?
The answer to Big Data ROI lies somewhere between the herd and nerd mentality. Thinking hard and being smart about each use case as early as possible avoids costly mistakes.
This talk will illustrate how Deutsche Telekom follows this segmentation approach to make sure every individual use case drives architecture design and technology selection.
Hadoop Analytics + Enterprise Class Storage: One-Stop Solution From EMC for H...EMC
Using Greenplum HD, Isilon Scale-Out NAS and EMC services, learn how you can quickly and easily deploy a powerful, yet worry-free Hadoop-based analytics engine. If you ever desired to take the plunge with Hadoop or wanted the confidence to grow your Hadoop deployment for full-scale production, learn how EMC can provide you the tested solution to do so.
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Debunking Common Myths of Hadoop Backup & Test Data ManagementImanis Data
These slides are from a webinar where Hari Mankude, CTO at Talena, discussed key concepts associated with Hadoop data management processes around scalable backup, recovery and test data management.
Presentación sobre la futura base de datos 18c, en la cual se incorpora todo lo mejor de las tecnologías Oracle, perfilando así una base de datos autónoma.
File Server and Storage Consolidation in the CloudBuurst
Consolidating your file servers in AWS or Azure cloud can be a difficult and complicated task, but the rewards can outweigh the hassle. In this deck, we cover:
- The state of the file server market today
- How to conquer unstructured data
- Benefits of file consolidation in the cloud
- Real customer use cases
Extreme Sports & Beyond: Exploring a new frontier in data with GoProCloudera, Inc.
GoPro is a powerful global brand, thanks in large part to its innovative cameras and accessories that capture moments other cameras just miss: surfing in Maui, skiing in Tahoe, recording your child’s first steps. And today, the company is nearly as well known for its user-generated social and content networks.
Join us for this special webinar hosted by Tableau, Trifacta, and Cloudera—featuring GoPro. We’ll dive into GoPro’s data strategy and architecture, from ingest and processing to data prep and reporting, all on AWS.
3 Things to Learn About:
* How Sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory
* How Sparklyr llows data scientists to use dplyr to translate R code into Spark SQL
* How Sparklyr supports MLlib so data scientists can run classifiers, regressions, and many other machine learning algorithms in Spark
Should I move my database to the cloud?James Serra
So you have been running on-prem SQL Server for a while now. Maybe you have taken the step to move it from bare metal to a VM, and have seen some nice benefits. Ready to see a TON more benefits? If you said “YES!”, then this is the session for you as I will go over the many benefits gained by moving your on-prem SQL Server to an Azure VM (IaaS). Then I will really blow your mind by showing you even more benefits by moving to Azure SQL Database (PaaS/DBaaS). And for those of you with a large data warehouse, I also got you covered with Azure SQL Data Warehouse. Along the way I will talk about the many hybrid approaches so you can take a gradual approve to moving to the cloud. If you are interested in cost savings, additional features, ease of use, quick scaling, improved reliability and ending the days of upgrading hardware, this is the session for you!
Introduction to Designing and Building Big Data ApplicationsCloudera, Inc.
Learn what the course covers, from capturing data to building a search interface; the spectrum of processing engines, Apache projects, and ecosystem tools available for converged analytics; who is best suited to attend the course and what prior knowledge you should have; and the benefits of building applications with an enterprise data hub.
The flash market started out monolithically. Flash was a single media type (high performance, high endurance SLC flash). Flash systems also had a single purpose of accelerating the response time of high-end databases. But now there are several flash options. Users can choose between high performance flash or highly dense, medium performance flash systems. At the same time, high capacity hard disk drives are making a case to be the archival storage medium of choice. How does an IT professional choose?
The way we store and manage data is changing. In the old days, there were only a handful of file formats and databases. Now there are countless databases and numerous file formats. The methods by which we access the data has also increased in number. As R users, we often access and analyze data in highly inefficient ways. Big Data tech has solved some of those problems.
This presentation will take attendees on a quick tour of the various relevant Big Data technologies. I’ll explain how these technologies fit together to form a stack for various data analysis uses cases. We’ll talk about what these technologies mean for the future of analyzing data with R.
Even if you work with “small data” this presentation will still be of interest because some Big Data tech has a small data use case.
Discusses what to consider when writing a facial recognition application and how to scale it on multiple nodes using Spark. The approach discusses tools like OpenCV and dlib for traditional approaches and Tensorflow for inference to create embeddings\features.
Hybrid Data Warehouse Hadoop ImplementationsDavid Portnoy
Data Warehouse vendors are evolving to incorporate the best Hadoop has to offer. Similarly, the Hadoop ecosystem is growing to include capabilities previously available only to large scale (MPP) DW platforms.
Cloudera Data Science Workbench: sparklyr, implyr, and More - dplyr Interfac...Cloudera, Inc.
You like to use R, and you need to use big data. dplyr, one of the most popular packages for R, makes it easy to query large data sets in scalable processing engines like Apache Spark and Apache Impala.
But there can be pitfalls: dplyr works differently with different data sources—and those differences can bite you if you don’t know what you’re doing.
Ian Cook is a data scientist, an R contributor, and a curriculum developer at Cloudera University. In this webinar, Ian will show you exactly what you need to know about sparklyr (from RStudio) and the package implyr (from Cloudera). He will show you how to write dplyr code that works across these different interfaces. And, he will solve mysteries:
Do I need to know SQL to use dplyr?
When is a “tbl” not a “tibble”?
Why is 1 not always equal to 1?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
3 things to learn:
Do I need to know SQL to use dplyr?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
Debunking Common Myths of Hadoop Backup & Test Data ManagementImanis Data
These slides are from a webinar where Hari Mankude, CTO at Talena, discussed key concepts associated with Hadoop data management processes around scalable backup, recovery and test data management.
Presentación sobre la futura base de datos 18c, en la cual se incorpora todo lo mejor de las tecnologías Oracle, perfilando así una base de datos autónoma.
File Server and Storage Consolidation in the CloudBuurst
Consolidating your file servers in AWS or Azure cloud can be a difficult and complicated task, but the rewards can outweigh the hassle. In this deck, we cover:
- The state of the file server market today
- How to conquer unstructured data
- Benefits of file consolidation in the cloud
- Real customer use cases
Extreme Sports & Beyond: Exploring a new frontier in data with GoProCloudera, Inc.
GoPro is a powerful global brand, thanks in large part to its innovative cameras and accessories that capture moments other cameras just miss: surfing in Maui, skiing in Tahoe, recording your child’s first steps. And today, the company is nearly as well known for its user-generated social and content networks.
Join us for this special webinar hosted by Tableau, Trifacta, and Cloudera—featuring GoPro. We’ll dive into GoPro’s data strategy and architecture, from ingest and processing to data prep and reporting, all on AWS.
3 Things to Learn About:
* How Sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory
* How Sparklyr llows data scientists to use dplyr to translate R code into Spark SQL
* How Sparklyr supports MLlib so data scientists can run classifiers, regressions, and many other machine learning algorithms in Spark
Should I move my database to the cloud?James Serra
So you have been running on-prem SQL Server for a while now. Maybe you have taken the step to move it from bare metal to a VM, and have seen some nice benefits. Ready to see a TON more benefits? If you said “YES!”, then this is the session for you as I will go over the many benefits gained by moving your on-prem SQL Server to an Azure VM (IaaS). Then I will really blow your mind by showing you even more benefits by moving to Azure SQL Database (PaaS/DBaaS). And for those of you with a large data warehouse, I also got you covered with Azure SQL Data Warehouse. Along the way I will talk about the many hybrid approaches so you can take a gradual approve to moving to the cloud. If you are interested in cost savings, additional features, ease of use, quick scaling, improved reliability and ending the days of upgrading hardware, this is the session for you!
Introduction to Designing and Building Big Data ApplicationsCloudera, Inc.
Learn what the course covers, from capturing data to building a search interface; the spectrum of processing engines, Apache projects, and ecosystem tools available for converged analytics; who is best suited to attend the course and what prior knowledge you should have; and the benefits of building applications with an enterprise data hub.
The flash market started out monolithically. Flash was a single media type (high performance, high endurance SLC flash). Flash systems also had a single purpose of accelerating the response time of high-end databases. But now there are several flash options. Users can choose between high performance flash or highly dense, medium performance flash systems. At the same time, high capacity hard disk drives are making a case to be the archival storage medium of choice. How does an IT professional choose?
The way we store and manage data is changing. In the old days, there were only a handful of file formats and databases. Now there are countless databases and numerous file formats. The methods by which we access the data has also increased in number. As R users, we often access and analyze data in highly inefficient ways. Big Data tech has solved some of those problems.
This presentation will take attendees on a quick tour of the various relevant Big Data technologies. I’ll explain how these technologies fit together to form a stack for various data analysis uses cases. We’ll talk about what these technologies mean for the future of analyzing data with R.
Even if you work with “small data” this presentation will still be of interest because some Big Data tech has a small data use case.
Discusses what to consider when writing a facial recognition application and how to scale it on multiple nodes using Spark. The approach discusses tools like OpenCV and dlib for traditional approaches and Tensorflow for inference to create embeddings\features.
Hybrid Data Warehouse Hadoop ImplementationsDavid Portnoy
Data Warehouse vendors are evolving to incorporate the best Hadoop has to offer. Similarly, the Hadoop ecosystem is growing to include capabilities previously available only to large scale (MPP) DW platforms.
Cloudera Data Science Workbench: sparklyr, implyr, and More - dplyr Interfac...Cloudera, Inc.
You like to use R, and you need to use big data. dplyr, one of the most popular packages for R, makes it easy to query large data sets in scalable processing engines like Apache Spark and Apache Impala.
But there can be pitfalls: dplyr works differently with different data sources—and those differences can bite you if you don’t know what you’re doing.
Ian Cook is a data scientist, an R contributor, and a curriculum developer at Cloudera University. In this webinar, Ian will show you exactly what you need to know about sparklyr (from RStudio) and the package implyr (from Cloudera). He will show you how to write dplyr code that works across these different interfaces. And, he will solve mysteries:
Do I need to know SQL to use dplyr?
When is a “tbl” not a “tibble”?
Why is 1 not always equal to 1?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
3 things to learn:
Do I need to know SQL to use dplyr?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
Is Mobile the Prescription for Sustained Behavior Change?HealthInnoventions
This white paper provides an overview of behavior change filtered through the lens of health and financial imperatives, systems thinking and evolving portable technologies. Health Innoventions’ authors and conference organizers (Max Wells and Michael Gallelli) suggest that a confluence of demands and growing dynamic and interactive capabilities will drive us to better science and application of behavior change and maintenance. It was prepared as a companion document to the conference Consumer-Centric Health: MODELS FOR CHANGE '11, which took place on October 12-13 in Seattle.
Hadoop Essentials -- The What, Why and How to Meet Agency ObjectivesCloudera, Inc.
This session will provide an executive overview of the Apache Hadoop ecosystem, its basic concepts, and its real-world applications. Attendees will learn how organizations worldwide are using the latest tools and strategies to harness their enterprise information to solve business problems and the types of data analysis commonly powered by Hadoop. Learn how various projects make up the Apache Hadoop ecosystem and the role each plays to improve data storage, management, interaction, and analysis. This is a valuable opportunity to gain insights into Hadoop functionality and how it can be applied to address compelling business challenges in your agency.
The Transformation of your Data in modern IT (Presented by DellEMC)Cloudera, Inc.
Organizations have a wealth of data contained within the existing infrastructures. At DellEMC we’re helping customers remove the barriers of legacy datastores and transforming the customer experience in the modern datacentre. Learn how to unshackle the valuable data inside your existing data warehouse, leverage new techniques, applications and technology to enhance the financial impact of all your data sources
5 Things that Make Hadoop a Game Changer
Webinar by Elliott Cordo, Caserta Concepts
There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop.
To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029
For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/
Enterprise Hadoop is Here to Stay: Plan Your Evolution StrategyInside Analysis
The Briefing Room with Neil Raden and Teradata
Live Webcast on August 19, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=1acd0b7ace309f765dc3196001d26a5e
Modern enterprises have been able to solve information management woes with the data warehouse, now a staple across the IT landscape that has evolved to a high level of sophistication and maturity with thousands of global implementations. Today’s modern enterprise has a similar challenge; big data and the fast evolution of the Hadoop ecosystem create plenty of new opportunities but also a significant number of operational pains as new solutions emerge.
Register for this episode of The Briefing Room to hear veteran Analyst Neil Raden as he explores the details and nature of Hadoop’s evolution. He’ll be briefed by Cesar Rojas of Teradata, who will share how Teradata solves some of the Hadoop operational challenges. He will also explain how the integration between Hadoop and the data warehouse can help organizations develop a more responsive and robust data management environment.
Visit InsideAnlaysis.com for more information.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Hadoop and the Data Warehouse: Point/Counter PointInside Analysis
Robin Bloor and Teradata
Live Webcast on April 22, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=2e69345c0a6a4e5a8de6fc72652e3bc6
Can you replace the data warehouse with Hadoop? Is Hadoop an ideal ETL subsystem? And what is the real magic of Hadoop? Everyone is looking to capitalize on the insights that lie in the vast pools of big data. Generating the value of that data relies heavily on several factors, especially choosing the right solution for the right context. With so many options out there, how do organizations best integrate these new big data solutions with the existing data warehouse environment?
Register for this episode of The Briefing Room to hear veteran analyst Dr. Robin Bloor as he explains where Hadoop fits into the information ecosystem. He’ll be briefed by Dan Graham of Teradata, who will offer perspective on how Hadoop can play a critical role in the analytic architecture. Bloor and Graham will interactively discuss big data in the big picture of the data center and will also seek to dispel several common misconceptions about Hadoop.
Visit InsideAnlaysis.com for more information.
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
Cloudera Federal Forum 2014: Hadoop's Impact on the Future of Data ManagementCloudera, Inc.
Chief Strategy Officer, Chairman and Founder of Cloudera Mike Olson, shares thoughts on the future of data management and how it relates to the public sector.
Complement Your Existing Data Warehouse with Big Data & HadoopDatameer
To view the full webinar, please go to: http://info.datameer.com/Slideshare-Complement-Your-Existing-EDW-with-Hadoop-OnDemand.html
With 40% yearly growth in data volumes, traditional data warehouses have become increasingly expensive and challenging.
Much of today’s new data sources are unstructured, making the structured data warehouse an unsuitable platform for analyses. As a result, organizations now look at Hadoop as a data platform to complement existing BI data warehouses, and a scalable, flexible and cost-effective solution for data storage and analysis.
Join Datameer and Cloudera in this webinar to discuss how Hadoop and big data analytics can help to:
-Get all the data your business needs quickly into one environment
Shorten the time to insight from months to days
Extend the life of your existing data warehouse investments
Enable your business analysts to ask and answer bigger questions
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
Tame Big Data with Oracle Data IntegrationMichael Rainey
In this session, Oracle Product Management covers how Oracle Data Integrator and Oracle GoldenGate are vital to big data initiatives across the enterprise, providing the movement, translation, and transformation of information and data not only heterogeneously but also in big data environments. Through a metadata-focused approach for cataloging, defining, and reusing big data technologies such as Hive, Hadoop Distributed File System (HDFS), HBase, Sqoop, Pig, Oracle Loader for Hadoop, Oracle SQL Connector for Hadoop Distributed File System, and additional big data projects, Oracle Data Integrator bridges the gap in the ability to unify data across these systems and helps deliver timely and trusted data to analytic and decision support platforms.
Co-presented with Alex Kotopoulis at Oracle OpenWorld 2014.
Optimizing your Modern Data Architecture - with Attunity, RCG Global Services...Hortonworks
Today’s enterprises are challenged with capturing large amounts of data from a number of sources in a variety of formats, and then storing it in a cost-effective, timely manner. With your current data warehouse, this may seem overwhelming. It doesn’t have to be. With a Hadoop-based modern data warehouse, you can overcome these challenges and get meaningful insights from real-time data.
Want to learn how? Join experts from Attunity, Hortonworks, and RCG Global Services for a live webinar - where we will be discussing enterprise data warehouse optimization. You will learn how to:
•Rebalance your data warehouse by identifying unused data and resource-intensive workloads that can be moved to Hadoop.
•Seamlessly integrate your current enterprise data warehouse with a Modern Data Architecture.
•Better utilize data assets to reduce costs while realizing more value from your data.
•Develop a roadmap for implementing the Hadoop-based Modern Data Architecture and Data Lake.
When Databases Meet Big data and Hadoop - Uni of Tromso Online LectureIrfan Elahi
Slides of my online lecture that I delivered to the grad students of University of Tromsø (Norway) about
"When Databases Meet Big Data - Expectations, Challenges and Opportunities"
on 13/09/2018.
The lecture provided an overview of what databases have been used for traditionally and with the rise of big data paradigms, what expectations do enterprises and organizations have now from them. With the shift from vertical scaling to horizontal scaling, what challenges germinate in the context of functional capabilities of databases and how does it all align with the expectations from big data platforms which are increasingly being considered for use-cases like ETL offloading and scalable data warehousing. Lastly, what opportunities lie in this niche and what lies beyond.
Transforming Data Architecture Complexity at Sears - StampedeCon 2013StampedeCon
At the StampedeCon 2013 Big Data conference in St. Louis, Justin Sheppard discussed Transforming Data Architecture Complexity at Sears. High ETL complexity and costs, data latency and redundancy, and batch window limits are just some of the IT challenges caused by traditional data warehouses. Gain an understanding of big data tools through the use cases and technology that enables Sears to solve the problems of the traditional enterprise data warehouse approach. Learn how Sears uses Hadoop as a data hub to minimize data architecture complexity – resulting in a reduction of time to insight by 30-70% – and discover “quick wins” such as mainframe MIPS reduction.
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/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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/
9. What is a Data Hub
A single, consolidated, fully
populated data archive that
gives unfettered user access to
analyze and report on data, with
appropriate security, as soon as
the data is created by the
transactional or other source
system
10. Why a Data Hub
• Most data latency is removed
• Users and analysts are put in a self-service mode
• The concept of a “data cube” is unnecessary
• Analysis at the lowest level – No need to run at the segment level
• Any question can be asked
• Business users and analysts have unrestricted ability to explore
• Correlation of any data set is immediately possible
• Significant reduction in reporting and analysis times
– Time to source the data
– Time for users to gain access to the data
• Reduction in IT labor ….
– Source Once – Use Many Times
11. • Data is Copied from source systems via ETL
• Sub-sets of data are captured
– Too expensive to keep all detail
– Takes too long to ETL all data fields from sources
• Each use of data generates more unique ETL jobs
• Data is segmented to reduce query times
• Cubes or views are generated to improve analysis speed
• Disparate data silos required ETL before users have access
• Data warehouse costs and performance limitations force
archiving and data truncation
• Tends to lead to different versions of “truth”
• Time lag or latency from data generation to use
The Traditional Approach
12. Benefits - Hadoop as a Data Hub
• All data is available
– All history
– All detail
• No need to filter, segment or cube before use
• Data can be consumed almost immediately
• No need to silo into different databases to
accommodate performance limitations
• Users do not require IT to ETL data before use
• Security is applied via Datameer profiles
• User self-service is a reality
13. Prerequisites
• An Enterprise data architecture that has a Data
Hub as a foundation
• Data sourcing must be controlled
• Metadata must be created for data sources
• A leader with the vision and capability to drive
• Willing business users to pilot and coach others
• A sustained strategy to Enterprise Data
Architecture and governance
• A carefully designed Hadoop data layer
architecture
14. Key Concepts
• A Data Hub is now reality
• Drives lower costs and reduces delays
• Time to value for data is reduced
• Business users and analysts are empowered
• The most important:
– Source Once – Re-use Many Times
– Source everything
– Retain everything
15. o ETL complexity is needed no-longer – DATA HUB
– Source Once – Re-Use many times
– ETL is transformed to ELTTTTTT with lower data latency
– Consume data in-place with Datameer
o ETL-induced data latency is largely eliminated
– Analysis is routinely possible within minutes of data creation
o Long-running overnight workload on Legacy Systems
– Can be eliminated and executed at any time
– Run times are a fraction of the original clock-time
o Batch processing on mainframes or other conventional batch
– Moved to Hadoop
– Run 10, 50, even 100 times faster.
o Intelligent Archive
– Put your archives/tape data on Hadoop and make it Intelligent
– Archive with the ability to run analytics or join it with other data
o Modernize Legacy
– Mainframe MIPs reduction has very attractive ROI
– Move Data Warehouse workload – Reduce Cost – Go Faster
Key Learning
