Next Steps in Your Digital Transformation
This session brings together all the lessons learnt throughout the day and shares with you practical advice on how to get started with, or accelerate, your journey to become a digital business.
Pivotal Digital Transformation Forum: Requirements to Become a Data-Driven En...VMware Tanzu
To become a data-driven enterprise, companies must move from inflexible legacy data infrastructure that cannot scale to agile data architectures based on scaled-up, open-source systems that can handle any type or source of data. This involves storing both structured and unstructured high-volume, high-velocity data and then analyzing it through machine learning, predictive analytics, and real-time analytics to develop advanced analytical applications and globally scaled, data-driven applications. Achieving this requires expertise in agile development, DevOps, hybrid cloud, and continuous delivery to innovate with closed-loop applications.
The document discusses how systems of systems are changing product design and manufacturing. As products, buildings, and infrastructure become smarter, more connected, and data-rich, design must shift from discrete things to integrated systems. The talk will showcase frog's view of "Big Design," which designs adaptive, modular, intelligent systems that connect the human, enterprise, and urban scales. Big Design uses design and engineering to shape interconnected, intelligent systems across many levels. This represents a shift in value from individual devices to connected systems.
Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?SnapLogic
Companies collect more data but struggle with how to glean the best insights. Use of Machine Learning also needs power data integration.
In this presentation, Janet Jaiswal, SnapLogic's VP of product marketing, reviews key strategies and technologies to deliver intelligent data via self-service ML models.
To learn more, visit https://www.snaplogic.com
Pivotal Digital Transformation Forum: Accelerate Time to Market with Business...VMware Tanzu
This document discusses how digital disruption is changing business and the importance of business innovation through cloud-native software and a DevOps approach. It argues that software is becoming a core differentiator and companies need to focus on accelerating time to market for new applications. Pivotal Cloud Foundry is presented as an open platform that can help businesses become more agile by removing constraints for developers and operators and allowing continuous delivery of applications and flexibility across clouds without vendor lock-in. Case studies demonstrate how Cloud Foundry has allowed faster delivery of applications at companies like Humana.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Data and its Role in Your Digital TransformationVMware Tanzu
The document discusses how data and data-driven approaches are fueling digital transformation and innovation across industries. It provides examples of how companies are leveraging large amounts of data and machine learning to improve products and business models. The document advocates becoming a data-driven enterprise by embracing new data sources, data processing techniques, and data analytics to gain insights and build intelligent applications.
Pivotal the new_pivotal_big_data_suite_-_revolutionary_foundation_to_leverage...EMC
The document discusses Pivotal's big data suite and business data lake offerings. It provides an overview of the components of a business data lake, including storage, ingestion, distillation, processing, unified data management, and action components. It also defines various data processing approaches like streaming, micro-batching, batch, and real-time response. The goal is to help organizations build analytics and transactional applications on big data to drive business insights and revenue.
Driving Real Insights Through Data ScienceVMware Tanzu
Major changes in industries have been brought about by the emergence of data-driven discoveries and applications. Many organizations are bringing together their data, and looking to drive change. But the ability to generate new insights in real time from a massive sets of data is still far from commonplace.
At this event, data technology experts and data scientists from Pivotal provided the latest business perspective on how data science and engineering can be used to accelerate the generation of new insights.
For information about upcoming Pivotal events, please visit: http://pivotal.io/news-events/#events
Pivotal Digital Transformation Forum: Requirements to Become a Data-Driven En...VMware Tanzu
To become a data-driven enterprise, companies must move from inflexible legacy data infrastructure that cannot scale to agile data architectures based on scaled-up, open-source systems that can handle any type or source of data. This involves storing both structured and unstructured high-volume, high-velocity data and then analyzing it through machine learning, predictive analytics, and real-time analytics to develop advanced analytical applications and globally scaled, data-driven applications. Achieving this requires expertise in agile development, DevOps, hybrid cloud, and continuous delivery to innovate with closed-loop applications.
The document discusses how systems of systems are changing product design and manufacturing. As products, buildings, and infrastructure become smarter, more connected, and data-rich, design must shift from discrete things to integrated systems. The talk will showcase frog's view of "Big Design," which designs adaptive, modular, intelligent systems that connect the human, enterprise, and urban scales. Big Design uses design and engineering to shape interconnected, intelligent systems across many levels. This represents a shift in value from individual devices to connected systems.
Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?SnapLogic
Companies collect more data but struggle with how to glean the best insights. Use of Machine Learning also needs power data integration.
In this presentation, Janet Jaiswal, SnapLogic's VP of product marketing, reviews key strategies and technologies to deliver intelligent data via self-service ML models.
To learn more, visit https://www.snaplogic.com
Pivotal Digital Transformation Forum: Accelerate Time to Market with Business...VMware Tanzu
This document discusses how digital disruption is changing business and the importance of business innovation through cloud-native software and a DevOps approach. It argues that software is becoming a core differentiator and companies need to focus on accelerating time to market for new applications. Pivotal Cloud Foundry is presented as an open platform that can help businesses become more agile by removing constraints for developers and operators and allowing continuous delivery of applications and flexibility across clouds without vendor lock-in. Case studies demonstrate how Cloud Foundry has allowed faster delivery of applications at companies like Humana.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Data and its Role in Your Digital TransformationVMware Tanzu
The document discusses how data and data-driven approaches are fueling digital transformation and innovation across industries. It provides examples of how companies are leveraging large amounts of data and machine learning to improve products and business models. The document advocates becoming a data-driven enterprise by embracing new data sources, data processing techniques, and data analytics to gain insights and build intelligent applications.
Pivotal the new_pivotal_big_data_suite_-_revolutionary_foundation_to_leverage...EMC
The document discusses Pivotal's big data suite and business data lake offerings. It provides an overview of the components of a business data lake, including storage, ingestion, distillation, processing, unified data management, and action components. It also defines various data processing approaches like streaming, micro-batching, batch, and real-time response. The goal is to help organizations build analytics and transactional applications on big data to drive business insights and revenue.
Driving Real Insights Through Data ScienceVMware Tanzu
Major changes in industries have been brought about by the emergence of data-driven discoveries and applications. Many organizations are bringing together their data, and looking to drive change. But the ability to generate new insights in real time from a massive sets of data is still far from commonplace.
At this event, data technology experts and data scientists from Pivotal provided the latest business perspective on how data science and engineering can be used to accelerate the generation of new insights.
For information about upcoming Pivotal events, please visit: http://pivotal.io/news-events/#events
How Data Science is Preventing College Dropouts and Advancing Student SuccessVMware Tanzu
Educational institutions have a wealth of information, which can be brought together in an institutional data lake to predict and influence student behavior. In this webinar, one of Pivotal's principal data scientists discusses a recent collaborative project with a top university, in which many data sources were used to build a 360-degree profile of student activity on campus and help predict student success. Learn about the data science pipelines that Pivotal developed and how they are now being used to predict student metrics (such as GPA, course grade and time to graduate), and even as intervention tools to help prevent students from dropping out.
Webinar recording: https://youtu.be/SxXZBmAs1aE
Data technology experts from Pivotal give the latest perspective on how big data analytics and applications are transforming organizations across industries.
This event provides an opportunity to learn about new developments in the rapidly-changing world of big data and understand best practices in creating Internet of Things (IoT) applications.
Learn more about the Pivotal Big Data Roadshow: http://pivotal.io/big-data/data-roadshow
Webinar: The Death of Traditional Data IntegrationSnapLogic
In this webinar, we hear from industry analyst, middleware expert and author David Linthicum on why “existing approaches to data integration won’t meet future needs as the use of technology continues to change.” David also says that, “drastic measures must be taken now to prepare enterprises for the arrival of this technology, and to position enterprises to take full advantage.”
This webinar will show you how the game is changing, and what you can do about it right now. We summarize the changes that are happening, and review new and emerging patterns of data integration, as well as data integration technology that you can buy today that lives up to these new expectations.
To learn more, visit: www.snaplogic.com/big-data
Next Steps In Your Digital TransformationVMware Tanzu
This session brings together all the lessons learnt throughout the day and shares with you practical advice on how to get started with, or accelerate, your journey to become a digital business.
Speaker: Fadi Yousuf, Sales Manager - Gulf & KSA, Pivotal
Pivotal Data Warehouse in the Age of Digital TransformationVMware Tanzu
View the recording: https://content.pivotal.io/webinars/the-data-warehouse-in-the-age-of-digital-transformation?utm_source-pivotalwebsite&utm-medium-email-link&utm-campaign=datawarehouse-hiredbrains-q117
In the past years of Big Data and digital transformation “euphoria”, Hadoop and Spark received most of the attention as platforms for large-scale data management and analytics. Data warehouses based on relational database technology, for a variety of reasons, came under scrutiny as perhaps no longer needed.
However, if there is anything users have learned recently it’s that the mission of data warehouses is as vital as ever. Cost and operational deficiencies can be overcome with a combination of cloud computing and open source software, and by leveraging the same economics of traditional big data projects - scale-up and scale-out at commodity pricing.
In this webinar, Neil Raden from Hired Brains Research makes the case that an evolved data warehouse implementation continues to play a vital role in the enterprise, providing unique business value that actually aids digital transformation. Attendees will learn:
- How the role of the data warehouse has evolved over time
- Why Hadoop and Spark are not replacements for the data warehouse
- How the data warehouse supports digital transformation initiatives
- Real-life examples of data warehousing in digital transformation scenarios
- Advice and best practices for evolving your own data warehouse practice
This document discusses enterprise data science and machine learning. It begins by noting that data is now more plentiful and machine learning opportunities are everywhere. However, challenges remain around scaling data science work, making models production-ready, and meeting different team needs. The document then introduces Cloudera's Data Science Workbench for addressing these challenges. It claims the Workbench provides a secure, self-service environment allowing data scientists direct access to enterprise data and tools while meeting IT requirements. Examples are given of how it supports the full data science pipeline from exploration to production. In demos, it highlights features like connecting to Hadoop clusters securely and enabling collaboration. Overall, the document pitches Cloudera's Workbench as a solution
[Infographic] Cloud Integration Drivers and Requirements in 2015SnapLogic
SnapLogic and TechValidate queried more than 100 U.S. companies with revenues greater than $500 million about the business and technical drivers and barriers for enterprise cloud application adoption in 2015 and beyond.
You can also learn how the SnapLogic Elastic Integration Platform can help by going to www.SnapLogic.com/iPaaS.
Slides from Lenses session at Redis Conf 19
The Rise of DataOps on Streaming data, Lenses as a DataOps platform with SQL on Redis and Kafka.
Gain visibility and unlock your data scientists.
Webinar: Attaining Excellence in Big Data IntegrationSnapLogic
This document discusses best practices for attaining excellence in big data integration. It notes that analytics and integration are top investment areas for big data technologies. There is still uncertainty around which Hadoop tools and distributions to use. The document recommends five best practices: 1) evaluate integration processes, 2) examine new approaches, 3) evaluate technology needs, 4) investigate dedicated integration technology, and 5) gain benefits that outweigh costs. It also discusses using the cloud for big data integration.
How to create intelligent Business Processes thanks to Big Data (BPM, Apache ...Kai Wähner
BPM is established, tools are stable, many companies use it successfully. However, today's business processes are based on data from relational databases or web services. Humans make decisions due to this information. Companies also use business intelligence and other tools to analyze their data. Though, business processes are executed without access to this important information because technical challenges occur when trying to integrate big masses of data from many different sources into the BPM engine. Additionally, bad data quality due to duplication, incompleteness and inconsistency prevents humans from making good decisions. That is status quo. Companies miss a huge opportunity here!
This session explains how to achieve intelligent business processes, which use big data to improve performance and outcomes. A live demo shows how big data can be integrated into business processes easily - just with open source tooling. In the end, the audience will understand why BPM needs big data to achieve intelligent business processes.
Webinar: It's the 21st Century - Why Isn't Your Data Integration Loosely Coup...SnapLogic
In this webinar, learn from digital transformation and SOA thought leader Jason Bloomberg about traditional enterprise application integration (EAI), the rise of SOA and Web Services, and the latest REST and JSON initiatives.
This presentation also features a discussion of the age-old problem of implementing loosely coupled data integration, an architectural approach to solving this difficult problem and a demonstration of SnapLogic.
To learn more, visit: www.snaplogic.com/connect-faster
We will look at how a major industrial organisation is transforming their entire value chain, how they got started, what they've achieved and lessons learnt along the way.
Speaker: Julian Fischer, CEO, Anynines
The document outlines 8 critical steps for getting started with industrial data collection: 1) Assess equipment and IT systems, 2) Map pain points and objectives, 3) Set a quick-win proof of concept, 4) Form a small dedicated IIoT team, 5) Resist problem-specific solutions, 6) Decide on cloud or on-premise storage, 7) Involve machine suppliers early, and 8) Choose an IIoT integrator wisely. Each step provides questions to consider. The key takeaways are to think big but start small, involve colleagues and partners, and keep the system open rather than locked into one technology.
Pivotal Digital Transformation Forum: Data Science VMware Tanzu
This document discusses how data science can bridge the gap between data generation and comprehension. It provides examples of smart apps that combine and link data from different sources and domains to infer patterns, identify root causes, and potentially improve outcomes in real-time. The document advocates adding smart capabilities to apps by leveraging data science and emphasizes collaborating across teams like product management and engineering rather than having isolated data science efforts.
This presentation provides an objective approach to make your legacy and custom-built applications agile and infused with intelligence. This allows your apps to utilize new and more substantial data sets as well as apply artificial intelligence and machine learning to take in-the-moment actions.
The SnapLogic Integration Cloud for ServiceNowSnapLogic
Learn more about using the SnapLogic Integration Cloud to unlock ServiceNow potential by integrating it with major ITSM Cloud and on-premise applications including BMC Remedy, CA Clarity, SAP SolutionManager, and Workday. SnapLogic’s ServiceNow integration will greatly improve efficiency and quality of IT service management.
To learn more, visit: http://www.snaplogic.com/solutions/servicenow-integration.
IBM is committed to big data and analytics. It has made large acquisitions and investments in this area, with over 1000 developers focused on big data technology. IBM views open source technologies like Hadoop, Spark, and the Open Data Platform initiative as the base for its software and solutions. It is also investing in making big data more accessible through familiar tools, technical standards, new analytics capabilities, and open source innovation.
seven steps to dataops @ dataops.rocks conference Oct 2019DataKitchen
The document outlines seven steps for implementing DataOps to improve data analytics projects: 1) orchestrate the data journey from access to production, 2) add automated tests and monitoring, 3) use version control for code, 4) enable branching and merging of code, 5) use multiple environments, 6) reuse and containerize components, and 7) parameterize processing. It also discusses three additional steps: data architecture, inter- and intra-team collaboration, and process analytics for measurement. The goal of DataOps is to increase project success rates by integrating testing, monitoring, collaboration and automation practices across the entire data and analytics workflow.
You are not Facebook or Google? Why you should still care about Big Data and ...Kai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data.
This session goes beyond the well-known examples of huge companies such as Facebook or Google with millions of users. Instead, this session explains the "big" paradigm and technology shift for your company. See several use cases how big data enables small / medium-sized companies to gain insight into new business opportunities (and threats) and how big data stands to transform much of what the modern enterprise is today.
Learn about solving the unique challenges of big data without an own research lab or several big data experts in your company. Learn how to implement the relevant use cases for your company with low costs and efforts by using open source frameworks, which simplify working with big data a lot.
** Watch the video to accompany these slides: https://www.cloverdx.com/webinars/starting-your-modern-dataops-journey **
- What is "Data Ops" and why should you consider it?
- How to begin your transition to a DevOps and DataOps-style of work
- How agile methodologies, version control, continuous integration or 'infrastructure as code' can improve the effectivity of your teams
- How you can use technology like CloverDX to start with DataOps
Discover how to make your development and data analytics processes more efficient and effective by shifting to a Dev/DataOps approach.
More CloverDX webinars: https://www.cloverdx.com/webinars
Twitter: https://twitter.com/cloverdx
LinkedIn: https://www.linkedin.com/company/cloverdx/
Get a free 45 day trial of the CloverDX Data Management Platform: https://www.cloverdx.com/trial-platform
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
How Data Science is Preventing College Dropouts and Advancing Student SuccessVMware Tanzu
Educational institutions have a wealth of information, which can be brought together in an institutional data lake to predict and influence student behavior. In this webinar, one of Pivotal's principal data scientists discusses a recent collaborative project with a top university, in which many data sources were used to build a 360-degree profile of student activity on campus and help predict student success. Learn about the data science pipelines that Pivotal developed and how they are now being used to predict student metrics (such as GPA, course grade and time to graduate), and even as intervention tools to help prevent students from dropping out.
Webinar recording: https://youtu.be/SxXZBmAs1aE
Data technology experts from Pivotal give the latest perspective on how big data analytics and applications are transforming organizations across industries.
This event provides an opportunity to learn about new developments in the rapidly-changing world of big data and understand best practices in creating Internet of Things (IoT) applications.
Learn more about the Pivotal Big Data Roadshow: http://pivotal.io/big-data/data-roadshow
Webinar: The Death of Traditional Data IntegrationSnapLogic
In this webinar, we hear from industry analyst, middleware expert and author David Linthicum on why “existing approaches to data integration won’t meet future needs as the use of technology continues to change.” David also says that, “drastic measures must be taken now to prepare enterprises for the arrival of this technology, and to position enterprises to take full advantage.”
This webinar will show you how the game is changing, and what you can do about it right now. We summarize the changes that are happening, and review new and emerging patterns of data integration, as well as data integration technology that you can buy today that lives up to these new expectations.
To learn more, visit: www.snaplogic.com/big-data
Next Steps In Your Digital TransformationVMware Tanzu
This session brings together all the lessons learnt throughout the day and shares with you practical advice on how to get started with, or accelerate, your journey to become a digital business.
Speaker: Fadi Yousuf, Sales Manager - Gulf & KSA, Pivotal
Pivotal Data Warehouse in the Age of Digital TransformationVMware Tanzu
View the recording: https://content.pivotal.io/webinars/the-data-warehouse-in-the-age-of-digital-transformation?utm_source-pivotalwebsite&utm-medium-email-link&utm-campaign=datawarehouse-hiredbrains-q117
In the past years of Big Data and digital transformation “euphoria”, Hadoop and Spark received most of the attention as platforms for large-scale data management and analytics. Data warehouses based on relational database technology, for a variety of reasons, came under scrutiny as perhaps no longer needed.
However, if there is anything users have learned recently it’s that the mission of data warehouses is as vital as ever. Cost and operational deficiencies can be overcome with a combination of cloud computing and open source software, and by leveraging the same economics of traditional big data projects - scale-up and scale-out at commodity pricing.
In this webinar, Neil Raden from Hired Brains Research makes the case that an evolved data warehouse implementation continues to play a vital role in the enterprise, providing unique business value that actually aids digital transformation. Attendees will learn:
- How the role of the data warehouse has evolved over time
- Why Hadoop and Spark are not replacements for the data warehouse
- How the data warehouse supports digital transformation initiatives
- Real-life examples of data warehousing in digital transformation scenarios
- Advice and best practices for evolving your own data warehouse practice
This document discusses enterprise data science and machine learning. It begins by noting that data is now more plentiful and machine learning opportunities are everywhere. However, challenges remain around scaling data science work, making models production-ready, and meeting different team needs. The document then introduces Cloudera's Data Science Workbench for addressing these challenges. It claims the Workbench provides a secure, self-service environment allowing data scientists direct access to enterprise data and tools while meeting IT requirements. Examples are given of how it supports the full data science pipeline from exploration to production. In demos, it highlights features like connecting to Hadoop clusters securely and enabling collaboration. Overall, the document pitches Cloudera's Workbench as a solution
[Infographic] Cloud Integration Drivers and Requirements in 2015SnapLogic
SnapLogic and TechValidate queried more than 100 U.S. companies with revenues greater than $500 million about the business and technical drivers and barriers for enterprise cloud application adoption in 2015 and beyond.
You can also learn how the SnapLogic Elastic Integration Platform can help by going to www.SnapLogic.com/iPaaS.
Slides from Lenses session at Redis Conf 19
The Rise of DataOps on Streaming data, Lenses as a DataOps platform with SQL on Redis and Kafka.
Gain visibility and unlock your data scientists.
Webinar: Attaining Excellence in Big Data IntegrationSnapLogic
This document discusses best practices for attaining excellence in big data integration. It notes that analytics and integration are top investment areas for big data technologies. There is still uncertainty around which Hadoop tools and distributions to use. The document recommends five best practices: 1) evaluate integration processes, 2) examine new approaches, 3) evaluate technology needs, 4) investigate dedicated integration technology, and 5) gain benefits that outweigh costs. It also discusses using the cloud for big data integration.
How to create intelligent Business Processes thanks to Big Data (BPM, Apache ...Kai Wähner
BPM is established, tools are stable, many companies use it successfully. However, today's business processes are based on data from relational databases or web services. Humans make decisions due to this information. Companies also use business intelligence and other tools to analyze their data. Though, business processes are executed without access to this important information because technical challenges occur when trying to integrate big masses of data from many different sources into the BPM engine. Additionally, bad data quality due to duplication, incompleteness and inconsistency prevents humans from making good decisions. That is status quo. Companies miss a huge opportunity here!
This session explains how to achieve intelligent business processes, which use big data to improve performance and outcomes. A live demo shows how big data can be integrated into business processes easily - just with open source tooling. In the end, the audience will understand why BPM needs big data to achieve intelligent business processes.
Webinar: It's the 21st Century - Why Isn't Your Data Integration Loosely Coup...SnapLogic
In this webinar, learn from digital transformation and SOA thought leader Jason Bloomberg about traditional enterprise application integration (EAI), the rise of SOA and Web Services, and the latest REST and JSON initiatives.
This presentation also features a discussion of the age-old problem of implementing loosely coupled data integration, an architectural approach to solving this difficult problem and a demonstration of SnapLogic.
To learn more, visit: www.snaplogic.com/connect-faster
We will look at how a major industrial organisation is transforming their entire value chain, how they got started, what they've achieved and lessons learnt along the way.
Speaker: Julian Fischer, CEO, Anynines
The document outlines 8 critical steps for getting started with industrial data collection: 1) Assess equipment and IT systems, 2) Map pain points and objectives, 3) Set a quick-win proof of concept, 4) Form a small dedicated IIoT team, 5) Resist problem-specific solutions, 6) Decide on cloud or on-premise storage, 7) Involve machine suppliers early, and 8) Choose an IIoT integrator wisely. Each step provides questions to consider. The key takeaways are to think big but start small, involve colleagues and partners, and keep the system open rather than locked into one technology.
Pivotal Digital Transformation Forum: Data Science VMware Tanzu
This document discusses how data science can bridge the gap between data generation and comprehension. It provides examples of smart apps that combine and link data from different sources and domains to infer patterns, identify root causes, and potentially improve outcomes in real-time. The document advocates adding smart capabilities to apps by leveraging data science and emphasizes collaborating across teams like product management and engineering rather than having isolated data science efforts.
This presentation provides an objective approach to make your legacy and custom-built applications agile and infused with intelligence. This allows your apps to utilize new and more substantial data sets as well as apply artificial intelligence and machine learning to take in-the-moment actions.
The SnapLogic Integration Cloud for ServiceNowSnapLogic
Learn more about using the SnapLogic Integration Cloud to unlock ServiceNow potential by integrating it with major ITSM Cloud and on-premise applications including BMC Remedy, CA Clarity, SAP SolutionManager, and Workday. SnapLogic’s ServiceNow integration will greatly improve efficiency and quality of IT service management.
To learn more, visit: http://www.snaplogic.com/solutions/servicenow-integration.
IBM is committed to big data and analytics. It has made large acquisitions and investments in this area, with over 1000 developers focused on big data technology. IBM views open source technologies like Hadoop, Spark, and the Open Data Platform initiative as the base for its software and solutions. It is also investing in making big data more accessible through familiar tools, technical standards, new analytics capabilities, and open source innovation.
seven steps to dataops @ dataops.rocks conference Oct 2019DataKitchen
The document outlines seven steps for implementing DataOps to improve data analytics projects: 1) orchestrate the data journey from access to production, 2) add automated tests and monitoring, 3) use version control for code, 4) enable branching and merging of code, 5) use multiple environments, 6) reuse and containerize components, and 7) parameterize processing. It also discusses three additional steps: data architecture, inter- and intra-team collaboration, and process analytics for measurement. The goal of DataOps is to increase project success rates by integrating testing, monitoring, collaboration and automation practices across the entire data and analytics workflow.
You are not Facebook or Google? Why you should still care about Big Data and ...Kai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data.
This session goes beyond the well-known examples of huge companies such as Facebook or Google with millions of users. Instead, this session explains the "big" paradigm and technology shift for your company. See several use cases how big data enables small / medium-sized companies to gain insight into new business opportunities (and threats) and how big data stands to transform much of what the modern enterprise is today.
Learn about solving the unique challenges of big data without an own research lab or several big data experts in your company. Learn how to implement the relevant use cases for your company with low costs and efforts by using open source frameworks, which simplify working with big data a lot.
** Watch the video to accompany these slides: https://www.cloverdx.com/webinars/starting-your-modern-dataops-journey **
- What is "Data Ops" and why should you consider it?
- How to begin your transition to a DevOps and DataOps-style of work
- How agile methodologies, version control, continuous integration or 'infrastructure as code' can improve the effectivity of your teams
- How you can use technology like CloverDX to start with DataOps
Discover how to make your development and data analytics processes more efficient and effective by shifting to a Dev/DataOps approach.
More CloverDX webinars: https://www.cloverdx.com/webinars
Twitter: https://twitter.com/cloverdx
LinkedIn: https://www.linkedin.com/company/cloverdx/
Get a free 45 day trial of the CloverDX Data Management Platform: https://www.cloverdx.com/trial-platform
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Columbia Business School's Center on Global Brand Leadership, in conjunction with the Aimia Institute, surveyed over 8000 global consumers to uncover how they perceive
and act on sharing their data with companies.
More information is available from:
http://gsb.columbia.edu/globalbrands
or
http://aimia.com
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Data Science - Part X - Time Series ForecastingDerek Kane
This lecture provides an overview of Time Series forecasting techniques and the process of creating effective forecasts. We will go through some of the popular statistical methods including time series decomposition, exponential smoothing, Holt-Winters, ARIMA, and GLM Models. These topics will be discussed in detail and we will go through the calibration and diagnostics effective time series models on a number of diverse datasets.
Data Science - Part XIII - Hidden Markov ModelsDerek Kane
This lecture provides an overview on Markov processes and Hidden Markov Models. We will start off by going through a basic conceptual example and then explore the types of problems that can be solved with HMM's. The underlying algorithms will be discussed in detail with a quantitative focus and then we will conclude with a practical example concerning stock market prediction which highlights the techniques.
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
HP Security Voltage provides data-centric security solutions to protect sensitive data in Hadoop environments. Their solutions leverage tokenization and encryption to safeguard data at rest, in motion, and in use across the data lifecycle. They presented use cases where their technology helped secure financial, healthcare, and telecommunications customer data in Hadoop and other platforms. Questions from analysts focused on implementation experience, performance impacts, integration with authentication, costs, and supported environments and partnerships.
MATATABI: Cyber Threat Analysis and Defense Platform using Huge Amount of Dat...APNIC
MATATABI: Cyber Threat Analysis and Defense Platform using Huge Amount of Datasets, by Yuji Sekiya.
Presented at the APNIC 40 APOPS 1 session, Tue 8 Sep 2015.
Balancing Mobile UX & Security: An API Management Perspective Presentation fr...CA API Management
This document discusses reconciling user experience and security in mobile applications. It explores techniques for user authentication on mobile that can disrupt user experience if not implemented properly. It proposes balancing authentication complexity and frequency to improve user experience without compromising security. The document also examines using biometrics, risk-based authentication, and single sign-on across mobile apps and third-party apps to improve both security and user experience on mobile. It describes components of a solution including API routing, brokering, and protected endpoints to enable secure access to APIs from mobile applications.
Pivotal Digital Transformation Forum: Journey to Become a Data-Driven EnterpriseVMware Tanzu
The document discusses Pivotal's Big Data Suite for helping enterprises become data-driven. It outlines challenges in analyzing large amounts of data and the value that can be gained. The suite includes tools for ingesting, processing, storing and analyzing streaming and batch data at scale. It also provides examples of how the suite can be used for applications like financial compliance monitoring and connected cars.
Data Security and Privacy by Contract: Hacking Us All Into Business Associate...Shawn Tuma
This presentation was delivered at the Southern Methodist University Law School, Science and Technology Law Review's 2015 Cybersecurity Symposium on October 23, 2015.
The document discusses software piracy trends from 2012-2015. It found that the number of pirated assets increased from 1.6 million per year between 2012-2014 to an expected 1.96 million in 2015. The most commonly pirated items were Android apps, key generators, Apple software, Windows desktop software, and Apple apps. The document recommends organizations implement run-time protections in applications, protect cryptographic keys, and ensure security investments are in line with application risk levels to help mitigate software piracy.
This document discusses security challenges for internet of things (IoT) devices and potential solutions. It describes how IoT devices have been hacked, including a baby monitor, printers catching fire, and hijacked consumer devices forming botnets. Network security protocols like TLS, DTLS and eDTLS are discussed as well as challenges of provisioning security for large numbers of constrained devices. The document advocates for defense-in-depth approaches using multiple complementary security mechanisms. It also examines security issues for industrial control systems, military equipment, and connected cars, noting many record large amounts of user data without adequate user control over data access. The document promotes market designs, legislation, and secure designs to help protect users from internet of threats.
Watch full webinar here: https://bit.ly/3mdj9i7
You will often hear that "data is the new gold"? In this context, data management is one of the areas that has received more attention from the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar, we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Watch this on-demand webinar as we cover:
- The most interesting trends in data management
- How to build a data fabric architecture?
- How to manage your data integration strategy in the new hybrid world
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of voice computing in future data analytic
IBM Cloud Pak for Data is a unified platform that simplifies data collection, organization, and analysis through an integrated cloud-native architecture. It allows enterprises to turn data into insights by unifying various data sources and providing a catalog of microservices for additional functionality. The platform addresses challenges organizations face in leveraging data due to legacy systems, regulatory constraints, and time spent preparing data. It provides a single interface for data teams to collaborate and access over 45 integrated services to more efficiently gain insights from data.
Best Practices For Building and Operating A Managed Data Lake - StampedeCon 2016StampedeCon
The document discusses using a data lake approach with EMC Isilon storage to address various business use cases. It describes how the solution provides shared storage for multiple workloads through multi-protocol support, enables data protection and isolation of client data, and allows testing applications across Hadoop distributions through a common platform. Examples are given of how this approach supports an enterprise data hub, data warehouse offloading, data integration, and enrichment services.
This document provides an agenda and overview for a presentation on leveraging big data to create value. The agenda includes sessions on Hadoop in the real world, Cisco servers for big data, and breakout brainstorming sessions. The presentation discusses how big data can be a competitive strategy, its financial benefits, and goals for applying it in ways that improve important business metrics. An overview of key big data technologies is presented, including Hadoop, NoSQL databases, and in-memory databases. The big data software stack and how big data expands the traditional data stack is also summarized.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Building Confidence in Big Data - IBM Smarter Business 2013 IBM Sverige
Success with big data comes down to confidence. Without confidence in the underlying data, decision makers may not trust and act on analytic insight. You need confidence in your data – that it’s correct, trusted, and protected through automated integration, visual context, and agile governance. You need confidence in your ability to accelerate time to value, with fast deployments of big data appliances. Learn how clients have succeeded with big data by building confidence in their data, ability to deploy, and skills. Presenter: David Corrigan, Big Data specialist, IBM. Mer från dagen på http://bit.ly/sb13se
The document discusses Microsoft's approach to implementing a data mesh architecture using their Azure Data Fabric. It describes how the Fabric can provide a unified foundation for data governance, security, and compliance while also enabling business units to independently manage their own domain-specific data products and analytics using automated data services. The Fabric aims to overcome issues with centralized data architectures by empowering lines of business and reducing dependencies on central teams. It also discusses how domains, workspaces, and "shortcuts" can help virtualize and share data across business units and data platforms while maintaining appropriate access controls and governance.
Accelerate Big Data Application Development with Cascading and HDP, Hortonwor...Hortonworks
Accelerate Big Data Application Development with Cascading and HDP, webinar hosted by Hortonworks and Concurrent. Visit Hortonworks.com/webinars to access the recording.
This document provides an introduction to big data. It defines big data as large and complex data sets that are difficult to process using traditional data management tools. It discusses the three V's of big data - volume, variety and velocity. Volume refers to the large scale of data. Variety means different data types. Velocity means the speed at which data is generated and processed. The document outlines topics that will be covered, including Hadoop, MapReduce, data mining techniques and graph databases. It provides examples of big data sources and challenges in capturing, analyzing and visualizing large and diverse data sets.
Big Data Fabric: A Necessity For Any Successful Big Data InitiativeDenodo
Watch this webinar in full here: https://buff.ly/2IxM8Iy
Watch all webinars from the Denodo Packed Lunch webinar series here: https://buff.ly/2IR3q6w
While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users.
Attend this session to learn how big data fabric enabled by data virtualization:
• Provides lightning fast self-service data access to business users
• Centralizes data security, governance and data privacy
• Fulfills the promise of data lakes to provide actionable insights
As users gain more experience with Hadoop, they are building on their early success and expanding the size and scope of Hadoop projects. Syncsort’s third annual Hadoop Market Adoption Survey reflects the fact that Hadoop is no longer considered a technology for the future as it was when we first started conducting this research.
Get an in-depth look at the survey results and five trends to watch for in 2017. You’ll also learn:
• The best uses for Hadoop in 2017 – real-word examples of how Enterprises are realizing the value of Big Data
• Solutions to help you address the challenges enterprises still face in employing Hadoop
• What the future of Hadoop means for your business
The key to the cognitive business is putting data to work. What is needed is a platform, an ecosystem, and a method.
Learn more about http://ibm.co/dataworks
This document discusses how organizations can use big data and operational analytics to transform IT operations. It outlines how taking a data-driven approach that combines machine data and wire data can provide real-time visibility across networks, applications, databases and other systems. This approach overcomes limitations of using individual monitoring tools by silo. The document also covers key considerations for implementing IT big data solutions such as data gravity, improving the signal-to-noise ratio, and understanding when data needs to be accessed in real-time. It provides an example of how healthcare company McKesson used network traffic analysis to improve Citrix application performance and reduce IT costs.
Geospatial Intelligence Middle East 2013_Big Data_Steven RamageSteven Ramage
Some initial considerations and discussion points around geospatial big data. Location adds context and relevance. Need to consider a number of V factors including Value.
This document discusses cognitive computing and analytics technologies. It provides examples of how cognitive systems can be applied, such as a toy that learns from child interactions. The document outlines a cognitive strategy and foundation that includes collecting and analyzing both structured and unstructured data. It also discusses the importance of cloud services, infrastructure, and security for cognitive systems. Finally, the document describes some of the cognitive computing APIs available from IBM Watson and how the set of APIs has expanded over time.
How Experian increased insights with HadoopPrecisely
This document provides an overview of MapR Technologies and their products. It discusses how MapR helps companies harness big data by providing an enterprise-grade distribution of Apache Hadoop that includes data protection, security, and high performance capabilities. It also highlights MapR partnerships with companies like Syncsort to provide data integration, migration, and analytics solutions that help customers derive more value from their data.
Crossing the bridge - how do we link end-user-computing and formal tech for d...J On The Beach
With Excel or custom tooling (Python, R, etc) there's flexibility to build data processing and preparation pipelines. Getting these to production level is often a different story as traditional or formal IT organisations are not well equipped to handle this kind of development.
In this talk, I'll show how we have combined SQL and NoSQL storage engines to create flexible and production ready data pipelines that can deal with unstructured data flows in an efficient manner.
Multi-Cloud Breaks IT Ops: Best Practices to De-Risk Your Cloud StrategyThousandEyes
Organizations are using multiple IaaS and SaaS providers today, yet traditional ITOps processes and tools are straining to cope with a vast new scope of challenges and risks. Recent research by Enterprise Management Associates (EMA) shows that 74% of enterprise network teams had incumbent network monitoring tools failing to address cloud requirements. As IT business leaders responsible for delivering services in this new ecosystem, how do you equip yourself with the right visibility?
Shamus McGillicuddy, Research Director for EMA’s network management practice, and Archana Kesavan, Director of Product Marketing at ThousandEyes dive deep into the challenges of multi-cloud and how to rethink your monitoring strategy and operational delivery processes.
Uncover:
Five common IT operational challenges of multi-cloud identified in recent EMA research
The risks of not evolving ITOps for a managed cloud environment
Four monitoring best practices for a cloud-centric IT Operation
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...Hortonworks
The document discusses a Big Data Meetup organized by C-BAG (Chennai Big Data Analytic Group) on October 29, 2014 in Chennai. It provides details about two speakers, Dhruv Kumar from Concurrent Inc. and Vinay Shukla from Hortonworks, who will discuss reducing development time for production-grade Hadoop applications and Hortonworks' Hadoop platform respectively. The remainder of the document consists of presentation slides that cover topics including the modern data architecture with Hadoop, enterprise goals for data architecture, unlocking applications from new data types, and case studies.
Similar to Pivotal Digital Transformation Forum: Becoming a Data Driven Enterprise (20)
What AI Means For Your Product Strategy And What To Do About ItVMware Tanzu
The document summarizes Matthew Quinn's presentation on "What AI Means For Your Product Strategy And What To Do About It" at Denver Startup Week 2023. The presentation discusses how generative AI could impact product strategies by potentially solving problems companies have ignored or allowing competitors to create new solutions. Quinn advises product teams to evaluate their strategies and roadmaps, ensure they understand user needs, and consider how AI may change the problems being addressed. He provides examples of how AI could influence product development for apps in home organization and solar sales. Quinn concludes by urging attendees not to ignore AI's potential impacts and to have hard conversations about emerging threats and opportunities.
Make the Right Thing the Obvious Thing at Cardinal Health 2023VMware Tanzu
This document discusses the evolution of internal developer platforms and defines what they are. It provides a timeline of how technologies like infrastructure as a service, public clouds, containers and Kubernetes have shaped developer platforms. The key aspects of an internal developer platform are described as providing application-centric abstractions, service level agreements, automated processes from code to production, consolidated monitoring and feedback. The document advocates that internal platforms should make the right choices obvious and easy for developers. It also introduces Backstage as an open source solution for building internal developer portals.
Enhancing DevEx and Simplifying Operations at ScaleVMware Tanzu
Cardinal Health introduced Tanzu Application Service in 2016 and set up foundations for cloud native applications in AWS and later migrated to GCP in 2018. TAS has provided Cardinal Health with benefits like faster development of applications, zero downtime for critical applications, hosting over 5,000 application instances, quicker patching for security vulnerabilities, and savings through reduced lead times and staffing needs.
Dan Vega discussed upcoming changes and improvements in Spring including Spring Boot 3, which will have support for JDK 17, Jakarta EE 9/10, ahead-of-time compilation, improved observability with Micrometer, and Project Loom's virtual threads. Spring Boot 3.1 additions were also highlighted such as Docker Compose integration and Spring Authorization Server 1.0. Spring Boot 3.2 will focus on embracing virtual threads from Project Loom to improve scalability of web applications.
Platforms, Platform Engineering, & Platform as a ProductVMware Tanzu
This document discusses building platforms as products and reducing developer toil. It notes that platform engineering now encompasses PaaS and developer tools. A quote from Mercedes-Benz emphasizes building platforms for developers, not for the company itself. The document contrasts reactive, ticket-driven approaches with automated, self-service platforms and products. It discusses moving from considering platforms as a cost center to experts that drive business results. Finally, it provides questions to identify sources of developer toil, such as issues with workstation setup, running software locally, integration testing, committing changes, and release processes.
This document provides an overview of building cloud-ready applications in .NET. It defines what makes an application cloud-ready, discusses common issues with legacy applications, and recommends design patterns and practices to address these issues, including loose coupling, high cohesion, messaging, service discovery, API gateways, and resiliency policies. It includes code examples and links to additional resources.
Dan Vega discussed new features and capabilities in Spring Boot 3 and beyond, including support for JDK 17, Jakarta EE 9, ahead-of-time compilation, observability with Micrometer, Docker Compose integration, and initial support for Project Loom's virtual threads in Spring Boot 3.2 to improve scalability. He provided an overview of each new feature and explained how they can help Spring applications.
Spring Cloud Gateway - SpringOne Tour 2023 Charles Schwab.pdfVMware Tanzu
Spring Cloud Gateway is a gateway that provides routing, security, monitoring, and resiliency capabilities for microservices. It acts as an API gateway and sits in front of microservices, routing requests to the appropriate microservice. The gateway uses predicates and filters to route requests and modify requests and responses. It is lightweight and built on reactive principles to enable it to scale to thousands of routes.
This document appears to be from a VMware Tanzu Developer Connect presentation. It discusses Tanzu Application Platform (TAP), which provides a developer experience on Kubernetes across multiple clouds. TAP aims to unlock developer productivity, build rapid paths to production, and coordinate the work of development, security and operations teams. It offers features like pre-configured templates, integrated developer tools, centralized visibility and workload status, role-based access control, automated pipelines and built-in security. The presentation provides examples of how these capabilities improve experiences for developers, operations teams and security teams.
The document provides information about a Tanzu Developer Connect Workshop on Tanzu Application Platform. The agenda includes welcome and introductions on Tanzu Application Platform, followed by interactive hands-on workshops on the developer experience and operator experience. It will conclude with a quiz, prizes and giveaways. The document discusses challenges with developing on Kubernetes and how Tanzu Application Platform aims to improve the developer experience with features like pre-configured templates, developer tools integration, rapid iteration and centralized management.
The Tanzu Developer Connect is a hands-on workshop that dives deep into TAP. Attendees receive a hands on experience. This is a great program to leverage accounts with current TAP opportunities.
The Tanzu Developer Connect is a hands-on workshop that dives deep into TAP. Attendees receive a hands on experience. This is a great program to leverage accounts with current TAP opportunities.
Simplify and Scale Enterprise Apps in the Cloud | Dallas 2023VMware Tanzu
This document discusses simplifying and scaling enterprise Spring applications in the cloud. It provides an overview of Azure Spring Apps, which is a fully managed platform for running Spring applications on Azure. Azure Spring Apps handles infrastructure management and application lifecycle management, allowing developers to focus on code. It is jointly built, operated, and supported by Microsoft and VMware. The document demonstrates how to create an Azure Spring Apps service, create an application, and deploy code to the application using three simple commands. It also discusses features of Azure Spring Apps Enterprise, which includes additional capabilities from VMware Tanzu components.
SpringOne Tour: Deliver 15-Factor Applications on Kubernetes with Spring BootVMware Tanzu
The document discusses 15 factors for building cloud native applications with Kubernetes based on the 12 factor app methodology. It covers factors such as treating code as immutable, externalizing configuration, building stateless and disposable processes, implementing authentication and authorization securely, and monitoring applications like space probes. The presentation aims to provide an overview of the 15 factors and demonstrate how to build cloud native applications using Kubernetes based on these principles.
SpringOne Tour: The Influential Software EngineerVMware Tanzu
The document discusses the importance of culture in software projects and how to influence culture. It notes that software projects involve people and personalities, not just technology. It emphasizes that culture informs everything a company does and is very difficult to change. It provides advice on being aware of your company's culture, finding ways to inculcate good cultural values like writing high-quality code, and approaches for influencing decision makers to prioritize culture.
SpringOne Tour: Domain-Driven Design: Theory vs PracticeVMware Tanzu
This document discusses domain-driven design, clean architecture, bounded contexts, and various modeling concepts. It provides examples of an e-scooter reservation system to illustrate domain modeling techniques. Key topics covered include identifying aggregates, bounded contexts, ensuring single sources of truth, avoiding anemic domain models, and focusing on observable domain behaviors rather than implementation details.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
9. Journey to Become a Data Driven Enterprise
STORE
• Structured
• Unstructured
• High Volume
• High Velocity
ANALYZE
• Predictive Analytics
• Machine Learning
• Advance Data Science
• Realtime Analytics
DEVELOP
• Advanced Analytic Pipelines
• Realtime Analytical Applications
• Global Scale Data-Driven
Applications
• Enterprise, Consumer, IoT, and
Mobile
INNOVATE
• Agile Dev Expertise
• DevOps
• Hybrid Cloud
• Continuous Delivery
• Closed Loop Applications
AGILE DEVELOPMENT
BIG DATA
PREDICTIVE ANALYTICS
ENTERPRISE PAAS
10. Journey to Become a Data Driven Enterprise
STORE
• Structured
• Unstructured
• High Volume
• High Velocity
ANALYZE
• Predictive Analytics
• Machine Learning
• Advance Data Science
• Realtime Analytics
DEVELOP
• Advanced Analytic Pipelines
• Realtime Analytical Applications
• Global Scale Data-Driven
Applications
• Enterprise, Consumer, IoT, and
Mobile
INNOVATE
• Agile Dev Expertise
• DevOps
• Hybrid Cloud
• Continuous Delivery
• Closed Loop Applications
AGILE DEVELOPMENT
BIG DATA
PREDICTIVE ANALYTICS
ENTERPRISE PAAS
Spring XD
Spark
Pivotal HD &
Open Data Platform
Spring XD
Pivotal Greenplum
Database
Pivotal HAWQ
Spring XD
Pivotal GemFire
Redis
Rabbit MQ
Spring IO
Groovy
Pivotal BDS on PCF
Pivotal Cloud Foundry
Pivotal LabsData ScienceData Engineering
11. CATCHING PEOPLE IN THE ACT OF DOING…
Value of
Data ($)
Time
µs ms s hour day month year yr+
“Fast Data” “Big Data”Traditional
Systems
Pivotal Data
Science Labs
12. Pivotal Big Data Suite
Complete
platform
SQL on Hadoop
leadership
Deployment
options
Open source
Flexible
licensing
Advanced data
services
13. EMC FEDERATION BUSINESS DATA LAKE
DATA & ANALYTICS CATALOG
(THIRD PARTY APPLICATIONS)
HADOOP
OPEN DATA PLATFORM
PIVOTAL BIG DATA SUITE
ADVANCED ANALYTICS
DATA PROCESSING
APPS AT SCALE
GREENPLUM DATABASE HAWQ
PIVOTAL HDSPARKSPRING XD
DATA SERVICES MANAGEMENT
ANALYTICS TOOLBOX
REDIS
RABBITMQ
GEMFIRE
BDS ON
PIVOTAL
DATA
MANAGER
DATA
GOVERNOR
INGEST
INDEX &
SEARCH
POLICY
MGMT
SECURITY
& ACCESS
CONTROL
VIRTUALIZATION PIVOTAL CLOUD FOUNDRY
EMC II STORAGE
DATA LAKE FOUNDATION: ISILON | ECS
VCE VBLOCK | XTREMIO
14. “
THE MAGIC HAPPENS WHEN YOU MARRY THE
TRADITIONAL ENGINEERING APPROACH WITH THE
DATA SCIENCE ENABLED BY THE DATA LAKE. IT
OPENS UP A WHOLE NEW WORLD OF POSSIBLE
‘WHAT IF’ QUESTIONS.
”DAVE BARTLETT, GE AVIATION
20. THE BUSINESS DATA LAKE JOURNEY
START
DISPARATE
DATA SILOS
STEP 1:
CONSOLIDATE
DATA
STEP 2:
ADD
HADOOP
STEP 3:
IMPLEMENT
ANALYTICS
STEP 4:
INTEGRATE
APP DEV
STEP 5:
BUSINESS DATA
LAKE
EMC BIG DATA
STORAGE
PIVOTAL HD
(OR ALT. DISTRIBUTION)
PIVOTAL BDS
(OR ALT. TOOLS)
PIVOTAL CLOUD
FOUNDRY
FAST TRACK
TECHNOLOGY
APPS
ANALYTIC
S
EMC ENGINEERED
SOFTWARE
21. A UNIQUE FEDERATION OF COMPANIES
1. PIVOTAL LABS
2. PIVOTAL CLOUD FOUNDRY
3. BIG DATA AND ANALYTICS
1. SERVER VIRTUALISATION
2. SOFTWARE DEFINED DATA CENTER
3. ORCHESTRATION AND MANAGEMENT
4. CLOUD SERVICES
1. CLOUD SERVICES
2. XSTREAM CLOUD MANAGEMENT
3. ENTERPRISE CLASS IAAS
1. SOFTWARE DEFINED STORAGE
2. FLASH
3. CONVERGED INFRASTRUCTURE
22. Catch people or things in the
act of doing something
and affect the outcome