Redefine Triage by Learning the Golden Nuggets of APM From Noted "APM Best Pr...CA Technologies
Increase your APM proficiency. Learn how you can identify and harness KPIs to make sense of your APM "big data." And find out how these techniques will help to prepare for your upgrade to the new features and functionality with latest APM release.
For more information on DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX
Outthink: machines coping with humans. A journey into the cognitive world - E...Codemotion
How changed the the Application Development's world from Apollo 11 to 2016? Exceeds the limits of code and allow you app to innovate your business. Intelligent Machine (Robot), Device which communicate and drone which fly but the core it's always the cognitive development. Cognitive Development: allow you application to solve new issue and innovate your business. Your application innovates your business outthik code limit.
Data Warehouse vs. Live Datamart - Comparison and DifferencesKai Wähner
Data Warehouses have existed for many years in almost every company. While they are still as good and relevant for the same use cases as they were 20 years ago, they cannot solve new, existing challenges and those sure to come in a ever-changing digital world. The upcoming sections will clarify when to still use a Data Warehouse and when to use a modern Live Datamart instead.
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
How to Apply Machine Learning with R, H20, Apache Spark MLlib or PMML to Real...Kai Wähner
"Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud.
"Fast Data" via stream processing is the solution to embed patterns - which were obtained from analyzing historical data - into future transactions in real-time. This session uses several real world success stories to explain the concepts behind stream processing and its relation to Hadoop and other big data platforms. The session discusses how patterns and statistical models of R, Spark MLlib and other technologies can be integrated into real-time processing using open source frameworks (such as Apache Storm, Spark or Flink) or products (such as IBM InfoSphere Streams or TIBCO StreamBase). A live demo shows the complete development lifecycle combining analytics, machine learning and stream processing.
Redefine Triage by Learning the Golden Nuggets of APM From Noted "APM Best Pr...CA Technologies
Increase your APM proficiency. Learn how you can identify and harness KPIs to make sense of your APM "big data." And find out how these techniques will help to prepare for your upgrade to the new features and functionality with latest APM release.
For more information on DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX
Outthink: machines coping with humans. A journey into the cognitive world - E...Codemotion
How changed the the Application Development's world from Apollo 11 to 2016? Exceeds the limits of code and allow you app to innovate your business. Intelligent Machine (Robot), Device which communicate and drone which fly but the core it's always the cognitive development. Cognitive Development: allow you application to solve new issue and innovate your business. Your application innovates your business outthik code limit.
Data Warehouse vs. Live Datamart - Comparison and DifferencesKai Wähner
Data Warehouses have existed for many years in almost every company. While they are still as good and relevant for the same use cases as they were 20 years ago, they cannot solve new, existing challenges and those sure to come in a ever-changing digital world. The upcoming sections will clarify when to still use a Data Warehouse and when to use a modern Live Datamart instead.
Deep Learning Image Processing Applications in the EnterpriseGanesan Narayanasamy
The presentation has many use cases covering the following Image classification: "The process of identifying and detecting an object or a feature in a digital image or video," the report states. In retail, deep learning models "quickly scan and analyze in-store imagery to intuitively determine inventory movement."
Voice recognition: "The ability to receive and interpret dictation or to understand and carry out spoken commands. Models are able to convert captured voice commands to text and then use natural language processing to understand what is being said and in what context." In transportation, deep learning "uses voice commands to enable drivers to make phone calls and adjust internal controls - all without taking their hands off the steering wheel."
Anomaly detection: "Deep learning technique strives to recognize abnormal patterns which don't match the behaviors expected for a particular system, out of millions of different transactions. These applications can lead to the discovery of an attack on financial networks, fraud detection in insurance filings or credit card purchases, even isolating sensor data in industrial facilities signifying a safety issue."
Recommendation engines: "Analyze user actions in order to provide recommendations based on user behavior."
Sentiment analysis: "Leverages deep learning-heavy techniques such as natural language processing, text analysis, and computational linguistics to gain clear insight into customer opinion, understanding of consumer sentiment, and measuring the impact of marketing strategies."
Video analysis: "Process and evaluate vast streams of video footage for a range of tasks including threat detection, which can be used in airport security, banks, and sporting events."
How to Apply Machine Learning with R, H20, Apache Spark MLlib or PMML to Real...Kai Wähner
"Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud.
"Fast Data" via stream processing is the solution to embed patterns - which were obtained from analyzing historical data - into future transactions in real-time. This session uses several real world success stories to explain the concepts behind stream processing and its relation to Hadoop and other big data platforms. The session discusses how patterns and statistical models of R, Spark MLlib and other technologies can be integrated into real-time processing using open source frameworks (such as Apache Storm, Spark or Flink) or products (such as IBM InfoSphere Streams or TIBCO StreamBase). A live demo shows the complete development lifecycle combining analytics, machine learning and stream processing.
A well-publicised PwC report projects that by 2030, “smart automation” technologies could contribute up to 14% of global GDP (around 10% to UK GDP). It also believesthat the long-term net effect of automation on the economy will be positive. Thus, despite the above uncomplimentary words from a scientist whose work still drives our modern digital economy,the verdict is in – technologies like Robotic Process Automation (RPA) are here to stay.
Data Preparation vs. Inline Data Wrangling in Data Science and Machine LearningKai Wähner
Comparison of Data Preparation vs. Data Wrangling Programming Languages, Frameworks and Tools in Machine Learning / Deep Learning Projects.
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various options for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Video Recording / Screencast of this Slide Deck: https://youtu.be/2MR5UynQocs
Driving Digital Transformation through Service-Centric AIOpsOpsRamp
Driving Digital Transformation through Service-Centric AIOps. Reduce the noise with artificial intelligence.
To learn more about how OpsRamp can help you manage the unmanageable, visit us at - https://www.opsramp.com
Also, follow us on social media channels to learn about product highlights, news, announcements, events, conferences and more -
Twitter - https://www.twitter.com/OpsRamp
LinkedIn - https://www.linkedin.com/company/opsramp
The Big Connection: Integrating Cloud with Enterprise SystemsInside Analysis
The Briefing Room with Robin Bloor and Dell Boomi
Live Webcast July 30, 2013
http://www.insideanalysis.com
Integrating cloud solutions with existing infrastructures poses formidable challenges for most organizations, especially when key business applications are complex, or spread across disparate systems. Companies need to leverage and analyze their data no matter where it lives, and they need the flexibility to make changes as needed. Achieving this level of agility requires a new kind of application integration that natively harnesses cloud architectures.
Register for this episode of The Briefing Room to learn from veteran Analyst Robin Bloor as he explains how the maturation of cloud services and platforms has improved application integration techniques. He'll be briefed by Wes Manning of Dell Boomi, who will tout his company’s cloud integration offering, which includes the ability to integrate hallmark solutions like SAP and Salesforce with on premise or SaaS-based applications. He will also share a demo of Boomi Suggest, a community-developed repository of data mappings that can greatly accelerate integration projects.
AIOps: Anomalous Span Detection in Distributed Traces Using Deep LearningJorge Cardoso
The field of AIOps, also known as Artificial Intelligence for IT Operations, uses algorithms and machine learning to dramatically improve the monitoring, operation, and maintenance of distributed systems. Its main premise is that operations can be automated using monitoring data to reduce the workload of operators (e.g., SREs or production engineers). Our current research explores how AIOps – and many related fields such as deep learning, machine learning, distributed traces, graph analysis, time-series analysis, sequence analysis, and log analysis – can be explored to effectively detect, localize, and remediate failures in large-scale cloud infrastructures (>50 regions and AZs). In particular, this lecture will describe how a particular monitoring data structure, called distributed trace, can be analyzed using deep learning to identify anomalies in its spans. This capability empowers operators to quickly identify which components of a distributed system are faulty.
R, Spark, Tensorflow, H20.ai Applied to Streaming AnalyticsKai Wähner
Slides from my talk at Codemotion Rome in March 2017. Development of analytic machine learning / deep learning models with R, Apache Spark ML, Tensorflow, H2O.ai, RapidMinder, KNIME and TIBCO Spotfire. Deployment to real time event processing / stream processing / streaming analytics engines like Apache Spark Streaming, Apache Flink, Kafka Streams, TIBCO StreamBase.
Doing DevOps for Big Data? What You Need to Know About AIOpsDevOps.com
AIOps has the promise to create hyper-efficiency within DevOps teams as they struggle with the diversity, complexity, and rate of change across the entire stack.
DevOps teams working with big data face unique challenges due to the complexity and diversity of the components that comprise the big data stack. At the same time, AIOps is maturing to the point of creating true efficiencies among these DevOps teams as they struggle against the diversity, complexity, dynamic behavior and rate of change across the entire stack.
How to apply machine learning into your CI/CD pipelineAlon Weiss
A quick introduction to AIOps, the business reasons why the CI/CD pipeline needs to constantly improve, and how this can be accomplished with data that's already available with existing Machine Learning and other algorithms.
http://www.opitz-consulting.com
In this session our experts and Oracle ACE Directors Danilo Schmiedel and Torsten Winterberg have presented an in-depth discussion of Oracles new Internet of Things (IoT) Cloud Service from an architectural perspective. They have presented a reference architecture that also includes Oracles Integration, Process, Big Data, and Mobile Cloud Services. During the session they have demonstrated highlights and lessons learned from their first implementations with IoT Cloud Service.
The core of the story has been a live demo showing the development of a vending machine case. The vending machine is simulated by a Pi, which calls the IoT cloud, routes data to BI cloud and some ERP in the cloud. The way back is initiated by an iBeacon placed on the vending machine, which triggers a mobile app that simulates payment and talks via IoT Cloud directly with the vending machine to complete the purchase.
http://www.opitz-consulting.com
Digital Shift in Insurance: How is the Industry Responding with the Influx of...DataWorks Summit
The digital connected world is having an impact on the technology environments that insurers must create to thrive in the new era of computing. The nature of customer interactions, business processes from product, risk and claims management are continuously changing. During this session we will review recent research and insights from insurance companies in the life, general and reinsurance markets and discuss the implications for insurers as the industry considers implications from core systems, predictive and preventive analytics and improvements to customer experiences.
Millions of dollars are being spent annually by the insurance industry in InsurTech investments from risk listening, customer interactions (chatbots, SMS messaging, smart interactive conversations), to methods of evaluating claims (digital capture at notice of incident, dashcams, connected homes/vehicles).
These are all new types of data which the industry hasn't previously had to manage and govern.
Additionally, at the heart of this is how to create new business opportunities from data. We will also have an interactive conversation on discussing and exploring insurance implications of the new computing environment from AI, Big Data and IoT (Edge computing).
This is a selection of slides from Cloudera's 2008 pitch deck to raise a $5 million Series A. Accel wound up winning the deal and became the initial investor in the company.
Realizing your AIOps goals with machine learning in ElasticElasticsearch
As the volume of observability data explodes, relying solely on human analysis can lead to undesired impacts on apps and infrastructure, as well as unsustainable SRE and developer workload. Learn how machine learning features embedded in Elastic Observability workflows enable reliability, efficiency, and sustainability outcomes for enterprise IT teams — no data scientists required.
NoSQL in Practice with TIBCO: Real World Use Cases and Customer Success Stori...Kai Wähner
NoSQL is not just about different storage alternatives such as document store, key value store, graphs or column-based databases. The hardware is also getting much more important. Besides common disks and SSDs, enterprises begin to use in-memory storages more and more because a distributed in-memory data grid provides very fast data access and update. While its performance will vary depending on multiple factors, it is not uncommon to be 100 times faster than corresponding database implementations. For this reason and others described in this session, in-memory computing is a great solution for lifting the burden of big data, reducing reliance on costly transactional systems, and building highly scalable, fault-tolerant applications. The session begins with a short introduction to in-memory computing. Afterwards, different frameworks and product alternatives are discussed for implementing in-memory solutions. Finally, the main part of this session shows several different real world uses cases where in-memory computing delivers business value by supercharging the infrastructure, e.g. to accelerate services, handle spikes in processing or ensure fault tolerance and disaster recovery.
A lot of in-memory data grid products are available. TIBCO ActiveSpaces, Oracle Coherence, Infinispan, IBM WebSphere eXtreme Scale, Hazelcast, Gigaspaces, GridGain, Pivotal Gemfire to name most of the important ones.
"Cindy Xing is a Principal Software Dev Lead at Microsoft, with over 15 years of working experience in building and delivering large scale software distributed systems.
Her talk during the Data Science Conference will be focused on Edge Computing. Edge computing is the practice of processing data near the edge of your network, where the data is being generated, instead of in a centralized data-processing warehouse."
The Future of Infrastructure: Key Trends to considerCapgemini
Gunnar Menzel Vice President, Chief Architect - Capgemini
Which technologies have made the biggest impact, and which ones will impact us greatest in the future? Will technology advances slow down, stay the same, or speed-up? Which trends and technologies should I consider? The Digital Agenda; shifting business models; and the need for speed at lower cost are impacting, shaping and forming new technologies – creating new opportunities at an ever-increasing pace. Gunnar will outline the various infrastructure-related trends and technologies that are currently key, in addition to those that will prove to be significant going forward.
The digital transformation is going forward due to Mobile, Cloud and Internet of Things. Disrupting business models leverage Big Data Analytics and Machine Learning.
"Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud. "Fast Data" via stream processing is the solution to embed patterns - which were obtained from analyzing historical data - into future transactions in real-time.
This session uses several real world success stories to explain the concepts behind stream processing and its relation to Hadoop and other big data platforms. It discusses how patterns and statistical models of R, Spark MLlib, H2O, and other technologies can be integrated into real-time processing by using several different real world case studies. The session also points out why a Microservices architecture helps solving the agile requirements for these kind of projects.
A brief overview of available open source frameworks and commercial products shows possible options for the implementation of stream processing, such as Apache Storm, Apache Flink, Spark Streaming, IBM InfoSphere Streams, or TIBCO StreamBase.
A live demo shows how to implement stream processing, how to integrate machine learning, and how human operations can be enabled in addition to the automatic processing via a Web UI and push events.
Keywords: Big Data, Fast Data, Machine Learning, Analytics, Analytic Model, Stream Processing, Event Processing, Streaming Analytics, Real Time, Hadoop, Spark, MLlib, Streaming, R, TERR, TIBCO, Spotfire, StreamBase, Live Datamart, H20, Predictive Analytics, Data Discovery, Insights, Patterns
A well-publicised PwC report projects that by 2030, “smart automation” technologies could contribute up to 14% of global GDP (around 10% to UK GDP). It also believesthat the long-term net effect of automation on the economy will be positive. Thus, despite the above uncomplimentary words from a scientist whose work still drives our modern digital economy,the verdict is in – technologies like Robotic Process Automation (RPA) are here to stay.
Data Preparation vs. Inline Data Wrangling in Data Science and Machine LearningKai Wähner
Comparison of Data Preparation vs. Data Wrangling Programming Languages, Frameworks and Tools in Machine Learning / Deep Learning Projects.
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various options for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Video Recording / Screencast of this Slide Deck: https://youtu.be/2MR5UynQocs
Driving Digital Transformation through Service-Centric AIOpsOpsRamp
Driving Digital Transformation through Service-Centric AIOps. Reduce the noise with artificial intelligence.
To learn more about how OpsRamp can help you manage the unmanageable, visit us at - https://www.opsramp.com
Also, follow us on social media channels to learn about product highlights, news, announcements, events, conferences and more -
Twitter - https://www.twitter.com/OpsRamp
LinkedIn - https://www.linkedin.com/company/opsramp
The Big Connection: Integrating Cloud with Enterprise SystemsInside Analysis
The Briefing Room with Robin Bloor and Dell Boomi
Live Webcast July 30, 2013
http://www.insideanalysis.com
Integrating cloud solutions with existing infrastructures poses formidable challenges for most organizations, especially when key business applications are complex, or spread across disparate systems. Companies need to leverage and analyze their data no matter where it lives, and they need the flexibility to make changes as needed. Achieving this level of agility requires a new kind of application integration that natively harnesses cloud architectures.
Register for this episode of The Briefing Room to learn from veteran Analyst Robin Bloor as he explains how the maturation of cloud services and platforms has improved application integration techniques. He'll be briefed by Wes Manning of Dell Boomi, who will tout his company’s cloud integration offering, which includes the ability to integrate hallmark solutions like SAP and Salesforce with on premise or SaaS-based applications. He will also share a demo of Boomi Suggest, a community-developed repository of data mappings that can greatly accelerate integration projects.
AIOps: Anomalous Span Detection in Distributed Traces Using Deep LearningJorge Cardoso
The field of AIOps, also known as Artificial Intelligence for IT Operations, uses algorithms and machine learning to dramatically improve the monitoring, operation, and maintenance of distributed systems. Its main premise is that operations can be automated using monitoring data to reduce the workload of operators (e.g., SREs or production engineers). Our current research explores how AIOps – and many related fields such as deep learning, machine learning, distributed traces, graph analysis, time-series analysis, sequence analysis, and log analysis – can be explored to effectively detect, localize, and remediate failures in large-scale cloud infrastructures (>50 regions and AZs). In particular, this lecture will describe how a particular monitoring data structure, called distributed trace, can be analyzed using deep learning to identify anomalies in its spans. This capability empowers operators to quickly identify which components of a distributed system are faulty.
R, Spark, Tensorflow, H20.ai Applied to Streaming AnalyticsKai Wähner
Slides from my talk at Codemotion Rome in March 2017. Development of analytic machine learning / deep learning models with R, Apache Spark ML, Tensorflow, H2O.ai, RapidMinder, KNIME and TIBCO Spotfire. Deployment to real time event processing / stream processing / streaming analytics engines like Apache Spark Streaming, Apache Flink, Kafka Streams, TIBCO StreamBase.
Doing DevOps for Big Data? What You Need to Know About AIOpsDevOps.com
AIOps has the promise to create hyper-efficiency within DevOps teams as they struggle with the diversity, complexity, and rate of change across the entire stack.
DevOps teams working with big data face unique challenges due to the complexity and diversity of the components that comprise the big data stack. At the same time, AIOps is maturing to the point of creating true efficiencies among these DevOps teams as they struggle against the diversity, complexity, dynamic behavior and rate of change across the entire stack.
How to apply machine learning into your CI/CD pipelineAlon Weiss
A quick introduction to AIOps, the business reasons why the CI/CD pipeline needs to constantly improve, and how this can be accomplished with data that's already available with existing Machine Learning and other algorithms.
http://www.opitz-consulting.com
In this session our experts and Oracle ACE Directors Danilo Schmiedel and Torsten Winterberg have presented an in-depth discussion of Oracles new Internet of Things (IoT) Cloud Service from an architectural perspective. They have presented a reference architecture that also includes Oracles Integration, Process, Big Data, and Mobile Cloud Services. During the session they have demonstrated highlights and lessons learned from their first implementations with IoT Cloud Service.
The core of the story has been a live demo showing the development of a vending machine case. The vending machine is simulated by a Pi, which calls the IoT cloud, routes data to BI cloud and some ERP in the cloud. The way back is initiated by an iBeacon placed on the vending machine, which triggers a mobile app that simulates payment and talks via IoT Cloud directly with the vending machine to complete the purchase.
http://www.opitz-consulting.com
Digital Shift in Insurance: How is the Industry Responding with the Influx of...DataWorks Summit
The digital connected world is having an impact on the technology environments that insurers must create to thrive in the new era of computing. The nature of customer interactions, business processes from product, risk and claims management are continuously changing. During this session we will review recent research and insights from insurance companies in the life, general and reinsurance markets and discuss the implications for insurers as the industry considers implications from core systems, predictive and preventive analytics and improvements to customer experiences.
Millions of dollars are being spent annually by the insurance industry in InsurTech investments from risk listening, customer interactions (chatbots, SMS messaging, smart interactive conversations), to methods of evaluating claims (digital capture at notice of incident, dashcams, connected homes/vehicles).
These are all new types of data which the industry hasn't previously had to manage and govern.
Additionally, at the heart of this is how to create new business opportunities from data. We will also have an interactive conversation on discussing and exploring insurance implications of the new computing environment from AI, Big Data and IoT (Edge computing).
This is a selection of slides from Cloudera's 2008 pitch deck to raise a $5 million Series A. Accel wound up winning the deal and became the initial investor in the company.
Realizing your AIOps goals with machine learning in ElasticElasticsearch
As the volume of observability data explodes, relying solely on human analysis can lead to undesired impacts on apps and infrastructure, as well as unsustainable SRE and developer workload. Learn how machine learning features embedded in Elastic Observability workflows enable reliability, efficiency, and sustainability outcomes for enterprise IT teams — no data scientists required.
NoSQL in Practice with TIBCO: Real World Use Cases and Customer Success Stori...Kai Wähner
NoSQL is not just about different storage alternatives such as document store, key value store, graphs or column-based databases. The hardware is also getting much more important. Besides common disks and SSDs, enterprises begin to use in-memory storages more and more because a distributed in-memory data grid provides very fast data access and update. While its performance will vary depending on multiple factors, it is not uncommon to be 100 times faster than corresponding database implementations. For this reason and others described in this session, in-memory computing is a great solution for lifting the burden of big data, reducing reliance on costly transactional systems, and building highly scalable, fault-tolerant applications. The session begins with a short introduction to in-memory computing. Afterwards, different frameworks and product alternatives are discussed for implementing in-memory solutions. Finally, the main part of this session shows several different real world uses cases where in-memory computing delivers business value by supercharging the infrastructure, e.g. to accelerate services, handle spikes in processing or ensure fault tolerance and disaster recovery.
A lot of in-memory data grid products are available. TIBCO ActiveSpaces, Oracle Coherence, Infinispan, IBM WebSphere eXtreme Scale, Hazelcast, Gigaspaces, GridGain, Pivotal Gemfire to name most of the important ones.
"Cindy Xing is a Principal Software Dev Lead at Microsoft, with over 15 years of working experience in building and delivering large scale software distributed systems.
Her talk during the Data Science Conference will be focused on Edge Computing. Edge computing is the practice of processing data near the edge of your network, where the data is being generated, instead of in a centralized data-processing warehouse."
The Future of Infrastructure: Key Trends to considerCapgemini
Gunnar Menzel Vice President, Chief Architect - Capgemini
Which technologies have made the biggest impact, and which ones will impact us greatest in the future? Will technology advances slow down, stay the same, or speed-up? Which trends and technologies should I consider? The Digital Agenda; shifting business models; and the need for speed at lower cost are impacting, shaping and forming new technologies – creating new opportunities at an ever-increasing pace. Gunnar will outline the various infrastructure-related trends and technologies that are currently key, in addition to those that will prove to be significant going forward.
The digital transformation is going forward due to Mobile, Cloud and Internet of Things. Disrupting business models leverage Big Data Analytics and Machine Learning.
"Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud. "Fast Data" via stream processing is the solution to embed patterns - which were obtained from analyzing historical data - into future transactions in real-time.
This session uses several real world success stories to explain the concepts behind stream processing and its relation to Hadoop and other big data platforms. It discusses how patterns and statistical models of R, Spark MLlib, H2O, and other technologies can be integrated into real-time processing by using several different real world case studies. The session also points out why a Microservices architecture helps solving the agile requirements for these kind of projects.
A brief overview of available open source frameworks and commercial products shows possible options for the implementation of stream processing, such as Apache Storm, Apache Flink, Spark Streaming, IBM InfoSphere Streams, or TIBCO StreamBase.
A live demo shows how to implement stream processing, how to integrate machine learning, and how human operations can be enabled in addition to the automatic processing via a Web UI and push events.
Keywords: Big Data, Fast Data, Machine Learning, Analytics, Analytic Model, Stream Processing, Event Processing, Streaming Analytics, Real Time, Hadoop, Spark, MLlib, Streaming, R, TERR, TIBCO, Spotfire, StreamBase, Live Datamart, H20, Predictive Analytics, Data Discovery, Insights, Patterns
Correlsense Enterprise APM vs Traditional Infographic Correlsense
Fascinating infographic that takes a look at Enterprise Application Performance Monitoring ver Traditional.
Enterprise businesses use new, legacy, and commercial
applications written in multiple programming languages. They may touch middleware components and reach end-users on browsers, rich clients, and mobile devices.
While traditional APM Traditional APM (or developer APM) products are designed to monitor vertical technology stacks like Java, .NET or PHP, on browsers and mobile devices. They provide strong technology support inside each stack, good deep dive capabilities, and are often used as part of new application development.
Secrets to Seeing it All; Enterpise Application Performance Management Correlsense
Correlsense drills down into the secrets of enterprise application performance management. Feel free to contact us to get more info on www.corelsense.com
Mobile Enterprise Application Platform: A solution to myriad challenges in en...[x]cube LABS
Our whitepaper on MEAP provides an overview of the mobile enterprise application platforms, challenges and benefits of MEAP, compares it to other alternate solutions and answers why and when MEAP can be an ideal solution in enterprise mobility ecosystem.
Point-to-Point vs. MEAP - The Right Approach for an Integrated Mobility Solut...RapidValue
There are two commonly used approaches for building integrated mobility solutions: Point-to-point integration and Mobile Enterprise Application Platform (MEAP).
This paper explains why an enterprise mobility integration solution is needed, describes and compares the two approaches, and provides a guide for how to choose the right mobility integration technique for your organization. The paper also examines various MEAP platforms available and the key differences between popular platforms - Kony and SAP Unwired Platform.
From a mobile application development standpoint, there is another widely used approach: cross-platform development frameworks. These frameworks allow
developers to build once and deploy across multiple device platforms. However, these frameworks lack integration and mobile device management capabilities,
and therefore we have excluded them from consideration for the purposes of this whitepaper. To learn more about cross-platform development, download our whitepaper: “How to Choose the Right Architecture for your Mobile Application” -
http://www.rapidvaluesolutions.com/whitepaper/
Mobile Bang Theory - Gartner Mobile & Wireless 2009Relayware
The ‘Mobile Bang’ Theory: Amplifying Business Profitability with High-Impact Mobile Moments.
This presentation showcases how the business value of mobile applications increases when implemented strategically across the enterprise. Both ROI snapshots and case studies illustrate the idea behind the “Mobile Bang” – which says every mobile interaction sparks multiple business reactions, adding up to sizeable returns across the enterprise.
Mobile field solution 30 crucial aspects for successful implementationsIuliana Baciu
Finding the right mobile solution for your field team is not an effortless project.
New constantly developing technologies are making it challenging to find the right software which
suits your business processes.
Defining Enterprise Performance Management (EPM) in Digital Era.pdfAnil
Speed agility, disruption, and innovation have become new the normal in the contemporary world. Enterprise Performance Management (EPM) is no exception. We have seen a tremendous shift in how business leaders manage their respective organizations’ performance in the last five years. Technology is a huge enabler facilitating real-time access to critical information at the desired granularity.
Learn about getting started with SAP Net Weaver Business Warehouse on IBM PowerLinux Solution Edition for SAP applications that provides you with an overview of the business solutions and the advantages and the benefits a business can gain when using such a solution. For more information on Power Systems, visit http://ibm.co/Lx6hfc.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
Is ERP on Cloud the next step for your business? | Cloud ERP BenefitsVIENNA Advantage
MARKET TRANSFORMATION
In the past few years the biggest and oldest ERP providers earned a reputation for time-consuming deployments, costly maintenance, outdated user interfaces, and general inflexibility.
But as enterprise solutions market is changing through the dynamics created by new technologies and devices, ERP providers are forced to move from their rigid image to more flexible, cloud based and user friendly image.
Consumer level applications and social platforms have transformed the thinking of people towards software and business applications.
Business users and IT specialists are looking for faster deployments and less maintenance, therefore the use of cloud solutions – especially in the form of software as a service (SaaS) – has increased rapidly.
Demand for ERP on Cloud Solutions
Companies are looking for ready made platforms they can build their specific solutions on, with least effort in product development.
An InformationWeek’s 2014 State of Cloud report found that 64% of companies (all with 50 or more employees) using some form of cloud technology have at least one SaaS app in the mix.
Research firm Gartner predicted that at least 30% of service-oriented businesses will move the majority of their ERP applications to the cloud by 2018.
PwC recent analysis of ERP on Cloud adoption shows that net new license revenues for traditional ERP systems have been declining since 2013 to a level that has already been surpassed by global revenue from cloud-based SaaS solutions.
Why Cloud Computing Matters?
ERP on cloud benefits customers in multiple ways, from providing application scalability and overall business flexibility to reduction of hardware costs and implementation time.
Cloud computing technology, especially the SaaS model, made it easier for small and medium companies to acquire cloud ERP solutions and not have to manage hardware, software, and upgrades while reducing up-front expenses.
VIENNA Advantage ERP and CRM is an established player of the new age of Cloud-based ERP systems. VIENNA Advantage is already working with various partners in many countries as an Application Backbone to Cloud Solutions and differentiates itself from traditional ERP players in its approach towards its platform and features.
Benefits of VIENNA Advantage SaaS Solution
1. One Application Instance and DB used by various customers and Users
2. All customers run the same version
3. More penetrated in the market
4. New customer means new Tenant
5. Meant for Small companies only
6. No real control over upgrades
7. No or very few customizations
8. Lesser Cost than Real Cloud Solution
Benefits of VIENNA Advantage Real Cloud Solution
1. Every company has its own application and database instance
2. No force to upgrade
3. Fully Customizable
4. Meant for Enterprises and Small companies
5. Higher cost compared to SaaS
6. Meant for scalable enterprise Applications
SAP was founded in 1972 in Walldorf, Germany. It stands for Systems, Applications and Products in Data Processing. Over the years, it has grown and evolved to become the world premier provider of client/server business solutions for which it is so well known today. The SAP R/3 enterprise application suite for open client/server systems has established a new standards for providing business information management solutions.
SAP product are consider excellent but not perfect. The main problems with software product is that it can never be perfect.
The main advantage of using SAP as your company ERP system is that SAP have a very high level of integration among its individual applications which guarantee consistency of data throughout the system and the company itself.
In a standard SAP project system, it is divided into three environments, Development, Quality Assurance and Production.
The development system is where most of the implementation work takes place. The quality assurance system is where all the final testing is conducted before moving the transports to the production environment. The production system is where all the daily business activities occur. It is also the client that all the end users use to perform their daily job functions.
To all company, the production system should only contains transport that have passed all the tests.
SAP is a table drive customization software. It allows businesses to make rapid changes in their business requirements with a common set of programs. User-exits are provided for business to add in additional source code. Tools such as screen variants are provided to let you set fields attributes whether to hide, display and make them mandatory fields.
This is what makes ERP system and SAP in particular so flexible. The table driven customization are driving the program functionality instead of those old fashioned hard-coded programs. Therefore, new and changed business requirements can be quickly implemented and tested in the system.
Many other business application software have seen this table driven customization advantage and are now changing their application software based on this table customizing concept.
In order to minimized your upgrading costs, the standard programs and tables should not be changed as far as possible. The main purpose of using a standard business application software like SAP is to reduced the amount of time and money spend on developing and testing all the programs. Therefore, most companies will try to utilized the available tools provided by SAP.
Similar to Correlsense Enterprise APM vs Traditional Infographic (20)
Unify Citrix & Back End Application Performance Data Presented by CorrelsenseCorrelsense
Rich client and Citrix applications are used widely in the enterprise environments, however one of the biggest blind spots of enterprises when it comes to providing visibility of any application, seems to be the delivery of services via citrix
Best Practices for Managing and Monitoring WebSphere Message BrokerCorrelsense
WebSphere Message Broker serves as a transactional backbone for many IT organizations yet introduces complexity around integrating, managing and monitoring messaging-based solutions. This results in lost message flows and stalled transactions. Join Correlsense for an online seminar which teaches holistic management and monitoring solutions for gaining visibility into and taking control of WMB. We discuss:
-How to identify key implementation and management challenges for WMB 6, 7 or 8
-A new approach to locating stalled transactions, understanding application dependencies and monitoring message flows
-Real world case studies and a live demo that illustrate ways to gain deeper visibility into your WebSphere Message Broker
Preventing the Next Deployment Issue with Continuous Performance Testing and ...Correlsense
Traditionally, identifying and remedying performance problems resulting from application deployments has been a slow, reactive process. Tools exist which report on application changes and problems after they occur, but how do you prevent your next performance issues before they even begins?
Join Correlsense and dbMaestro for an online seminar outlining the crucial strategies for continuous performance testing and monitoring. We will discuss:
-Limitations of traditional strategies for application deployments
-Best practices for eliminating the risks of application changes
-Solutions for proactive application performance monitoring and database change management
5 APM and Capacity Planning Imperatives for a Virtualized WorldCorrelsense
The proliferation of virtualized applications has greatly increased the complexity of capacity planning and performance management. Monitoring and forecasting CPU utilization is no longer enough. IT operations and capacity planners now must understand and optimize their applications and infrastructure from the end user to the data center.
Join Correlsense and Metron-Athene for an online seminar which will explore key performance management and capacity planning strategies for a virtualized world. We will discuss:
What you need to know about capacity management when operating in both physical and virtual environments
How performance monitoring in virtual environments relates to your capacity management goals
What is unique about capacity and performance management for virtualized applications
New Approaches to Faster Oracle Forms System PerformanceCorrelsense
Are your end-users complaining that Forms is slow? Ever wonder what the source of the problem is? Want to learn what are the fastest, most effective strategies to improve overall performance and end user experience?
Join us for a webinar where we will showcase best practices for application support engineers, application owners, QA engineers, Oracle Forms developers and EBS Integrators. Topics include:
Minimizing start up times and resource requirements
Improving speed of Forms rendering
Gaining visibility into the potential source of bottlenecks in Oracle components
Speakers: Mia Urman, CEO of OraPlayer Ltd. and Frank Days, VP of Marketing, Correlsense
The Essentials of Mobile App Performance Testing and MonitoringCorrelsense
Complexity across mobile carriers, locations and operating systems has made building mobile apps and monitoring their end user performance time consuming and expensive. The importance of testing mobile apps on iOS, Android and Windows Phone is increasing as more users embrace these devices. Join Correlsense and uTest for an online seminar which will teach you the steps to successful mobile application testing and performance management. We will discuss:
- The proliferation of mobile devices and the technical challenges they bring to end user experience monitoring
- Ways to prepare mobile applications for peak usage periods with the right load and performance testing techniques
- Tips and techniques for gaining visibility into the performance of mobile applications with the right monitoring tools
We will conclude with a discussion of the Correlsense and uTest solutions.
Five Keys for Performance Management of Oracle Forms and E-Business SuiteCorrelsense
Today's APM tools do not provide sufficient capabilities to perform real end user monitoring of Oracle applications. While these tools can track basic performance data, most solutions do not cover the entire Oracle E-Business Suite technology stack and it can be challenging to acquire the in-depth visibility needed to properly manage your application's performance.
Join us for a webinar where we will showcase solutions for application support engineers, application owners, QA engineers, Oracle Forms developers and EBS Integrators. Topics include how to:
Isolate problems before end users experience them
Gain visibility into the potential source of bottlenecks in Oracle components
Reduce the risk and overall time to rollout for new applications, Oracle Forms migrations and EBS upgrades
Analyze stress tests to identify, isolate and resolve scalability issues before rolling out to production
Monitor your end user experience with both real-time and historical performance metrics
Speakers: Mia Urman, CEO of OraPlayer Ltd. and Frank Days, VP of Marketing, Correlsense
Best Practices for Managing SaaS ApplicationsCorrelsense
The proliferation of SaaS applications like Salesforce.com is creating a host of new management challenges. For example, how do you measure the performance of applications you don’t host? What real-time data do you have to communicate with business stakeholders? How will you know if SLA commitments are being met?
Join us for a webinar exploring the best practices for managing SaaS applications, including:
*Important ways that the management of SaaS and hosted application management differ
*The unique challenges of supporting enterprise SaaS applications
*Case studies demonstrating new techniques and tools for measuring the performance of hosted applications like Salesforce.com
An Introduction to Software Performance EngineeringCorrelsense
Software performance engineering is becoming increasingly important to businesses as they look to improve the non-functional performance of applications and get more out of IT investments. By leveraging performance engineering techniques, IT professionals can be indispensable in building and optimizing scalable systems. This
introductory course will teach you the essentials of software
performance engineering including :
• The performance challenges faced by Enterprise IT today
• What is software performance engineering (SPE)?
• Best practices for building scalable software systems
• The approaches to integrating SPE into IT project lifecycles
• Common frameworks for measuring application performance and service levels
• The impact of SPE on software developers, testers, capacity planes,
and other IT professionals
• Case studies from the finance, retail, and insurance industries
Instructor: Walter Kuketz, SVP and CTO, Collaborative Consulting
This training is sponsored by Correlsense, Collaborative Consulting,
and New Horizons
The increasing adoption of DevOps principles has led to greater integration between software development (both application and software engineering) and IT operations (both systems administration and infrastructure). In this online seminar, we will explore the DevOps approaches
An Integrated Approach to ITIL Aligned Capacity ManagementCorrelsense
A brief overview of ITIL Capacity Management. The need for both component data and transaction data to do effective Capacity Management. Monitor your business transactions. Component data, business data and a framework for Capacity Management reporting, analysis, and planning. An integrated solution for Capacity Management
New approaches to managing complex applicationsCorrelsense
The proliferation of multi-platform, web-enabled, cloud-based applications within an increasingly complex IT infrastructure creates a host of new management challenges. As your transactions move between new distributed systems and legacy mainframes components your existing approach to application monitoring might be missing important performance issues.
In this informative presentation, we discuss:
- Strategies for managing these increasingly complex applications
- New approaches to managing service levels - from the browser to the mainframe
- Innovative ways to gain complete visibility into end-to-end user experience and eliminate all application performance blind-spots
EMA - Measuring the User Experience in the CloudCorrelsense
Cloud computing brings with it many benefits, especially lower IT costs and increased flexibility. However, the dynamic, hybrid nature of Cloud environments require enterprises to re-think their existing IT management processes and tools. Applications that perform poorly and fail to meet service levels — whether on premise or in the Cloud — can cause users to churn and revenues to drop.
Join EMA Research Director Julie Craig and Correlsense CEO Oren Elias for this Webinar that will highlight ways to measure how end users experience your business applications, as well as identify which approaches are likely candidates for a long-term strategy.
Attendees will learn:
How end-user response times are impacted by components and new changes that are introduced in the IT environment
The 3 tips for managing Cloud-based applications
The "secret sauce" for reliable end user monitoring, based on customer case studies that will be presented
Show Me the Money: Connecting Performance Engineering to Real Business ResultsCorrelsense
Performance testing and optimization are often neglected parts of enterprise application roll out and upgrade initiatives.
The challenge for many IT managers is communicating the value of IT performance projects to business stakeholders who would benefit the most.
An interactive discussion with Walter Kuketz, CTO of Collaborative Consulting where he shares:
- How to align key business drivers with your performance engineering projects
- Ways to bridge the IT-business stakeholder communication gap
- A new approach to model business transactions and their IT dependencies
Host: Frank Days
Title: VP of Marketing, Correlsense
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
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
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
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
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/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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.
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.
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.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Correlsense Enterprise APM vs Traditional Infographic
1. ENTERPRISE APM
Enterprise businesses use new, legacy, and commercial
applications written in multiple programming languages. They
may touch middleware components and reach end-users on
browsers, rich clients, and mobile devices. Problems can
happen anywhere, be sporadic, or impact only a few critical
transactions. It is important to monitor horizontally across
technology stacks.
Traditional APM (or developer APM) products are designed
to monitor vertical technology stacks like Java, .NET or
PHP, on browsers and mobile devices. They provide strong
technology support inside each stack, good deep dive
capabilities, and are often used as part of new application
development.
NEW
LEGACY
AND
COMMERCIAL
APPLICATIONS
BROWSER
RICH CLIENT
CITRIX
MOBILE
TRACK
ALL
TRANSACTIONS
ALL HOPS
20
THE SHAPE OF DEVOPS
Whether it is in the cloud or in the
enterprise, diverse DevOps teams
need to lead the way. They need to
share a common toolkit across
technology stacks and speak the
same language.
When things go wrong, the team
needs to rely on an agreed upon set
of monitoring data they can all
understand. No war rooms, no
blame storming, just a search for
the truth.
Development, operations,
database, networking, and other IT
groups need to see everything,
everywhere, all the time.
TECHNOLOGY ADOPTION RATES
Cloud adoption is ramping up
rapidly, but legacy and
enterprise software aren’t going
anywhere. The enterprise
involves hybrid models and
heterogeneous networks. Being
able to capture performance
data across multiple technology
stacks is a key to success.
31
MEASURE – the real user experience of
employees, customers, partners and others
in real time on browsers, rich clients, and
mobile devices.
TRACK – every transaction across every
hop. Sampling or averaging obscures key
information. Tracking only when a problem is
identified misses key data about starting
conditions.
REPORT – provide information in easy to
use charts, dashboards, and reports that
everyone can understand and agree on.
If you have a distributed and
heterogeneous IT
environment, you need to
capture performance data
horizontally across
technologies. You need
Enterprise APM.
If you have applications built
around a single technology
stack, and you can accept
sampling and averaging of
performance data, then
Traditional APM might be right
for you.
TRADITIONAL APM
JAVA
.NET
PHP
APPLICATIONS
BROWSERS
AND
MOBILE
SAMPLE
OR
AVERAGE
TRANSACTIONS
VS.
<20%<15%
50%
70%
SaaS
CRM
SaaS
ERP
Enterprise
Cloud
Adoption
SMB Cloud
Adoption
ISOLATE – problems that may be unique to
a user, location, or time period. These
intermittent issues can only be resolved with
data about every transaction.
SERVE – provide information that is relevant
to multiple organizations from business
people, to IT management, and all other
technical disciplines. Serve both the Dev
and the Ops of DevOps.
ENTERPRISE APM TRADITIONAL APM
MEASURE – the real user experience of
employees, customers, partners and others
in real time on browsers and mobile devices.
Typically do not monitor rich clients.
TRACK – typically sample or average
transactions. Sometimes track all
transactions after a problem is found. Must
be published to remote server. Do not
capture all hops of the transaction.
REPORT – provide information in easy to
use charts, dashboards, and reports that
everyone can understand and agree on.
ISOLATE – problems that may be unique to
an application and its technology stack.
Intermittent issues are difficult to spot and
may be lost in averaging.
SERVE – primarily developers of Java,
.NET, and PHP applications. The resulting
insights can be shared with other groups.